Towards Massive Connectivity Support for Scalable mMTC … · 2018-04-06 · 1 Towards Massive...

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Towards Massive Connectivity Support for ScalablemMTC Communications in 5G networks

Carsten Bockelmann, Nuno K. Pratas, Gerhard Wunder, Stephan Saur, Monica Navarro, David Gregoratti,Guillaume Vivier, Elisabeth de Carvalho, Yalei Ji, Cedomir Stefanovic, Petar Popovski, Qi Wang,

Malte Schellmann, Evangelos Kosmatos, Panagiotis Demestichas, Miruna Raceala-Motoc, Peter Jung,Slawomir Stanczak and Armin Dekorsy

Abstract—The fifth generation of cellular communication sys-tems is foreseen to enable a multitude of new applications anduse cases with very different requirements. A new 5G multi-service air interface needs to enhance broadband performanceas well as provide new levels of reliability, latency and supportednumber of users. In this paper we focus on the massive MachineType Communications (mMTC) service within a multi-serviceair interface. Specifically, we present an overview of differentphysical and medium access techniques to address the problemof a massive number of access attempts in mMTC and discuss theprotocol performance of these solutions in a common evaluationframework.

Index Terms—5G, mMTC, massive access, massive connectiv-ity, random access

I. INTRODUCTION

The prospect of billions of interconnected devices withinthe paradigm of the Internet of Things (IoT) has become oneof the main drivers of the research and development in the ICTsector. In fact, the 5G requirements for IMT-2020 include thesupport of a multiplicity of services and applications, withmassive Machine Type Communications (mMTC) being oneof the three cores services. The other core services being theUltra Reliable Low Latency (URLLC) and the extreme MobileBroadband (eMBB) communications.

The focus in this paper is on the massive access pro-tocols and multi-user decoding techniques associated withthe support of the mMTC core service. The objective is toattach a large number of low-rate low-power devices, termedMachine-Type Devices (MTDs), to the cellular network. Thereare multiple factors that demand increased number of con-nected MTDs: the smart-grid, large scale environment andstructure monitoring, asset and health monitoring, etc. Typ-ically, these MTDs connect asynchronously and sporadicallyto the network to transmit small data payloads. Connectedobjects include various types with an extremely wide setof requirements: for instance, a connected goggle providingaugmented reality would require lower latency and higherthroughput compared to a connected smoke detector. However,it is commonly understood that mMTC indicates the family ofdevices requiring sporadic access to the network to transmitsmall data payloads. The sporadic access leads to having anunknown, random subset of devices being active at a giventransmission instant or frame, which necessitates the use ofsome form of random access protocol.

Most of the existing MTC connections, not necessarily mas-sive, are wireless and take place via open standard short rangetechnologies that operate in unlicensed spectrum, such as IEEE802.15.x and 802.11. Another trend is seen in the proprietarytechnologies for wide-area IoT, such as SIGFOX [1] and LoRA[2], addressing the physical domain not covered by short-range technologies and thus providing a clear indication of anemerging market that is yet to be filled by service providers.Until recently, the cellular standards could only provide accessto MTDs via SMS or GPRS. This approach suffers fromcoverage limitations (e.g., in deep indoor for instance forgas or water meters), non-optimized hardware and limitedsubscription models. Moreover, the 2G/3G systems were notdesigned to handle thousands of sporadically active MTDs. Asa result, the 3GPP has extended the support of LTE to MTCwith the standardization of cat-M and NB-IoT in 2015-2016.Those standards meet most of the mMTC requirements, butstill need to be improved to support the massive number ofterminals with low capabilities, sporadic activity patterns, andshort packet transmissions.

One of the major obstacles for the proliferation of efficientcellular access for mMTC stems from the deficiencies of theaccess reservation procedure, a key building block of thecellular access networking. Currently, the access reservationprocedure is designed to enable connection establishment froma relatively low number of accessing devices. Additionally,each device has moderate to high data-rate requirements suchthat the overhead of current access protocols with multiplephases is relatively small. Both assumptions, the low numberof devices as well as moderate to high data rates are in con-tradiction to mMTC needs. Thus, enhancement of the accessreservation procedure for mMTC traffic has been in the focusof both the research community [3] and standardization [4].However, there is a common understanding that the mMTCtraffic requirements call for a more radical redesign of thecellular access [5].

Indeed, 3GPP has recently concentrated its standardizationefforts in this regard in four parallel tracks, which are (i)LTE for M2M (eMTC), focusing on the modification ofLTE radio access network (RAN) for mMTC services andtargeted at devices with reduced air-interface capabilities [6],(ii) narrow-band IoT (NB-IoT) which targets low-cost narrow-band devices with reduced functionalities [7], (iii) extendedcoverage GSM for IoT (EC-GSM-IoT) [8] and (iv) the supportof mMTC in 5G. In the efforts (i)-(iii), the goal can be

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summarized as [9]: improved indoor coverage (15-20dB whencompared to current cellular systems) and outdoor coverage upto 15 km, support of massive number of low data-rate deviceswith modest device complexity, improved power efficiency toensure longer battery life, reduced access latency and efficientco-existence with the legacy cellular systems. In (iv), thedevelopment towards 5G has started in 3GPP; and while thefirst phase, to be standardized in Release 15 [10], focuses onextreme MBB (eMBB) services, the URLLC and mMTC willbe in the focus of the following phases.

In this paper we summarize several approaches to addressthe massive access problem for mMTC in 5G and presentan evaluation framework to assess the performance of the pre-sented approaches in terms of the access protocol performance.The presented solutions are part of the main innovations andoutcomes of the FANTASTIC-5G project [11] 1. First, wewill outline the overall mMTC challenges and the specificresearch questions to be addressed in section II (also see theoverview paper [10]) and provide a short overview of the stateof the art MTC systems in section III . Then our system leveland evaluation approach will be outlined in section IV anddetailed technical approaches and their achieved performancefor pure MAC protocols will be discussed in section V andcombined PHY& MAC approaches in section VI. Finally, wewill present the results and compare different solutions interms of their requirements and advantages. The paper wrapswith conclusions in section VIII.

II. MMTC CHALLENGES

Many MTC applications are already served by today’scommunication systems. However, the characteristic propertiesof mMTC, i.e. the massive number of devices and the veryshort payload sizes, require novel approaches and concepts.5G offers the opportunity to tackle the critical challenges in aseamless cellular system combining mMTC and all the otherservices. This leads to the following mMTC challenges:• Control signaling challenge: In the existing LTE specifi-

cation an endless cascade of signalling exchange betweenMTD, eNodeB and core network is initiated if an MTD isin idle mode and intends to send one single small packet.The overall number of sent bits is dominated by controlinformation, and the actual data becomes negligible.Therefore, a 5G system must provide low overhead datatransmission modes through novel MAC and PHY design.Additionally, higher layer enhancements, such as radioresource control signaling, are urgently required to lowerthe overhead on the reconnecting and re-authentication ofidle users. Finally, methods that enable the transmissionof small data packets over the control plane should beconsidered.

• Access capacity challenge: In LTE the first step toaccessing the system or reconnecting when the devicein idle mode, is the access reservation protocol. Thethroughput of the LTE access reservation protocol is

1FANTASTIC-5G is the phase 1 project of Horizon 2020 in the frame-work of 5G PPP dealing with the air interface below 6GHz with time-linecompleting on the July 2017.

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Low Latency (U

RLC)

High Coverage (mMTC)

Freq

uenc

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Time

High Throughput (eMBB)

Fig. 1. Multi service integration.

severely degraded since there is no specific collisionresolution procedure in physical (PHY) and mediumaccess (MAC) layer. A 5G system must at least enhancethe access reservation protocol through novel MAC andPHY approaches to support a massive number of devices.

• Power consumption: The MTDs are often battery pow-ered and require 10+ years of autonomy. For that purpose,the access and communication schemes should be powerefficient. This challenge is also related to the type of con-nection: either always UL triggered (mobile originated)or DL traffic (network originated communications) areconsidered. For instance, in a Sigfox model, communica-tion is always triggered by an UL request, which helpsin terms of power consumption (no need to wake up forpaging channels).

• Multi-Service Integration: LTE is mostly focused onMBB services and uses a single frame definition andcommon control channels for these services. In order toenable coexistence of services with very different require-ments, 5G needs to include flexible frame definitions,a robust waveform and flexible control channel designto allow for dynamic bandwidth sharing and differentPHY/MAC approaches. An example is provided in Fig. 1on the multi-service integration over frequency, time andspace resources.

We focus on the MAC and PHY layer enhancements requiredto solve the outlined challenges. On the one hand, access pro-tocols with novel waveforms are considered to enable spectraland temporal asynchronicities with very low control overhead;on the other hand, several MAC and PHY approaches andtheir combination are presented to specifically address thefirst two challenges ”Control Signalling” and ”Throughput”.The focus of this paper is on summarizing potential solutionsand providing insight on the access protocol throughput andlatency of these solutions as will be discussed in section IV.Novel waveforms are only exploited as enabling technologyfor a novel access protocol here, an exhaustive treatment ofthe waveforms being considered in a 5G setting is providedin [12], [13].

A. System Level Considerations

When the scenarios under examination are extended in orderto examine topologies with many cells and in order to takeinto consideration higher layer functionalities then severalsystem level considerations emerge. In addition, system level

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scenarios may include cooperative functions between two ormore cells (e.g. by using the X2 interface) such as coordinatedpower control and mobility. In such environments, one cansummarize the main system level topics of mMTC as: a) inter-cell interference from devices connected at neighboring cells;b) power control considerations; c) frame structure consider-ations and; d) intra-cell interference caused by asynchronoustransmissions.

In scenarios in which several cells exists, interferenceemerges both among intra- and inter-cell devices. Regardingintra-cell interference, it emerge in cases of contentions, thusregarding the mMTC access protocols, interference emergesin the access notification stage of multi-stage and two-stageaccess protocols or during the combined access and data phaseof one-stage protocols. On the contrary, inter-cell interferencemay emerge in any phase of the system including access,connection establishment and data phases, regardless of theselection of the access protocol.

In mMTC scenarios with a single cell, power controlmechanisms are targeting to minimise the interference betweendevices (intra-cell interference) and in increasing the powerefficiency to ensure longer battery life. In a multiple cellscenario, power control mechanisms are also targeting tominimise the inter-cell interference in addition to the above. Inthis direction, several coordinated power control mechanismsexist which study the trade-off between the effectiveness(preciseness of power control) and the overall overhead.Among the innovation of FANTASTIC-5G is the propositionof flexible frame definitions appropriate for a multi-serviceenvironment (Fig. 1). In this direction, in contrast to eMBBservices which are supported by numerologies with typicalLTE TTIs (e.g 1ms) and URLLC service with strict latencyrequirements supported by small TTIs (e.g. 0.25ms), thespecial requirements of mMTC services can be satisfied bynumerologies with long TTIs and short subcarrier spacing inorder to increase coverage and decrease device complexity andpower consumption.

Regarding the loose uplink synchronization, one main lim-itation of the mMTC devices with sporadic uplink data is thatthey use the downlink channel for synchronization. This isnot a major problem in small to medium sized cell envi-ronments (e.g., with inter-site distance 500m) and in casesof channel realizations with low delay spread values (e.g.,EPA [14]), because in these cases the use of cyclic prefix(CP) compensates for any deviations of the transmission fromthe detection window reference time. But, in case of largecells (e.g. inter-site distance > 1500m) and for channels withhigh delay spreads (e.g. ETU [14]), the deviation can becomelarger, especially for the devices afar from the base station,and can surpass the selected CP value. In this case, the trans-mission is considered asynchronous to the detection windowand it produces interference to the transmissions adjacentin frequency. The power of this interference is affected byvarious parameters (e.g., the size of two bursts, the existenceof guard bands between them, etc.). In FANTASTIC-5G a setof new waveforms are proposed with properties which canlimit and in some cases eliminate the interference effects dueto asynchronicity [12], [13].

III. MMTC STATE OF THE ART

Several ongoing efforts aim to support mMTC in commer-cial communication systems, but most of these only supportparts of the mMTC requirements. Short payload packets andextended coverage are already available in some of the solu-tions. However, the problem of a massive number of devicesattempting access has not been solved. In the following, weprovide a short overview of mMTC systems currently availableor under development covering 3GPP systems as well as Non-3GPP systems.

A. Non-3GPP Low Power Wide Area Networks

LoRA is a Low Power Wide Area Network (LPWAN) and istypically laid out in a star-of-stars topology in which gatewaysrelay messages between end-devices and a central networkserver in the network back-end, [2]. The communicationbetween end-devices and gateways is spread out on differentfrequency channels and data rates. The selection of the datarate is a trade-off between communication range and messageduration offering a range of 0.3 kbps to 50 kbps through anadaptive data rate scheme. The access is based on a proprietarychirp based spread spectrum scheme and the MAC protocol isbased on frequency and time ALOHA. LoRA operates in thesub-GHz bands and the vendors claim coverage on the orderof 10–15 km in rural areas and 3–5 km in urban areas.

Sigfox is also a LPWAN that supports infrequent bi-directional communication, employs ultra narrow-band (UNB)wireless modulation as access technology, while the MACprotocol is based on frequency and time ALOHA [1], [15].The upper layers are proprietary and their definition is notpublic. The vendor claims coverage on the order of 30–50 kmin rural areas and 3–10 km in urban areas.

IEEE 802.11ah is a WAN, offering low-power and long-range operation. The operating frequency of IEEE 802.11ahis below 1GHz, allowing a single access point (AP) to provideservice to an area of up to 1 km. The PHY and MAC protocoloperation is similar to the one present in the 802.11 family ofprotocols, extended with the introduction of restricted accesswindow during which only certain number of devices areallowed to contend based on their device IDs [16].

There are other network systems built on top of IEEE802.15.4 (6LoWPAN, ISA100.11a, WirelessHart) which arefocused on low number of devices while providing reliabilityguarantees. Finally, there are other network systems with theirown protocol stack such as Ingenu and Weightless.

All of the presented LPWANs assume a rather simplephysical layer processing and are not capable of coping withmassive number of simultaneously active devices.

B. 3GPP Low Power Wide Area Networks

Until recently, MTDs were being served by 2G based so-lutions. However, with the success of non-3GPP technologiesas previously described, such as LoRA, Sigfox, Ingenu andWeightless, the cellular industry decided to accelerate thedefinition of an efficient MTC set of solutions and came upwith solutions standardized in 2016. The aim was to introduce

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new features to the LTE releases that would support IoT-likedevices and would exploit the existing 4G coverage aroundthe world. However, these new features would need to alignwith the new IoT key requirements which can be summarizedas following:• Low cost receiver devices ( 2− 5 $);• Long battery life (> 10 years)• Extended coverage (+15 dB) over LTE-AIn order to achieve the three objectives (cost, power effi-

ciency, extended coverage), design choices were made:• Single antenna design (to reduce cost)• Half duplex transmission (to reduce cost)• Narrow band reception (to reduce cost, power consump-

tion)• Peak rate reduction (to reduce cost, complexity)• Limited MCS and limited number of Transmit modes (to

reduce complexity)• Lower transmit power (to reduce power consumption)• Extended DRX and new power saving modes (to reduce

power consumption)• Transmission repetition (for enhanced coverage)

Three types of IoT devices are currently supported in the 3GPPstandards up to Release 13. These are the category M1 (Cat-M1), NB-IoT (NB1) and the extended coverage GSM (EC-GSM). The latter solution targets a very specific market (2Gonly) and is most likely to stay as a niche technology as the2G systems spectrum resources are re-farmed into 4G.

1) Cat-M1: The eMTC (now denoted as cat-M1) comesfrom the need to support simpler devices than the UE typesdefined currently, while being capable to take advantage of theexisting LTE capabilities and network support. The changesin comparison with the LTE system take place both at thedevice and at the network infrastructure level, where themost important one is the reduction of the device-supportedbandwidth from 20 MHz to 1.4 MHz in both downlinkand uplink [17]. The main consequence of this change isthat the control signals (e.g. synchronization or broadcast ofsystem block information) which are currently spread overthe 20 MHz band, will be altered to support the coexistenceof both LTE-M UEs and the standard, more capable, UEs.Another important feature of this new UE category is thereduced power consumption, achieved by the transceiver-chaincomplexity and cost reduction, such as support of uplink anddownlink rate of 1 Mbps, half-duplex operation, use of asingle antenna, reduced operation bandwidth of 1.4 MHz, andreduction of the allowed maximum transmission power from23 dBm to 20 dBm. Furthermore, there is the requirementto increase the cellular coverage of these LTE-M UEs byproviding up to 15 dBs extra in the cellular link budget.

The preamble structure and access procedure are the same asin LTE, with the introduction of a simplified procedure withoutthe security overhead. It is focused on increasing coverage,while still keeping LTE-like functionality.

2) NB1: The NB-IoT (also denoted as NB1) pertains to aclean slate design of an access network dedicated to serve amassive number of low throughput, delay tolerant and ultra-low cost devices. NB-IoT can be seen as an evolution of eMTC

in respect to the optimization of the trade-off between devicecost and capabilities; as well as a substitute to legacy GPRSto serve low rate IoT applications. The main technical featuresare: (i) reduced bandwidth of 180 kHz in downlink and uplink;(ii) maximum device transmission power of 23 dBm; and (iii)increased link budget by 20 dB extra when compared withcommercially available legacy GPRS, specifically to improvethe coverage of indoor IoT devices. This coverage enhance-ment can be achieved by power boosting of the data andcontrol signals, message repetitions and relaxed performancerequirements, e.g., by allowing longer signal acquisition timeand higher error rate. An important enabler for this coverageenhancement is the introduction of multiple coverage classes,which allow the network to adapt to the device’s coverageimpairments.

It has a new PRACH structure based on multi-hopping andis not based on Zadoff-Chu sequences like in LTE. There arethree versions of the access protocol (full similar to LTE,medium similar to the optimized access in eMTC and lightwith a preamble followed by data transmission). The mainfocus of the NB-IoT is on providing extreme coverage, withsupported number of users similar to LTE-M [18].

IV. MMTC IN A MULTI-SERVICE AIR INTERFACE

A. System Model and Assumptions

In general, we assume mMTC to be part of a multi-serviceair interface suitable to serve all services envisioned in 5Gin a single air interface [19], as depicted in Fig. 1. Thebase physical layer assumption for such a multi-service airinterface is a multi-carrier system with a suitable waveformand flexible numerologies as standardized for New Radio (NR)in 3GPPP. Thus, the mMTC service (denoted as MMC inFANTASTIC-5G) may use part of a resource block grid as itis depicted in Fig. 1 and can be organized using all or part ofthese resources. Of course, the amount of resources availablefor mMTC and the numerology used will vary according tohigher layer management functionalities that balance servicerequirements in a given scenario or cell. For example, LTEprovides only limited resources for the PRACH that facilitatesthe access reservation protocol in LTE and thereby limits thenumber of serviceable users.

In contrast to this general view on a multi-service archi-tecture, we aim to present different solutions in a comparableframework such that the access protocol performance can begauged by different key performance indicators (KPIs). Thus,for evaluation of the proposal described in section V and VI,we consider a single cell scenario using the basic PHY layerassumptions summarized in Table I. This allows the evaluationof the base performance of different MMC PHY/MAC con-cepts. Furthermore, we assume a generic OFDM waveform asbase assumption that excludes topics like synchronization ro-bustness or service separation solved by appropriate waveformchoices [13].

B. Building Blocks

In order to address the mMTC challenges we have identifieda number of building blocks that are classified into (i) Physical

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Parameter Value ExplanationTTI 1 msBandwidth 10 MHz 50 PRBs per TTIAllocation size 1 PRB 1 PRB = 1 ms x 180 kHzNo. of antennas 1 Base assumption is single antenna at

UE and BSTraffic model Poisson Arrival rate λPacket size 8 BytesMeanwaiting time

0.5 ms Avg. time offset between wake-up ofthe UE and the beginning of the nextTTI when a SR is sent

ACK/NACKresponse time

3 ms A request or packet sent in TTI i isfollowed by ACK/NACK at TTI i +3, earliest retransmission then is TTIi+ 4

RandomBack-off

0..10 ms Uniform distribution, back-off afterNACK

MaxRetransmissions

4 The fourth NACK is the ”final”NACK

TABLE IBASIC ASSUMPTIONS FOR MMC EVALUATION

Layer, (ii) MAC layer, (iii) RRC layer and (iv) Waveforms.The focus of this paper is on the first two, i.e. the physicaland MAC layers. However, PHY and MAC enhancementsalone will not be able to solve the massive access challenges.Therefore, we also provide a short outlook on RRC andwaveforms.

1) Physical Layer: The design of access reservation pro-tocols is usually based on idealized assumptions about thePHY performance and behavior. A classical assumption incontention based protocols is that concurrently active usersare colliding and cannot be retrieved. Recently, MAC protocolanalysis took the capture effect [20] into account, i.e. thedecodability of users with sufficiently different powers suchthat at least one can be still decoded. PHY layer technologiesthat are able to resolve more collisions through advancedreceiver processing like successive interference cancellation(SIC) have been in focus to enhance the performance ofthe overall access protocol. Furthermore, the performanceof such technologies in different fading scenarios as wellas under the assumption of asynchronous communication,strongly determines the performance baseline of all MACprotocols based on specific PHY solutions. In FANTASTIC-5G we studied different PHY collision resolution techniquesin combination with various access protocols. On the one handclassical multi-user detection (MUD) as well as CompressiveSensing based enhancements are considered, and on the otherhand also Compress- or Compute-and-Forward based schemesare considered that can be closely related to or even combinedwith advanced protocols like Coded Random Access.

2) Medium Access Control Layer: We distinguishes threetypes of access protocols: (a) multi-stage; (b) two-stage; and(c) one-stage. These can be interpreted very differently, andeach of the three types may contain several access protocolvariants. We depict these in Fig. 2. A multi-stage accessprotocol (a), for which the current LTE connection establish-ment protocol is a prime example, is composed of at leastthree phases, the access, connection establishment (includingauthentication and security) and finally the data phase. A two-stage access protocol (b) allows the UE separating the access

UE BTS

Connection Establishment

Access Attempt 1

Access Attempt n

UE BTS

Access + Data Attempt 1

Access + Data Attempt n

Data

UE BTS

Access Attempt 1

Access Attempt n

Data

(a)

(b)

(c)

Fig. 2. High level description of the three considered access protocols types:(a) Multi-stage access protocol with an access, connection establishment anddata phase; (b) Two-stages access protocol with access and data phases; and(c) One-stage access with combined access and data phase.

notification stage with its data delivery stage, e.g. through anintermediate feedback message. This leaves room for feedbackand resource allocation to the UE from the eNB. The feedbackcould be power control and timing alignment. What is meantby a one-stage access protocol (c) is that both the accessnotification and data delivery need to be done in a singletransaction, e.g. using one or several consecutive packets orin a single transmission. All three types of access protocolscan lead to scheduled access mode, where the devices afterestablishing a connection to the network do not need to re-establish access in future attempts.

The work presented in this paper focuses on two-stageand one-stage access protocols. As a common assumption thesignaling associated with the first connection establishment(mostly the establishment of mutual authentication and secu-rity) is assumed to be reused from a previous session wheresome incarnation of a full multi-stage access protocol tookplace.

3) Radio Resource Control Layer: A major observationbeyond PHY and MAY layer was that the transition fromidle mode to connected mode and vice versa used in today’ssystems must be simplified or even avoided. Connectionlesstransmission of small packets from UEs once registered andauthenticated in the network may reduce the required numberof signalling messages significantly. In this case, a smallpacket must comprise both source and destination addressesand payload. An important component in the reduction ofthe required signalling, upon connection establishment, is theaddition of new RRC states such as the RRC extant state(see [21]) which will allow the devices to maintain the securitycontext active over a long period.

4) Waveforms: Another major conclusion is that due toproperties of new 5G waveforms tight uplink synchronizationis not required anymore for the small packets typical inmMTC (see [12], [13]). This allows to compress or even avoidbroadcast messages that are usually required for synchronousoperation. One example is the random access response (RAR)in LTE which consists of 56 bits for each UE that has senta detected preamble. Essentially, RAR comprises a temporaryidentifier, a time offset value, and a grant for the subsequentsignaling messages. While for the one-stage access protocolsdiscussed here the RAR can be completely omitted, otherslike the contention-based two-stage variants combined with

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new waveforms can significantly reduce such overhead.

C. Key Performance Metrics

To evaluate the performance of our various contributions onthe PHY and MAY layers detailed in sections V and VI weconsider two key performance indicators:• The Protocol Throughput (TP) denotes the total number

of served devices per TTI. It directly addresses themassive access problem by showing how many users canbe served given a certain access load.

• The Access Latency (AL) measures the amount of time(measured in TTIs) between T1 the time instant whena device has new data to transmit (packet arrival at thedevice) and T2 the time instant when the device’s data isreceived successfully (packet arrival at the receiver, whichin most cases is the Base Station). Here, it complementsthe throughput to provide a complete view. Withoutlatency considerations the throughput could be arbitrarilyenhanced by aggregation of access opportunities andlonger back-off times. Therefore, technologies can onlybe fairly compared if both KPIs are considered together.

This manuscript gives a compact overview of the proposedprotocols and highlights evaluation results obtained in theEU funded project FANTASTIC-5G. More details on theevaluation of the proposed protocols and additional results canbe found [21].

V. MAC PROTOCOL PROCEDURES

In this section we present three different MAC layer ap-proaches using idealized models of the physical layer. First,we present results for One-Stage vs Two-Stages Access Pro-tocols (OSTSAP) with different number of preambles andadditionally exploiting decoding of multiple collisions (captureeffect) showing that one-stage protocols offer better latencywhereas two-stage protocols with collision resolution allowfor much higher throughput. Second, we present Signaturebased Access with Integrated Authentication (SBAIA) thatextends the idea of random access preambles like in standardLTE to a signature formed of multiple preambles enablingmuch higher throughput with added functionality like au-thentication. Finally, we present Non-Orthogonal Access withTime-Alignment Free Transmission (NOTAFT) that exploitsthe relaxed timing constraints of Pulse-shaped OFDM andMIMO processing to lower the signaling overhead for MTDsand enable massive access.

A. One-Stage vs Two-Stages Access Protocols (OSTSAP)

In this section we describe implementation variants ofthe generic two-stage and one-stage schemes shown in Fig.2(b) and (c), respectively. In contrast to the other solutionspresented in this paper, the performance evaluation is limitedto pure protocol performance. In case of single-user detection(SUD), this means that two packets collided on the samedata resource are always lost, whereas a single packet isalways successful. In case of multi-user detection (MUD),we apply an idealized model to get the upper bound of the

potential performance gain [22]. We assume that at mosttwo superimposed packets on the same data resource can bedecoded given that the UEs have utilized different preambles.Unpredicted overlapping of more than two packets leads tothe loss of all of them. Of course, this scheme can be easilyextended to more than two users. In a more general view, theprobability of successful decoding of any packet P (n), giventhat n packets overlap, depends on multiple parameters, e.g.,the distribution of receive power at the BTS, the modulationand coding scheme (MCS) and the multiple access method onthe PHY layer itself [20].

A detailed introduction of the protocol options can be foundin [23]. The following paragraphs briefly summarize the two-stage protocol in Fig. 2(b): The UE sends a random preamblesequence, also referred to as service request. We assume a setof S sequences which can be uniquely detected and separatedat the BTS through a correlation receiver. However, the BTScannot distinguish whether just one single UE or severalUEs have sent the same sequence. The latter case is referredto as preamble collision. With increasing S, this probabilitycan be reduced at cost of a larger amount of required radioresources MS . Given a constant number of resource unitsper time slot, M = MS + MD, increasing MS reducesthe available data resources MD accordingly. Without lossof generality we assume in the following a preamble signalgeneration and transmission scheme equivalent to the PhysicalRandom Access Channel (PRACH) in LTE, and resource unitsmimic a Physical Resource Blocks (PRB) stacked in frequencydimension.

The BTS broadcasts information related to the assignmentof radio resources. In the simplest case this is a binary vectorV of length S indicating whether or not the originator ofthe respective sequence is allowed to transmit its data packetin the second stage. This implies a fixed mapping betweenpreamble sequence and data resource. Typically, the numberof sequences S exceeds the number of data resources MD byan over-provisioning factor N , i.e. S = NMD. Consequently,N sequences point to one single data resource. The BTS with-out multi-user capability will therefore acknowledge just onedetected preamble and reject the remaining. In case of MUD,a second detected preamble is acknowledged as well. A moresophisticated feedback scheme comprises a resource indexinstead of just one bit ACK/NACK, allowing the BTS for afully flexible assignment of the detected service requests to theavailable data resources at cost of a larger downlink signalingoverhead. A further enhanced scheme includes additionallythe queue length of waiting UEs that could not yet be served.This enables a distribution of the detected service requests inboth frequency and time domain, i.e. surplus service requestsare automatically shifted to the next free time slot. In thesecond stage the acknowledged data packet transmission takesplace. In case of any error, the retransmission scheme withparameters in Table I is initiated.

In the one-stage protocol shown in Fig. 2(c), the inter-mediate feedback after preamble detection is missing. Themain advantage is the acceleration of the complete process.Preamble for activity detection and data packet can be trans-mitted in the same time slot. However, the capability of the

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two-stage protocol to control data packet transmissions andto reduce collisions is not present any more. It is thereforestraightforward to combine one-stage access with MUD. Asignificantly high over-provisioning factor N allows the BTSto separate the service requests and to gain awareness howmany data packets overlap on each of the MD resources.

0 20 40 60 80 1000

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Arrival Rate λ [arrivals/TTI]

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One-Stage (SUD) with 108 preamblesOne-Stage (MUD) with 108 preamblesOne-Stage (MUD) with 216 preamblesTwo-Stage (SUD) with 108 preamblesTwo-Stage (SUD) with 216 preamblesTwo-Stage (MUD) with 108 preamblesTwo-Stage (MUD) with 216 preambles

Fig. 3. Protocol throughput of the one-stage and two-stage variants withSUD and MUD depending on the number of preambles S as a function ofthe arrival rate λ.

In Figure 3 the achievable protocol throughput with SUDand MUD is depicted as a function of the arrival rate λ fordifferent large sets of preamble sequences S. Obviously, thetwo-stage protocol outperforms its counterpart with respectto throughput. Main reason for this result is the possibilityto assign the available data resources through the interme-diate feedback after preamble detection, and consequentlyto reduce collisions. With a larger set of preambles S, theperformance can be significantly improved, especially in highload situations (arrival rate λ > 30), and motivates furtherefforts to optimize the preamble sequence design for 5G.MUD improves the performance for both the one-stage and thetwo-stage access protocol because the number of resources isvirtually increased. We remark that only cases with the sameover-provisioning factor N , i.e. the same ratio of availablepreambles and data resources can be directly compared, e.g.,two-stage SUD with 108 preambles (solid dark green) andMUD with 216 preambles (dotted light green).

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One-Stage (SUD) with 108 preamblesOne-Stage (MUD) with 108 preamblesOne-Stage (MUD) with 216 preamblesTwo-Stage (SUD) with 108 preamblesTwo-Stage (SUD) with 216 preamblesTwo-Stage (MUD) with 108 preamblesTwo-Stage (MUD) with 216 preambles

Fig. 4. Access latency of the one-stage and two-stage variants with SUD andMUD depending on the number of preambles S as a function of the arrivalrate λ.

Figure 4 shows the achievable access latency of successful

1

PRAC

H

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H

PRAC

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Data

1. Signature

2. Access Grant

3. Data

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Signature Frame

Preambles

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L sub-frame per frame(b)

(c)

5. Data4. Connection Setup

3. Connection Request

2. Access Grant

1. Preamble

6. Ack

UE BTS

(a)

Fig. 5. (a) LTE-based two-stages random access (b) Signature-based two-stages random access; (c) Physical random access resources mapping torandom access preambles and Signature frame constructed from L sub-framescomposed each by M random access preambles.

packet transmissions with SUD and MUD. We see that theone-stage protocol overall can achieve significantly smallerdelays if the traffic load is very low. A combination withMUD further reduces the access latency. The good resultfor very high load is misleading in this respect because thecorresponding throughput in Fig. 3 is close to zero. In therange around λ = 25, the two-stage protocol benefits from thelower collision probability, i.e. smaller retransmission rate. Wefurther see that a larger set of preambles S can also providesome gain regarding access latency and that combination withMUD is advantageous as well.

B. Signature based Access with Integrated Authentication(SBAIA)

In the LTE(-A/Pro) random access protocol, depicted inFig. 5(a), each device contends for access within a PhysicalRandom Access Channel (PRACH) by selecting randomly oneof the M available preambles. In case the device’s accessattempt is not successful (i.e. the preamble selected by thedevice was also activated by at least one other device or itwas not detected at all), then the device will back-off and re-attempt access later. This procedure is repeated until the deviceis either successful or the amount of allowed retransmissions isexceeded. In case the access attempt is successful, the devicehas then to inform the network about its identity and howmany resources it requires to transmit its data payload. Thisprotocol step is necessary only because the transmission of apreamble does not encode any information about the devicenor its requirements.

In contrast, in the proposed random access scheme, de-picted in Fig. 5(b), we allow each device to contend witha predefined sequence of preambles over multiple PRACHs,which we denote as the device’s signature. These signatures,i.e. the preamble activation pattern over multiple PRACHs,are constructed based on information unique to each device(such as the device’s identity). From a protocol standpoint,this signature can then be used to identify the device andits requirements (e.g. the amount of resources required totransmit its data payload). This in turn allows a significant

8

reduction of the amount of exchanges in the access protocolto achieve the same functionality, as it can be seen whencomparing Fig. 5(a) and (b). These signatures are transmittedsynchronously over a frame composed of several PRACHs,as depicted in Fig. 5(c). This is made possible only if thepreambles in each PRACH: (i) are orthogonal to each other;(ii) can be detected simultaneously; and (iii) allow the basestation to detect a preamble even when it is transmitted bymultiple devices [24], i.e. a collision in the “preamble space”is still interpreted as an activated preamble. This last propertycan be interpreted as the OR logical operation, since eachpreamble is detected as activated if there is at least onedevice that transmits the preamble. This observation was themotivation for the use of Bloom filters – a data structure basedon the OR operation for testing set membership [25] – for theconstruction of the access signatures. Specifically, the device’sidentity is hashed over multiple independent hash functionsand the resulting output used to select which preamble inwhich PRACH to activate. Finally, all the above properties canbe obtained from preambles generated from spread sequencessuch as the Zadoff-Chu sequences.

In the following we describe briefly the signature con-struction, transmission and detection. Assume that a device’sidentity is given by u and its corresponding signature as s(h) =f(u). Where f(.) corresponds to the operation of hashing overmultiple independent hash functions. The resulting signaturecan be represented as a binary vector, in such way that thebits at ’0’ correspond to inactive preambles, while bits at ’1’represent the active preambles. As the transmission of all thedevices’ signatures occurs in a synchronous fashion, then thebase station receiver will observe a superposition of all thetransmitted signatures as,

y =

N⊕h=1

s(h), (1)

where s(h) is the detected version of s(h). The detection if agiven signature is active is done by testing if the followingholds

s = s⊗

y, (2)

where⊗

is the bit-wise AND.The drawback of this signature construction is that even in

the case of perfect preamble detection and no false detection,the base station can still detect signatures that have not beentransmitted (i.e. the corresponding device is not active) forwhich (2) holds. In other words, the base station may decodefalse positives. The signatures can then be designed in terms ofthe number of active preambles and the signature length; andin doing so control the number of false positives generated.

The signature decoding can be performed in an iterativemanner, since the base station will receive each PRACH se-quentially; and compare each of the observed active preambleswith the valid signatures. This approach is inspired by thefact that the active preambles, which constitute a signature,are randomly spread over the PRACHs of the signature frameand, in principle, the base station does not need to receive allof them to detect that the signature has been transmitted. As

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Fig. 6. Protocol throughput for signature based access with 216 preambles.

the signature of a device is detected, the device is notified andgranted access to the channel, following the access protocoldepicted in Fig. 5(a).

In the following we provide a comparison in terms ofprotocol throughput and access latency compared with a LTE(-A/Pro) baseline. The PRACH configuration follows the detailsin Table I. The mean number of arrivals is assumed to beknown, and the signature based scheme dimensioned for it.The probability of preamble detection by the base stationis set to pd = 0.99 and the probability of false detectionof a preamble is set to pf = 10−3 [26]. In the baseline,i.e. LTE(-A/Pro) scheme, we assume the typical values forthe backoff window of 20 ms, a maximum number of 10access attempts, 10 ms until the grant message is receivedand 40 ms until the connection setup (collision resolution) isreceived. We assume that PRACH occurs every 1 ms, wherethere are 54 available preambles for contention per PRACHin the LTE baseline which require 6 dedicated PRBs; whilefor the proposed scheme we assume that 216 preambles areavailable per PRACH that require 12 of the available PRBsfor their generation.

The protocol throughput achieved by this scheme is pro-vided in Fig. 6. Note that the result provided is the lowerbound throughput, yet for higher loads the throughput willnot go beyond 38 packets per TTI as this corresponds to themaximum available PRBs per TTI.

In Fig. 7 is provided the upper and lower bounds of theaccess latency achieved by the signature scheme, where it canbe observed that both bounds decrease with the increasingarrival rate. This decrease is due to the signature lengthdecreasing with the access load, which has a direct impacton the access latency.

Signature based random access is a novel access schemethat allows the reduction of the exchanges required to transmitsmall payloads in wireless access protocols. The function-ality of the described protocol can be extended to includeauthentication and security establishment and prioritization oftraffic [27], [28]. This is possible, since the access pattern canbe made in such a way to encode any kind of information.

C. Non-Orthogonal Access with Time Alignment Free Trans-mission (NOTAFT)

In the current LTE system, both CP-OFDM and DFTs-OFDM impose strict synchronization requirements to the

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LTE BaselineSignature Lower BoundSignature Upper Bound

Fig. 7. Access latency of signature based access for 216 preambles.

system. In order to guarantee reliable link performance, thetiming inaccuracy of the receiving window needs to be keptwithin the range of the cyclic prefix. In the cellular uplink,however, the mobility of the users yields a continuous changein the propagation delay of their transmission signals, andthus introduces time-variant timing offsets. In order to tacklesuch random and variable timing misalignment, a closed-looptime alignment (TA) procedure is implemented in the LTEsystems for enabling the BS to track each individual user’suplink timing during an active connection. However, for MTCwith stringent power consumption limitations and sporadicactivity with rather short data packets, it is desirable to designa simplified access procedure that can enable a grant-free andTA-free transmission of a short data packet in a single shot,yielding the one-stage access according to Fig. 2.

The first requirement derived from the above problemstatement is the time asynchronous transmission, which is afeature supported by enhanced multi-carrier schemes like pulseshaped OFDM [29]. Pulse-shaped OFDM (P-OFDM) fullymaintains the signal structure of CP-OFDM, while allowingfor pulse shapes other than the rectangular pulse to balancethe localization of the signal power in the time and frequencydomain. Let M be the FFT size, N be the number of sampleswithin one symbol period and Ts be the sampling period. Weconsider the time-frequency rectangular lattice for the OFDMsystem (T, F ), with T = NTs denoting the symbol periodandF = (MTs)

−1 the subcarrier spacing. The P-OFDM transmitsignal can be given as

s(t) =

+∞∑n=−∞

M∑m=1

am,ng(t− nT )ej2πmF (t−nT ). (3)

Here, am,n is the complex-valued information bearing symbolwith sub-carrier index m and symbol index n, respectively,and g(t) represents the transmit pulse shape. At the receiver,demodulation of the received signal r(t) is performed basedon the receive pulse shape γ(t):

am,n =

∫ +∞

n=−∞r(t)γ(t− nT )e−j2πmF (t−nT ). (4)

By carefully designing the pulse shapes g(t) and γ(t), thepower localization in the time and frequency domain of a pulsecan be adjusted. In this work, robustness against distortionsfrom large timing offsets is desired. To this end, following

BSUE1 UE2broadcast system info

downlink

syncdownlink

sync

payload+UEid UEid

decodedACK

payload+UEid cannot

decode

No ACK received

2nd try

payload+UEid

random

time

backoff

one-shot

transmission

Fig. 8. Proposed random access procedure with non-orthogonal timealignment free transmission

the design approach elaborated in [30], an orthogonalizedGaussian pulse which spreads four symbol periods is adoptedas the transmit and receive pulse. In comparison to CP-OFDM,it can be shown that this pulse exhibits a high resilience againsttiming offsets. This allows for asynchronous transmissionwithout timing adjustment within cell coverage. Therefore, thetiming alignment procedure during the random access phasecan be omitted.

In contrast to the baseline assumptions outlined in Table I,we assume pulse shaped OFDM (P-OFDM) [29] coupled witha space division multiple access (SDMA) scheme relying onmultiple antennas at the BS. Coupling these two technologiesfacilitates a non-orthogonal grant-free access scheme support-ing collision resolution based on MIMO detection techniqueson the BS side. To this end, we assume that each spatial layercarries a demodulation reference signal (DMRS) orthogonalto those of the other layers.

The proposed random access scheme with non-orthogonalTA-free transmission is illustrated in Fig. 8 and can bedescribed as follows:

1) UE establishes downlink synchronization to the primarycell and obtains the system configuration by decodingbroadcast channel information. The broadcast informa-tion may include cell-specific reference signal setting,maximum number of retransmission and default trans-mission scheme.

2) The UE randomly selects a resource block and transmitsits short packet data payload including its UE identifier.Here, the resource block consists of the time-frequencyresource on a spatial layer which is identified by itsDMRS.

3) The BS decodes the received signal. With a successfullydecoded data payload, the UE can be identified and anacknowledgment is fed back.

4) If a UE receives an ACK, the NOTAFT transmission iscompleted.

5) If no ACK is received, a UE takes a random time back-off, and then steps 2-3 are repeated until either an ACK

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Fig. 9. Protocol throughput of the non-orthogonal access with time alignmentfree transmission

is received or the maximum number of retransmissionsis reached.

We examine the uplink transmission in a single macro-cell scenario without timing adjustment. Due to the radiopropagation delay, a timing misalignment is present upon thearrival of the uplink signal at the BS. Assuming a cell radiusof 2 km, this timing misalignment is calculated according tothe propagation delay of the round trip, laying approximatelyin the range of [0, 13]µs. Link performance evaluation in[29] shows no significant loss for such scenario when P-OFDM with an appropriately designed pulse spanning foursymbol durations is employed. Therefore, for the protocolevaluation, we assume that packet loss is not caused by thetiming misalignment, but only by the resource collision, i.e. iftwo UEs select the same spatial layer on the same resourceblock. Since access preamble is used, the total number ofavailable resource blocks, i.e. 50 PRBs, can be employed fornon-orthogonal data transmission for 10 MHz mode. With atypical setting of four antennas on the BS side, this amountsto a total of 200 random access opportunities per TTI. Thisscheme is compared to the multi-stage access scheme with TA,depicted in Fig. 2. Parameters listed in Table I are applied.

Fig. 9 depicts the achievable packet throughput as a functionof the arrival rate. Since no resource is allocated for therandom access procedure, all PRBs are utilized for datatransmission. Given a much higher number of random accessopportunities, the proposed NOTAFT scheme offers signifi-cantly higher throughput especially when the arrival rate isrelatively high.

As shown in Fig. 10, since the timing adjustment procedureis removed, the proposed one-shot transmission scheme ex-hibits lower access latency compared to the baseline approach.

In summary, the proposed access procedure facilitates a’single-shot transmission’, enabling a reduced end-to-end la-tency as well as a lower signaling overhead for short packettransmissions. Thanks to this, it could substantially extend thebattery life of devices for a better sleep/wake-up operation.

VI. PHY AND MAC INTEGRATED SCHEMES

In the following we present four approaches that extendthe pure MAC protocol view of the previous section in termsof the physical layer assumptions. Here, all presented results

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Fig. 10. Access latency of the non-orthogonal access with time alignmentfree transmission

include simulation of physical layer transmission at least in-cluding coding and modulation and in most cases also channelestimation. First, we present Compressive Sensing Multi-UserDetection (CSMUD) which exploits sparsity due to sporadicactivitiy in mMTC enabling efficient Multi-User detectionin each random access slot of a slotted ALOHA setting.Second, we present Coded Random Access with PhysicalLayer Network Coding (CRAPLNC) extending the CSMUDapproach to frames using ideas from network coding, whichresults in a high throughput Coded Random Access scheme.Third, we present Compressive Sensing Coded Random Ac-cess (CCRA) that combines Coded Random Access CSMUDwith an underlay control channel significantly reducing controloverhead. Finally, we present Slotted Compute and Forward(SCF) focusing on very dense networks with high numbers ofmini base stations forwarding messages to a full base stationto efficiently enable mMTC scenarios.

A. Compressive Sensing Multi-User Detection (CSMUD)

The massive access problem outlined in section II is char-acterized by a massive number of MTDs that do not sendinformation continously but rather sporadically in large timeintervals or even event driven. As already outlined from aMAC perspective different access protocols can structure sucha sporadic access pattern. Still, the physical layer design of theaccess procedure remains open and naturally depends on theMAC protocol choice. Focusing on a one-stage protocol earlyworks on sporadic access in combination with Code DivisionMultiple Access (CDMA) already noted that intermittend useractivity leads to a multi-user detection (MUD) problem withsparsity that required novel algorithmic solutions [31]. Mostimportantly, with the development of compressive sensing(CS) a new mathematical tool was available to solve MUDwith sparsity [32]. A major advantage of combininig com-pressive sensing ideas and MUD lies in the theoretical guar-antees of CS for under-determined detection problems. Priorto the so-called Compressive Sensing Multi-user Detection(CSMUD) most MUD problems with sparsity focued on fully-determined systems where the number of resources and userscoincide. With CS detection guarantees can be given even ifthe number of resources is strictly smaller than the numberof users which enables user detection even in highly over-loaded CDMA setups. From this basic idea CSMUD has been

11

BS

3

2

1

user nodes

U TTIsNP ND

Fig. 11. Sporadic uplink transmission of multiple devices sending NP pilotsybmols and ND data symbols to a BS.

extended in multiple directions ranging from non-coherentcommunication [33] to channel estimation with user activitydetection. In the following we will revisit the CSMUD ideasfor channel estimation with simultaneous user actvity detection[34] and present numerical evaluation result in combinationwith a simple one-shot protocol. The basic CSMUD ideaspresented in the following also serve as an introduction tothe presented solutions in section VI-B and VI-C.

Following the assumptions laid out earlier, i.e. a certain timeand frequency budget is allocated to the MMC service andit is well separated and robust by choice of an appropriatewaveform, Fig. 11 depicts a schematic view on the MMCaccess protocol. Each TTI all Nact active users out of theoverall U users access the system by transmitting NP pilotsand ND data symbols both spread over the whole bandwidththrough one of NS pseudo-noise (PN) spreading sequencessi ∈ CLS ∀ i = 1, . . . , NS . The number of available spreadingsequences NS and their length LS determine the physicallayer performance of CSMUD. If the number of active usersNact is in the order of or larger than the number of availablespreading sequence NS collisions will occurr. If the less activeusers access the system than spreading sequences are availablethe systems performance will be dominated by the CSMUDperformance, i.e. the separation of the NS PN sequences oflength LS . Obviously, the longer the spreading sequence, thelower the achievable data rate given TTI length and bandwidthfrom Table I but the higher the robustness and separability ofspreading sequences. The resulting trade-off between MUDperformance and collision probabilty in dependence of retrans-mission is highly non-trivial. Only the physical layer designtrade-off between NP and ND was already investigated [34],but the interaction with different MAC protocols is still anopen problem. Hence, we will restrict the presented evaluationresults to a single parametrization that is designed to achievethe packet size of Table I.

As indicated in Fig. 11 each user sends a packet of two parts.The first part consists of NP pilot symbols that are uniqueper user and serve to estimate channel and activity throughCSMUD. The second part consists of the spread ND datasymbols that can be detected and decoded through standardapproaches. Each slot is assumed to occupy 10 MHz and 1 msper table I. To formalize the task of CSMUD we summarize theuser channels hi ∈ CNh ∀i = 1, . . . , U in a stacked channelvector h = [h1, . . . ,hU ]T ∈ CNNh . Due to the sporadic

activity, channels of inactive users will be modeled as zeros,i.e. for inactive user hi = 0Nh

∀i ∈ Z, where Z and Z denotethe index set of all active and inactive users, respectively. Thisleads to additional structure in the detection problem, i.e. thevector h is strictly group-sparse with groups of size Nh. Thejoint channel and activity signal model is then,

y = Sh + n, (5)

where S ∈ CM×N denotes the preamble matrix containingall user preambles, y ∈ CM denotes the received signalconsisting of the superimposed NP pilots of all users at thebase station and n ∈ CM summarize all noise sources asAWGN. The preamble matrix S exhibits a Toeplitz structureper user describing the convolution of channel and pilots,i.e. S = [Si, . . .SU ]T with Si being a Toeplitz matrix of useri pilots si.

Depending on the underlying system assumptions (asyn-chronicity, waveform, channel model, etc.) the exact valuesof M and N vary, but are dependent on the number of usersU , the pilot length NP and the lenght of the channel impulseresponse Nh. To simplify notation we focus on a one-tapRayleigh fading channel, i.e. Nh = 1. Then, the detectionproblem can be cast as

h = argminh∈CN

‖h‖0 s.t. ‖y − Sh‖2 < ε, (6)

which is easily extended to Nh-tap Rayleigh fading channelsif a group sparsity constraint is introduced (cf. [34]). Theminimization in (6) targets the sparsest vector denoted bythe “pseudo-Norm”‖h‖0 that counts the number of non-zerosgiven an `2-norm constraint to adhere to a given noise leveldependent on ε. The solution of (6) can be approached inmany different ways like convex relaxation or sub-optimalGreedy approaches which are meanwhile very well coveredin the literature.

To evaluate CSMUD with respect to the KPIs and as-sumptions outlined in section IV the physical layer approachCSMUD was combined with a simple one-stage protocol withrandom backoff according to the parameters of table I. Eachactive user transmits its data in the current TTI and repeats thistransmission in case of failure up to four times. A combinedMAC and PHY numerical simulation was conducted over 104

trails including the full physical layer processing (encoding,modulation, channel estimation, multi-user detection, demod-ulation, decoding) with BPSK, a [5; 7]8 convolutional code,least squares multi-user equalization and BCJR decoding.The activity and channel estimation step is achieved by thegroup orthogonal matching pursuit algorithm (GOMP). Thespreading factor is NS = 32 and up to K = 64 uniquespreading sequences / preambles are considered. The trafficmodel follows a Poisson arrival process with an arrival rateas shown on the x-axis of Fig. 12 and 13. Both KPIs dependon the signal-to-noise ratio (SNR), which is here assumed tobe either 0, 5 or 10 dB and identical for all users. Hence, weimplicitly assume some form of open-loop power control withidealized conditions.

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Fig. 12. Protocol throughput for CSMUD.

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Fig. 13. Access latency for CSMUD.

Fig. 12 depicts the protocol throughput of the CSMUDscheme which clearly shows a nearly linear scaling withincreased arrival rate for the 10 dB case up to an arrivalrate of 16 at which the probability of successfully detectingand decoding a user begins to decline due to the interferencelevel and a strong increase in retransmissions. Surprisingly,beyond λ = 32 the throughput increases again. This can beexplained by the performance of the GOMP algorithm that isemployed to solve (6). Up to half of the available sequencesK the detection performance declines because the numberof non-zeros to be estimated increases up to the maximumpotential for errors at exactly K/2. Due to the chosen stoppingcriteria the estimated channels h can be 100% wrong, i.e.all active users are estimated as inactive (missed detections)and all inactive users are estimated as active (false alarm).Beyond K/2, however, detection performance increases againwith the decreasing number of zeros in the estimated vector.This is finally limited by the least squares performance ofa two times overloaded CDMA system at 64 active users.Naturally, this behavior is also reflected for the lower SNRsof 5 dB and 0 dB with overall decreased performance. Note,that the CSMUD approach used here does not exploit re-transmission in any way. A combined decoding approach likepresented in Section VI-B can strongly improve performancein cases where single slots are overloaded. However, this ishighly dependent on the specific parameters of the system[35]. Furthermore, a comparison with the results presented inSection V-A indicates that the numerical simulations presentedhere behave differently than the pure MAC performance givenorthogonal resources. Especially, the slope is lower, but the

performance peak also occures later and seems broader hintingat a more robust behavior.

Fig. 13 presents the access latency which is very low forall presented working points and shows much lower overalllatencies than other schemes. Obviously, a single transmissionis sufficient most of the time for 10 dB, which is increasedwith lower SNR and higher arrival rates. The discussed GOMPbehavior does not influence the latency as strongly but leadsto small variations around the maximum latency. The accesslatency is much lower than for example using signature basedaccess or the frame focused PLNC enhanced scheme describedin Section VI-B. This is easily explained by the fact that theboth have to aggregate multiple TTIs to facilitate a successfulaccess compared to the setup used here.

B. Coded Random Access with Physical Layer Network Cod-ing (CRAPLNC)

This proposal is inspired by a random access scheme aimingat reduced signalling. More specifically it considers physi-cal layer techniques aiming to increase collision resolutionthrough advanced receiver processing, and their integrationwith the MAC protocol. It targets one-stage protocols, al-though the PHY layer solution can be also exploited in two-stage protocols by allowing more than one packet transmissionper radio resource block and resolving collisions throughadvanced receivers. The solution falls under the category ofcoded random access [36], where features of channel codingare exploited both at the slot and frame level. In particular,the scheme partially presented in [37], [38] is extended formassive access, with emphasis on the transmission of shortpackets. The proposed scheme assumes a minimum coordina-tion that ensures packet synchronization. The strategy focuseson collision resolution in a frame slotted ALOHA mediumaccess scheme, where users are granted certain level of re-dundancy per transmission attempt. It exploits two featuresof coded schemes: the first one relates to the property thatin the finite-field F2, although the individual messages cannot be correctly decoded, a linear combination of them (thebitwise XOR of a set of messages) may be. This property ledto the so-called compute-and-forward [39], [40], which provedachievable gains, from an information-theory point of view.The second one exploits the increase in the diversity order ofa linear system of equations if it is defined over an extendedGalois Field Fq with field order q = 2n. The multiple accessscheme operates as follows:• Given a frame size of S slots, users pick at random

the slot positions where they will attempt transmissionof each redundancy packet2. In principle the schemecan operate with a different level of redundancy R anddistribution. Although the illustrative example in Fig. 14,sets R = 2 for all users, the scheme can be combinedwith optimized distributions.

• Each message, previous to channel encoding and modu-lation3, allows for a linear pre-coding, which consist in a

2In general, redundancy packets are not the same, they correspond todifferent codewords per user message.

3The same channel code and modulation among users is assumed.

13

xK,2sKxK,1sK

·ENCUk uk,r

k,r

ck,r xk,r

......

1

2

3

K

...

Radio Resource Block

u2,1^

u1,1 + uK,1^ ^

u3,2 + u2,2^ ^

...

u3,1^

Fig. 14. Coded Random Access, with PLNC and extended Galois fieldprecoding.

symbol-wise multiplication in the extended Galois FieldFq , i.e. uFq

(m) = αr ×UFq(m) where UFq

(m) denotesthe m-th symbol of the non-binary representation of thebinary message U. Pre-coding coefficients αr ∈ Fq aregenerated randomly4.

• User detection and channel estimation is enabled bymeans of a preamble including the user signature andsmall overhead for identification of pre-coding coeffi-cients.

• At the receiver side, for each slot, the receiver performsuser detection and channel estimation, followed by thechannel decoding stage. Each decoded message or linearcombination (in F2), generates a new row at the framematrix A ∈ Fq . If A is full rank, collisions can be re-solved without the need for having one singleton packet.

For two-stage protocols, only the PHY layer componentis used, applying advanced decoding to the reception of thedata transmission stage. That is, the data transmission stagecan be modified to allow several users to transmit theirmessages over the same physical resources. It only transmitsthe payload data since in this scenario, the receiver knowswhich users are transmitting and simply takes advantage ofthe increased capture probability provided by the advanceddecoding scheme.

Relevant aspects of the scheme rely on the detection,channel estimation and decoding algorithms applied to thereceived signal within a single slot. In particular, for thedetection of colliding users and channel estimation in one-stage protocols, we resort to a CSMUD algorithm, as intro-duced in section VI-A (see also [41]). More specifically, weconsider channels with no delay spread and, thus, the simpleCSMUD form in (6) is sufficient. Note that in the case wherea packet fits a single radio resource block, as it is the casefor the minimum allocation size of 1 PRB = 180 kHz ×1 ms (see Table I), the channel can be assumed constant. Atthe receiver side, advanced decoding (joint decoder and the”seek-and-decode” principle) is implemented independently ateach slot, applied after standard SIC fails to decode any moremessages, thus reducing complexity. Final decision decodingis made at the end of the frame (S slots), although variants

4Note that the system can be configured to include no pre-coding, αr ∈ F2

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Fig. 15. Throughput performance of CRAPLNC massive access scheme forseveral SNRs.

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SNR=0dBSNR=10dBSNR=20dB

Fig. 16. Latency performance of CRAPLNC massive access scheme forseveral SNRs.

to the scheme could allow faster acknowledgments as soon asindividual messages are correctly decoded at each slot. Resultsare shown in Figs. 15 and 16 for very short codes (i.e. binaryLDPC with codeword length of 164 coded symbols) andsystem parameters defined in Table I under block fadingchannels, shows relevant throughput gains against benchmark(slotted ALOHA) for moderate/high loads, even with no pre-coding. Results are also encouraging in terms of robustnessagainst channel estimation errors, and user misdetection. Weshall remark that simulation results include full physical layerimplementation (multi-user detection, channel estimation anddecoding) over the medium access control (for a frame size ofS = 10 slots). Further details can be found in [21] includingadditional KPIs.

C. Compressive Sensing Coded Random Access (CCRA)

Recent concepts combine advanced MAC protocols withCompressive Sensing (CS) based multiuser detection [42],[43]. In this section, we introduce a concept for sparse jointactivity, channel and data detection in the context of the CodedALOHA (FDMA) protocol which we call Compressive CodedRandom Access (CCRA) extending the work in [42], [44],[45], [46]. We will argue that a simple sparse activity and datadetection is not sufficient (as many papers do) because controlresources are in the order of the data. In addition, we will 1)improve on the performance of such protocols in terms of thereduction of resources required for the user activity, channelestimation and data detection 2) achieve the required channelestimation quality for the successive interference cancellation

14

Fig. 17. Schematic of the CCRA scheme: sets are ... the common controlchannel

procedured required in coded ALOHA and CCRA.Let us assume for simplicity a single time slot and an OFDM

system with n subcarriers. This is easily generalized to thecase where there are multiple time slots, notably, within thecoherence time so that channels are constant over these slots.Let pi ∈ Cn be some signature from a given set P ⊂ Cn andxi ∈ Xn be an unknown (uncoded) data sequence (e.g. BPSK)from the modulation alphabet Xn both for the i-th user withi ∈ {1, ..., u} and u is the (fixed) maximum set of users inthe systems. Note that in our system n is a very large number,e.g. 24k. Due to the random zero-mean nature of xi we have1nE‖pi+xi‖2`2 = 1, i.e. the total (normalized) transmit poweris unity. Provided user i is active, we set:

α :=1

n‖pi‖2`2 and α′ := 1− α =

1

nE‖xi‖2`2 (7)

Hence, the control signalling fraction of the power is α. If auser is not active then we set both pi = xi = 0, i.e. eithera user is active and seeks to transmit data or it is inactive.Whether or wether not a user is active depends on the trafficmodel and is discussed below.

Let hi ∈ Cs denotes the sampled channel impulse response(CIR) of user i where s� n is the length of the cyclic prefix(further structural assumptions on hi are also discussed below).Let [hi, 0] ∈ Cn denote the zero-padded CIR. The receivedsignal y ∈ Cn is then:

y =

u−1∑i=0

circ([hi, 0])(pi + xi) + e (8)

yB = ΦBy (9)

Here, circ([hi, 0]) ∈ Cn is the circulant matrix with [hi, 0] inits first column. The AWGN is denoted as e ∼ CN (0, σ2) ∈Cn, i.e. E(ee∗) = σ2In. ΦB denotes some measurementmatrix (to be specified) where the active rows indices arecollected in B with cardinality m. Typically, B refers to someset of subcarriers in case of Fourier (FFT) measurements (Φ isorthonormal matrix) but, mainly for analytical purposes, alsoGaussian measurements are considered (Φ is not orthonormalmatrix).

The key idea of CCRA scheme is that all users’ preamblespi ∀i ’live’ entirely in B while all data resides in thecomplement BC , i.e. formally supp(pi) ⊆ B ∀i, (hence, fororthonormal matrix Φ like FFT there is no interference inbetween). We will call this a common overloaded controlchannel [45] which is used for the user activity and channeldetection. Since data resides only in BC the entire bandwidthBC can be divided into B frequency patterns. Each pattern isuniquely addressed by the preamble and indicates where thedata and corresponding copies are placed. the scheme worksas follows: if a user wants to transmit a small data portion, thepilot/data ratio α is fixed and a preamble is randomly selectedfrom the entire set. The signature determines where (and howmany of) the several copies in the B available frequency slotsare placed which are processed in a specific way (see below).Such copies can greatly increase the utilization and capacityof the traditional e.g. ALOHA schemes and which is used forthe data detection. An illustration of the scheme is in Fig. 17.

To derive a proper model for the user activity and channeldetection, we can stack the users as:

y = D(p)h+ C(h)x+ e (10)

where D(p) := [circ(s)(p1), . . . ,circ(s)(pu)] ∈ Cn×us andC(h) := [circ(n)([h1, 0]), . . . ,circ(n)([hu, 0])] ∈ Cn×un arethe corresponding compound matrices, respectively p =[pT1 pT2 ...pTu ]T und h = [hT1 hT2 ...hTu ]T are the correspond-ing compound vectors. In general, the measurement map isdifficult to analyze since D(p) depends on the specific designof the signatures pi. One choice of P that works for a smallnumber of active users and n� us is as follows: We choosep0 to be a sequence with unit power in frequency domain,i.e. such that (up to phases, which can be selected accordingto other optimization criteria, e.g. PAPR):

| (p0)i | ={ √

nm i ∈ B

0 else(11)

where p0 := Wp0 denotes the FFT transform of p0. Sincen ≥ us, the matrix D(p) can be completely composed ofcyclical shifts of the sequence p0, i.e. p1 = p0, p2 =

p(s)1 , p3 = p

(s)2 , . . . ,where p(i) is the i times cyclically

shifted p. Hence, D(p) is a single circulant matrix, and, inthis situation, we can show, that the control channel is finallyrepresented as:

yB = Ah+ z,

where A is a subsampled m × us FFT matrix, which isnormalized by a factor of

√1/m and z ∼ CN

(0, σ

2

n Im

).

Now, based on this measurement model, the most importantassumptions on the structure of h are:• Bounded support of hi (with high probability),

i.e. supp(hi) ≤ s and s� n• Sparse user activity, i.e. ku users out of u are actually

active• Sparsity of hi, i.e. ‖hi‖l0 ≤ ksHence, classical sparsity of h is k := kuks and the typical

arsenal of CS algorithms can be used. In CCRA, though, weare exploiting block-column sparsity: a k-sparse compound

15

vector h is so-called block-column sparse, i.e. (ku, ks)-sparse,if it consists of ku active blocks of length s each ks-sparse.Block-column sparsity is exploited in the detection of theactivity and channel by a new algorithm called HierarchicalHTP (HiHTP). HiHTP uses a so-called block-column tresh-olding operator Lks,ku(z). This operator can be efficientlycalculated by selecting the ks absolutely largest entries ineach block and subsequently the ku blocks that are largestin `2-norm. The strategy of the HiHTP algorithm is to use thethresholding operator Lku,ks to iteratively estimate the supportof h and subsequently solve the inverse problem restrictedto the support estimate. HiHTP comes with explicit recoveryguarantees while exploiting the specific structure of h, see[47].

The data detection algorithm can be seen as in instance ofcoded slotted ALOHA framework [36], tuned to incorporatethe particularities of the physical layer addressed in the paper,as described in the previous section. Specifically, the randomaccess algorithms assumes that:

• the users are active in multiple combinations of time-frequency slots, denoted simply as slots in further text,

• the activity pattern, i.e. the choice of the slots is random,according to a predefined distribution,

• every time a user is active, it sends a replica of packet,which contains data,

• each replica contains a pointer to all other replicas sentby the same user.

Obviously, due to the random nature of the choice of slots,the access point (i.e. the base station) observes idle slots(with no active user), singleton slots (with a single activeuser) and collision slots (with multiple active users). Usinga compressive sensing receiver, the base station, decodes indi-vidual users from non-idle slots, learns where the replicas haveoccurred, removes (cancels) the replicas, and tries to decodenew users from slots from which replicas (i.e. interferingusers) have been cancelled. In this way, due to the cancellingof replicas, the slots containing collisions that previously mayhave not been decodable, can become decodable. This processis executed in iterations, until there are no slots from whichnew users can be decoded. The above described operation canbe represented via graph. Analytical modeling of the aboveis the main prerequisite to assess the performance of therandom access algorithm, which in turn, allows for the designof the probability distribution that governs the choice slots,and which is typically optimized to maximize the throughput,i.e. the number of resolved packets per slot [36].

We follow the common simulation assumptions describedin Table I. Note that the pilot-to-data ratio is only 13% so theoverhead compared to LTE-4G has significantly reduced (re-ported to be up to 2000% in [48]). For the CCRA throughputevaluation, we use BPSK modulated subcarriers and succes-sive interference cancellation. Fig. 18 shows the throughput ofactually successfully recovered packets over different arrivalrates using at most three replicas per packet (optimum resultsfrom testing one to five copies). It can be seen that withthree replicas the performance is significant improved over,say, traditional slotted ALOHA (SA) which achieves only

max. 40% normalized throughput (i.e. 20 user/TTI). Whilenot shown here in detail, we mention that BER performancefor detecting replicas at 15dB SNR is well below 10−1 evenfor those with three-step interference cancellation detectionprocedure pointing out the good channel estimation perfor-mance. Altogether, we conclude that even for this challengingscenario the CCRA achieves a significant throughput gainwith reasonable BER performance per detected and decodedpacket and, at the same time, drastically reduces the signallingoverhead.

0 10 20 30 40 500

10

20

30

40

50


Prot

ocol

Thr

ough

put

[use

rs/T

TI]

Fig. 18. Throughput performance of CCRA over arrival rate.

D. Slotted Compute and Forward (SCF)

The presented Slotted Compute-and-Forward (SCF) ap-proach is a random access extension of the Compute-and-Forward (CF) relaying scheme introduced in [49]. The ap-proach combines the concept of network densification withphysical-layer network coding and a multicarrier transmissionscheme (OFDM). Using linear codes it enables the networkto exploit channel collisions [50] by decoding linear combi-nations of the messages transmitted by different devices thataccess the channel simultaneously in the same frequency band.The scheme assumes a dense network infrastructure with alarge number of MTC devices accessing the wireless channel,where each transmitter can be heard by multiple mini basestations. The data transmission is a two-hop communicationwith multiple mini base stations acting as relays. They receiveindividual superpositions of the sent signals, process, decodeand forward them to the macro base station. The macro basestation then estimates the transmitted messages over a finitefield based on the received linear combinations. A simplifiedexample is shown in Fig. 19.

Let us assume that the large set of uniformly distributedMTC devices Mtot, with Mtot := |Mtot|, is supported bya set of mini base stations Btot, which are connected to themacro base station through a wired or wireless communication.Each mini base station has only knowledge of its own channelcoefficients, whereas the MTC devices have no channel stateinformation. Let M ⊂ Mtot, with M := |M|, be a set ofactive MTC devices that can be heard by each mini base stationb ∈ B of a predefined subset B ⊂ Btot and B := |B|. Notethat, for simplicity, we have assumed here B = M . To increaserobustness it is often reasonable to choose B > M and solveinstead the over-determined system of equations. Each device

16

~w1 ∈ Fk2

~w2 ∈ Fk2

~w1 ∈ Fk2

~w2 ∈ Fk2

~x1 ∈ Cn

~x2 ∈ Cn

encode h11

h21

h12

h22

modulate

Solve for

original

decodeequationof originalmessage

modulateencode

decodeequationof originalmessage

message

~u1 =

2⊕l=1

β1l ~wl ∈ Fk2

~u2 =2⊕l=1

β2l ~wl ∈ Fk2

Fig. 19. Toy example describing the main processing blocks for the 2transmitters × 2 mini base stations case.

m ∈M, has a length-k complex message wm = (wRm,w

Im),

with wRm and wI

m real, respectively imaginary part drawnfrom some finite field Fkp , and maps its message to a length-ncodeword xm ∈ Cn subject to an average power constraint1n‖xm‖

2 ≤ P . We model the complex baseband signal ybreceived by mini base station b ∈ B as

yb =∑m∈M

hbmxm + zb , (12)

where zb ∼ CN (0, In) denotes independent Gaussian noise ofunit variance (per dimension) and hbm is the complex-valuedchannel coefficient between MTC device m and base station b.

The mini base station b performs rescaling and integer–forcing to obtain a noisy linear combination of the transmittedcodewords with integer coefficients:

yb = αbyb =∑m∈M

abmxm +∑m∈M

(αbhbm − abm)xm + αbzb︸︷︷︸effective noise

,

and decode Fp linear combinations

ub :=⊕m∈M

βbmwm

of messages wm over Fp. The scaling factor αb and theinteger coefficients abm are chosen such that the effectivenoise is minimized. The equation coefficients βbm satisfyβbm = [abm] mod p ∈ Fp. Once the mini base stations havesuccessfully decoded the linear equations they forward thesealong with the respective coefficients βb = (βb1, . . . , βbM ) tothe macro base station. If the equation coefficients have beenchosen such that the matrix B := (β1, . . . ,βB)T ∈ FB×Mp

is invertible over Fp, the macro base station estimates theoriginal messages by calculating [51].

(w1, . . . , wM )T = B−1(u1, . . . , uB)T . (13)

This approach makes the SCF solution especially suited fortwo-hop communication scenarios where the capacity limitedsecond hop is a bottleneck in the transmission. For a trans-mission to be successful, the superposition of messages hasto be successfully decoded, and B must be invertible overFp, meaning that the system of linear equations at the macrobase station has to be of full rank. To reduce the probability ofrank deficiency, each MTC device transmits the same messageover several frequency slots. For our simulations we considerfour slots. We further allow for cooperation between mini basestations and macro base station for up to four colliding devices.

0 5 10 15 20 250

5

10

15

20

25


Prot

ocol

Thr

ough

put

[pac

kets

/TT

I]

10 dB20 dB30 dB40 dB

Fig. 20. Throughput performance of SCF massive access scheme for severalSNRs of the first hop.

To analyze the end-to-end performance of the approach weconsider a system which consists of the following blocks:coding and modulation, resource allocation, transmission overthe wireless channel, signal reception and processing at themini base stations, forwarding to the macro base station, dataaggregation at the macro base station.

We assume that channel estimation has been performedand all active devices have been identified [52]. For theslotted transmission synchronization within the guard intervalis assumed. All nodes are equipped with a single antenna whileall devices transmit at an equal rate. The messages are encodedusing an LDPC channel code with a code rate of R=1/4. Eachtransmitter transmits complex messages of 128 bit, sendinga total number of 256 information bit. The encoded data ismodulated using a QPSK modulation alphabet. The channelis modeled as a four-tap block fading Rayleigh multipathchannel. For the sake of computational complexity we assumethat no more than 9 devices collide on the same resourceblock at a given time. Note that the number of active devicesduring one time-slot can be much higher. Since each devicetransmits two independent messages over complex channels,up to 18 messages can collide. The traffic model follows aPoisson arrival process with an arrival rate λ per time-slot.

Both KPIs, protocol throughput and access latency arehighly dependent on the signal-to-noise ratio (SNR).

The throughput, shown in Figure 20, is defined as the meannumber of successfully transmitted messages for a certainarrival rate λ. No retransmissions have been considered in thesimulations when determining the protocol throughput. Sincethe macro base station is still able to decode, packets are notdiscarded when two or more collisions occur, leading to a highthroughput even for the lower SNR region. In the consideredsetup the throughput does not improve significantly for SNRvalues above 20dB. In order to determine the access latency,depicted in Figure 21, we combined the SCF physical layerapproach with a random backoff protocol. Transmission isrepeated in case of failure until a successful transmission oruntil the maximum number of retransmissions is reached. If arandom access is successful at the first attempt, the expectedlatency includes the wake-up time and the time to perform asuccessful random access. If the random access is successfulat a later attempt, the access latency includes the latencycaused by the unsuccessful attempts prior to the successful

17

0 5 10 15 20 250

5

10

15

20


Acc

ess

Lat

ency

[ms]

10 dB20 dB30 dB40 dB

Fig. 21. Access Latency of SCF massive access scheme for several SNRsof the first hop.

transmission, the back-off time between retransmissions andthe latency of the last successful random access.

Since users manage to transmit their messages on averagein one or two transmissions, the SCF access latency can bekept very low.

E. Massive MIMO

We now consider a massive access solution that takesadvantage of the Massive MIMO capabilities, where the basestation of a massive MIMO system is equipped with a verylarge number of antennas and can create a very large number ofspatial Degrees of Freedom (DoF) under favourable propaga-tion conditions. Those DoFs are naturally suited to efficientlyserve a very large number of devices such as in machine-typecommunications, not only by spatially multiplexing a densecrowd of devices but also by improving contention resolutionin resource access. We target a multiple antenna system atlegacy frequency band (below 6GHz) where the devices areassumed to have a small number of antennas due to their size.The use of a larger number of antennas at the devices is inprinciple possible at millimeter-wave bands. However, the costof devices equipped with multiple antennas and beamformingcapabilities at those bands is currently a limitation in MTCapplications.

This solution addresses two important aspects in machine-type communications: acquisition of Channel State Informa-tion (CSI) and data communications for uplink traffic. CSI isestimated at the BS based on training via pilot sequences. Thepilot sequences available are assumed to be mutually orthogo-nal. For UL machine-type traffic, CSI estimation suffers fromtwo fundamental limits. First, the duration of pilot sequencesis limited by the (time-frequency) coherence interval of thechannel, as well as the transmit power of the device. Fororthogonal pilot sequences and in crowd scenarios, it meansthat the number of sequences could be in severe shortage.Therefore, allocation policy of the pilot sequences becomesa central question. Second, the data traffic is intermittent andonly a subset of the devices is active simultaneously. Hencea fixed pilot allocation to all the devices in the system wouldbe highly inefficient. Pilot allocation has rather to adapt andscale with the traffic activity pattern and not to the actualnumber of devices present in the system. A natural choice isto decentralize pilot access to the devices and make it random.

1

⋯

⋯

⋯Transmission slot

Device 1

Device 2

Device 3

Device 4

1 2

1 2

1 2

1 2

Transmission Frame

Fig. 22. Illustration of the transmission frame with four active devices{D1, D2, D3, D4} and two mutually orthogonal pilot sequences {Φ1,Φ2}.

Random access to pilot sequence leads to pilot collision,also known as pilot contamination. Pilot contamination isa major impairment in massive MIMO system: when usedfor data decoding, contaminated channel estimates lead tointerference that can be significant. The basic idea of theproposed joint pilot and data access is to randomize the effectof pilot contamination over multiple transmission slots, sothat the the effect of contamination-induced interference isaveraged out and becomes predictable. Related work can befound [53], [54], [55].

Uplink transmission is organized into transmission framesmade out of multiple transmission slots. A transmission slot isa time-frequency unit where the channel can be approximatedas constant. Fig. 22 depicts a simplified example with fouractive devices and two orthogonal pilot sequences, where τuis the duration of a transmission slot and τp is the durationof the pilot sequences in symbols. A block fading model isassumed, with independent realization in each slot and for eachdevice.

A device with data to transmit waits for the start of anew transmission frame. Each active device encodes its datainto one codeword that is divided into multiple parts andtransmits one pilot sequence followed by one part of thecodeword within a transmission slot. The pilot sequence servesto estimate the channel that is then used for soft decoding ofthe associated codeword portion. Within a transmission frame,a number of Ka devices are active out of a total number ofK devices. The activation probability of a device is pa.

In order to randomize the effect of pilot contamination,pilot hopping is performed. In each transmission slot, eachactive device selects one pilot sequence from the set of or-thogonal pilot sequences according to a pseudo-random pilot-hopping pattern that is unique to the device. Hence, in eachtransmission slot and for one given device, contamination-induced interference comes from different sets of devices. Thecodeword of the device experiences all possible contaminationevents from the Ka active devices, provided that the number oftransmission slots duration is sufficiently long. Likewise, foran asymptotic large number of transmission slots, the additivenoise at the BS and fading is averaged out. Under thoseasymptotic conditions, a maximal achievable rate per devicecan be defined within each transmission frame. Achievingthis rate assumes the following features: a) estimation of thenumber of active users at the BS, b) estimation of the averagechannel energy per device at the BS and at the device, c)BS broadcasts the rate associated to each value of average

18

channel energy. With conditions a) and b), the BS computes amaximal achievable rate per device. The BS broadcasts boththe channel energy and its associate rate for each active device.As the device itself knows its channel energy, it can associateits assigned rate.

For each transmission slot, the following steps are per-formed. First, the BS detects which pilot sequences are inuse. This is done by correlating the received signal with eachsequence available. The pilot detection outcomes are bufferedin order to be utilized for device activity detection. Second,for each pilot sequence detected, the corresponding channelestimate is computed. In this work, MMSE channel estimationis performed. When there is pilot collision, channel estimationis contaminated. Third, for each pilot sequence detected, amultiple antenna processing based on the channel estimate isapplied to the data symbols in the slot and its output is bufferedalong with its associated pilot index. In this work, MaximumRatio Combining (MRC) is utilized.

A unique pseudorandom pilot-hopping pattern is assignedto each device. The pilot-hopping patterns are known at theBS and serve for device identification at the BS. In orderto detect the transmitting devices, the BS combines the pilotsequence detection outcomes from the slots that follow thepattern. Based on the identifying pilot-hopping patterns, theBS identifies which MRC outputs to combine to decode thedata of each transmitting device.

Our main performance metric is the system uplink sum rate.It is the sum rate per transmission frame averaged over theactivation statistics of the device population. We work on anapproximation of the uplink sum rate R that is tight thanksto channel hardening and when the total number of devices islarge. This metric depends on the total number of BS antennasM and the number of pilot sequences, τp: the larger thosequantities, the more devices can be multiplexed. Bound R isalso a function of the device activation probability, pa. Tomaximize the sum rate, one can optimize pa and τp. Whenthe number of antennas M and the duration of transmissionslot τu are of the same order, the sum rate scales of

√Mτu.

Heuristic solutions indicate that one third of the transmissionslot should be devoted to training while the average numberof active devices should be of the order of

√Mτu.

Figure 23 and Figure 24 show the performance metricsfor a scenario with K = 400 and M = 100, 200, 400 foran SNR of 10dB. The transmission slot duration is fixed toτu = 300 and taup = 100: this ratio is chosen as it leads toa near-optimal solution (see above). We compute the averagesum rate per device from which we determine the averagedelay to transmit 8 bytes per device over a bandwidth of1MHz. This study relies on an information theory framework,where the devices are guaranteed to transmit their data reliably.Therefore, the average number of active devices that havesuccessfully transmitted is also the average number of activeusers in the TTI. The performance metrics are plotted againstthe arrival rate per TTI.

VII. DISCUSSION AND COMPARISON

The shown performance results show that each solutionprovides a trade-off between throughput and latency. Yet we

0 2 4 6 8 10 12 140

5

10

15

20


Prot

ocol

Thr

ough

put

[use

rs/T

TI]

# antennas M = 100# antennas M = 200# antennas M = 400

Fig. 23. Protocol throughout as a function of the arrival rate for K = 400and for M = 100, 200, 400.

0 2 4 6 8 10 12 140

0.05

0.1

0.15

0.2


Acc

ess

Lat

ency

[ms]

# antennas M = 100# antennas M = 200# antennas M = 400

Fig. 24. Access latency (ms) as a function of the arrival rate for K = 400and for M = 100, 200, 400.

can conclude that very significant gains can be achieved ifthe following techniques are applied in the design of massiveaccess protocols:• Physical layer: (i) compressive sensing for multi-user

detection (CSMUD, CRAPLNC), (ii) multi-user decoding(OSTSAP, CRAPLNC), (iii) redesign of access preambles(OSTSAP) and (iv) multiple spatial layers (NOTAFT);

• Medium access layer: (v) coding over retransmissions(SBA, CRAPLNC), (vi) back-off schemes (OSTSAP,CRAPLNC);

• Protocol Design: (vii) one-stage protocols (CSMUD) and(viii) low overhead network synchronization (NOTAFT).

One final remark is that for all the schemes a large part ofthe complexity is at the receiver of the base station, while thetransmitter operation at the devices does not suffer an increasein complexity (with the exception of the CRPLNC scheme).

VIII. CONCLUSIONS

For massive Machine Type Communications to take place,there is the need for efficient access protocols capable of with-standing a massive number of devices contending for networkaccess. We have proposed several random-access schemesof one-stage and two-stages types. Several physical layerand medium access layer techniques have been considered.The physical layer techniques include multi-user detectionusing compressive sensing techniques, collision resolution andharness of interference using physical layer network codingand non-orthogonal access with relaxed time-alignment. The

19

medium access layer techniques include coded random accessand signature based access, one/two-stage random access andfast uplink access protocols with a focus on latency reduction.A common evaluation framework has been defined and indi-vidual performance results provided. These results will help onthe design of a robust massive access solutions, by identifyingwhich techniques lead to higher protocol performance, anddoing so provide recommendations on the protocol design forthe NR in 3GPP.

ACKNOWLEDGEMENT

This work has been performed in the framework of theHorizon 2020 Project FANTASTIC-5G under Grant ICT-671660, which is partly funded by the European Union. Theauthors would like to acknowledge the contributions of theircolleagues in FANTASTIC-5G.

Emil Bjornson, Jesper H. Sørensen and Erik G. Larsson arecontributors of the work presented in section V.C.

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