Use Cases: 3GPP Long Term Evolution (LTE) and Wireless ... · • Physical cell-ID automatic...

3GPP Long Term Evolution LTE and

Wireless Sensor Networks (WSNs)

Dr.-Ing. Abdalkarim Awad

Page 1

Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel

Integrated Communication Systems Group

www.tu-ilmenau.de/ics

Use Cases:

3GPP Long Term Evolution (LTE) and


Self-Organizing


3.11.2011




Page 2




3GPP Long Term Evolution (LTE)




Page 3




LTE (3.9G) and LTE-A(4G)

• High data rates (100Mbps (1Gbps LTE-A) downlink and 50Mbps uplink)

• Low latency

• Support Mobility

• No more RNC (Radio Network Controller)

• RNC functionalities are moved in eNodeB

• X2 interface for seamless mobility (i.e. data/context forwarding) and interference management




Page 4




Costs

• CAPital EXpenditures (CAPEX) determine the direction

and level of investment telecommunications carriers

make (in network equipment as well as services)

• CAPEX is based on a combination of two primary

factors

– Number of customers served

– Volume and quality of services provided

• OPerational EXpenditures (OPEX) : running cost

• Growing wireless markets imply gowing OPEX




Page 5




Drivers For Self-Organization

• High complexity and high number of parameters

• Operation of heterogeneous networks

• Expanding number of Base Stations (BSs)

– Introducing of home evolved NodeBs (eNodeBs) leads to a huge number of nodes to be operated in multi-vendor scenarios

OPEX is expanding

• Reduction of OPEX requires reducing human interactions by

– Configuring and optimizing the network automatically while allowing the operator to be the final control instance

• High quality must be ensured SONs are essential




Page 6




Drivers For Self-Organization

• SON can improve the network performance and quality

of service. This can be achieved through applying

different techniques that can optimize the performance of

the network.

• Unlike 2G and 3G, in next generation mobile

communication networks, there will be no need for RNC.

Therefore the eNodeBs will be more interactive.

• There is room for cooperative management of base

stations among different operators.




Page 7




Functionalities Of SONs




Page 8




Functionalities Of SONs

Self-Configuration (plug and play)

Self-Optimization (auto-tune)

Self-Healing (auto-repair)

Self-P

lann

ing

(dynm

ic re

-com

puta

tion)

• Auto-setup • Auto- neighbor

detection • ...

• Coverage & capacity • Mobility robustness • Load balancing • ...

• HW/SW failuer detection

• Cell outage detection • ...




Page 9




Self-Configuration

• Definition

– “The process where newly deployed eNBs are configured by

automatic installation procedures to get the necessary basic

configuration for system operation”

• Works in preoperational state

• How

– Create logical associations with the network

• Establishment of necessary security contexts (providing a

secure control channel between new elements and servers in

the network)

– Download configuration files from a configuration server (using

NETCONF protocol)

– Doing a self-test to ensure that everything is working as intended




Page 10




Self-Configuration

eNB

eNB

eNB

1. IP address allocation, self-

configuration subsystem detection GW

4. Transport and radio configuration

Self-configuration

subsystem

Normal

OAM subsystem

OAM subsystem




Page 11




Self-Optimization

• Definition

– “The process where User Equipments‟ (UE) and eNBs‟

performance measurements are used to auto tune the network”

• Works in operational state

• How

– Optimizing the configuration while taking into account regional

characteristics of radio propagation, traffic and UEs mobility

– Analysis of statistics and deciding what are optimal parameters

– Detecting problems with quality, identifies the root cause, and

automatically takes remedial actions

• Examples: neighbor list optimization, coverage optimization,

etc.




Page 12




Self-Healing

• Definition

– “The process enabling the system detecting the problems by itself and mitigating them whilst avoiding user impact and reducing maintenance costs”


• End-to-end service recovery time should be < 1 sec

• How

– Automated fault detection

– Root cause identification

– Recovery actions application

– If fault cannot be resolved, do some actions to avoid performance degradation




Page 13




Architectures Of SONs




Page 14




Requirements & Taxonomy

• Support of network sharing between network operators

• Providing an easy transition from operator controlled to

autonomous operation

• Three architecture

– Centralized SON

– Distributed SON

– Hybrid SON




Page 15




Centralised SON

• SON algorithms are executed in the

OAM System

• SON functionalities reside in a small

number of locations at a high level in

the architecture

• Pros

– Easy to deploy and to manage

• Cons

– OAM is vendor specific (multi-vendor

optimization is problematic)

– Not applicable for situations where self-

organization tasks should be fast

eNB eNB

OAM OAM

Centralized OAM

Itf-N

SON

SON SON




Page 16




Distributed SON

• SON functionalities reside in the

eNB at the lower level of network

architecture

• Fully autonomous distributed RAN

optimization

• Pros

– Applicable for situations where self-

organization task should be

achieved fast

• Cons

– Hard to deploy and manage

– X2 interfaces should be extended

eNB eNB

OAM OAM

Centralized OAM

Itf-N

SON SON

x2




Page 17




Hybrid SON

• Idea is to push some of the SON

functionalities on the eNB itself

and some on OAMs

• Pros

– Allowance for a high degree of

automation guarantee, control

and inspection

• Cons

– Hard to deploy and manage

– Requiring of multiple interfaces

extensions

eNB eNB

OAM OAM

Centralized OAM

Itf-N

SON

SON SON

SON SON

x2




Page 18




Use Cases




Page 19




What Are The Use Cases Defined In 3GPP?

• Automatic Neighbor Relation (ANR)

• Coverage and capacity optimization

• Energy saving

• Interference reduction

• Physical cell-ID automatic configuration

• Mobility robust optimization

• Mobility load balancing optimization

• Random Access Channel (RACH) optimization




Page 20




Automatic Neighbor Relation (ANR)

• Relations between neighbor eNBs should be carefully

determined since they affect the network performance

– Handoff performance, call dropping probability, etc.

x2 x2 x2

eNB1

eNB2

eNB3

eNB4 The mobiles residing in the range of eNB2 may move to either eNB1 or eNB3 an in advance actions maybe done to optimize the performance (ressources reservation)




Page 21




Automatic Neighbor Relation (ANR)

• ANRs covers following steps

– Neighbor cell discovery

• eNB instructs UEs to do measurements

• New joined eNBs are detected based on the analysis of measurent results

– Configuration of X2 interfaces between eNBs

– Connection setup with neighbor eNBs

– ANR optimization

• Update as new eNBs join/disjoin the network

• How to accurately optimize the neighbor relation is still an open issue till now

• Some steps work in preoperational state, while some others work in operational state




Page 22




Mobility robust optimization

Reduce the number of HO-related Radio Link Failure (RLF) Reduce the HO-related issues that lead to degradation in the QoS.

Failures due to too late HO triggering Failures due to too early HO triggering Failures due to HO to a wrong cell

Time

Power




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Too Late HO




Page 24




Too Early HO




Page 25




HO To wrong cell




Page 26




Coverage & Capacity Optimization

• Goal

– Maximizing the capacity while ensuring coverage requirements

• Holes free coverage

• Improved capacity with given resources


• 3 Cases LTE coverage holes within

other Radio Access

Technologies (RATs)

• QoS degradation due to

frequent inter RAT handoffs Non LTE coverage LTE coverage

LTE cell smaller

than planned




Page 27





– LTE coverage holes and no alternative RAT

• Significant call drops due to coverage holes




Page 28





– Isolated LTE cells

• Coverage blackouts in network‟s border areas




Page 29





• Solution

– Update the BS parameters

such as height, tilt and Tx

power




Page 30




Energy Saving

• Goal

– Reduction of OPEX by saving energy resources


• How can energy be saved

– Tx power optimization

• Minimal saving but possible throughout the day

– Switching off some of the Tx of a cell

• Possible where antenna diversity is not required

– Complete eNB switch off

• Maximum saving but possible only during low load times

• Also if users are away from home eNB and closed subscriber group cells




Page 31




Interference Reduction

• Goal

– Improving the network performance by means of reducing the interference between its equipments


• Many limitations due to the applied frequency band

– Interference depends on frequency band characteristics

• Solutions

– Decrease eNBs density

• Hard to apply due to the capacity decrease and the existence of home eNBs that are not under the control of the network operator

– Power control and/or reconfigure the wireless setup

– Interference cancellation, coordination and randomization




Page 32




Physical Cell-ID Automatic Configuration

• Goal

– Automatically configure the physical Cell-ID (collision and confusion free assignment of physical Cell-ID)

• Works in preoperational state

– A part of self-configuration procedure

• Main limitation is that there is only 504

physical Cell-IDs available

• Solution

– eNB-based solution

(distributed solution)

– OAM-based solution

(centralized solution)




Page 33




Physical Cell-ID Automatic Configuration

• eNB-based solution (distributed solution)

– eNB chooses an arbitrary Cell-ID

– eNB instructs UEs to do measurements, collects and analyses measurements results

– eNB starts communicating with neighbors using X2 interfaces

– In case the eNB has detected a conflict, a new Cell-ID is assigned and the procedure is repeated again

• OAM-based solution (centralized solution)

– eNB instructs UEs to do measurements, collects and sends the results to the OAM

– The OAM assigns a Cell-ID to the eNB

– Cell-ID assigning procedure may require doing updates to other eNBs in the network




Page 34




Conclusions

• Future mobile communication networks will be much

more dynamic and hard to manage SONs are a

necessity

– Optimize the performance

– Reduce OPEX

• Three Architecture for SON

– Centralized, distributed and Hybrid

• Very important: SONs should allow the network

operator to be capable of doing any required changes




Page 35




Summary (what do I need to know)

• Why Self-organization in next generation networks?

• How can self organization be used in different use case

such as

– ANR

– Mobility rubostness

– Energy saving

– PCI




Page 36




References

• Self-Organizing Networks (SON):Concepts and Requirements, 3GPP TS 32.500 V0.3.1 (2008-07)

• LTE Operations and Maintenance Strategy, white paper

http://www.motorola.com/staticfiles/Business/Solutions/Industry%20Solutions/Service%20Providers/Network

%20Operators/LTE/_Document/Static%20Files/LTE%20Operability%20SON%20White%20Paper.pdf

• OAM Architecture for SON, 3GPP TSG SA WG5 & RAN WG3 LTE Adhoc, R3-071244 ,13th – 14th June

2007

• Self-X RAN, http://www.wiopt.org/pdf/WiOpt09_Keynote_Speech3.pdf

• Self-Organizing Networks, NEC's Proposals For Next-Generation Radio Network Management,

http://www.nec.com/global/solutions/nsp/mwc2009/images/SON_whitePaper_V19_clean.pdf, February 2009

• Self Organizing Networks: A Manufacturers View, ICT Mobile Summit Santander, Spain, June 2009

• S. Feng, E. Seidel, Self-Organizing Networks (SON) in 3GPP Long Term Evolution,

http://www.nomor.de/uploads/gc/TQ/gcTQfDWApo9osPfQwQoBzw/SelfOrganisingNetworksInLTE_2008-

05.pdf

• Next Generation Mobile Networks Beyond HSPA and EVDO, NGMN Alliance, December 2006

• NGMN Recommendation on SON and O&M Requirements, NGMN Alliance, December 2008

• NGMN Use Cases related to Self Organizing Network, Overall Description, NGMN Alliance, December 2008

• E. Bogenfeld, I. Gaspard, “Self-X in Radio Access Networks”, end-to-end efficiency FP7 Project, December

2008

• Self-organizing Networks (SON) in 3GPP Long Term Evolution, Nomor Research GmbH, May 2008

• Self-configuring and Self-optimizing Network Use Cases and Solutions. 3GPP TR36902 v1.2.0, June 2009

http://www.motorola.com/staticfiles/Business/Solutions/Industry Solutions/Service Providers/Network Operators/LTE/_Document/Static Files/LTE Operability SON White Paper.pdf

































http://www.wiopt.org/pdf/WiOpt09_Keynote_Speech3.pdf












http://www.nec.com/global/solutions/nsp/mwc2009/images/SON_whitePaper_V19_clean.pdf













http://www.nomor.de/uploads/gc/TQ/gcTQfDWApo9osPfQwQoBzw/SelfOrganisingNetworksInLTE_2008-05.pdf

















Page 37








Page 38





Network

• Large Size

• Mostly Static

• Data-centric

• Private Nets.

• ……

Node

• Limited resources

• Battery

• No Global ID

• …

Applications

• Habitat Monitoring

• Smart home, building , metering (smart Grid)

• Surveillance and rescue

• …..




Page 39




SENSOR NETWORKS ARCHITECTURE

Internet, Satellite,

Sink

Task Manager




Page 40




Characteristics of sensor nodes

• Low cost*, size, and weight per

node

• Limited resources – Computing : microcontroller

– Storage: few KB of RAM, 100s of KB of flash.

– Communication: low range , poor connectivity,

sometimes only broadcasting.

• Normally rely on batteries.




Page 41




Characteristics of sensor nodes

• Prone to failures

• More use of broadcast communications instead

of point-to-point

• Nodes do not have a global ID such as an IP

address

• The security, both on physical and

communication level, is more limited than in

classical wireless networks




Page 42




CHARACTERISTICS OF WSNs

• Very large number of nodes

• Nodes need to be close to each other

• Asymmetric flow of information

• Communications are triggered by queries or events

• Limited amount of energy

• Mostly static topology




Page 43




APPLICATIONS




Page 44




Military Applications:

• Monitoring friendly forces, equipment and ammunition

• Battlefield surveillance

• Reconnaissance of opposing forces and terrain

• Targeting

• Battle damage assessment

• Nuclear, Biological and Chemical (NBC) attack

detection and reconnaissance




Page 45




Environmental Applications

• Tracking the movements of birds, small animals, and

insects

• Monitoring environmental conditions that affect crops

and livestock

• Chemical/biological detection

• Pollution study

• Precision agriculture

• Flood detection, and Forest fire detection.




Page 46




Habitat Monitoring http://www.greatduckisland.net Great Duck Island in Maine.

http://www.greatduckisland.net/




Page 47




Health Applications

•

• Providing interfaces for the disabled • Integrated patient monitoring • Diagnostics • Telemonitoring of human physiological data • Tracking and monitoring doctors and patients inside a hospital, and • Drug administration in hospitals




Page 48




• Traffic monitoring, accident detection, recovery assistance

• Finding out empty parking lots in a city, without

asking a server (car-to-car communication)

• Vehicle tracking and detection

Smart Roads




Page 49




Smart Grid

• Monitoring product quality

• Factory Floor Automation

• Constructing smart homes

• Constructing smart office spaces

• Smart spaces




Page 50




Smart Grid

WSN is able to offer

customers and utilities a

convenient, cost-effective

way to monitor energy

creation in real-time, as well

as manage the deployed

system componentsuality

• Factory Floor Automation

• Constructing smart homes

• Constructing smart office spaces

• Smart spaces




Page 51




WSNs, How To Self-Organize?

• Supervised self-organization

– An overlay layer of supervision (can be provided by the user or an overlay management system)

– Better described with centralized self-organization

• Unsupervised self-organization

– Little or no interaction with the ultimate user/ management system

– Better described with distributed self-organization

• Hybrid self-organization

– Inherits properties of both, supervised and unsupervised




Page 52




Where Is Self-Organization Required?

• Goal: Enable WSNs to adapt themselves based on their environment (not being application-specific)

• Self-organization techniques are required in many tasks, such as

– Deployment and topology control of WSNs

– Address management

– Channel access

– Routing

– Power efficiency

– Quality of Service (QoS )

– .....




Page 53




Deployment of WSNs




Page 54




Deployment Issues! What Does it all mean?

• Question

– How should sensor nodes be deployed so that a required

QoS is guaranteed?

• Challenges

– Which parts of the area should be covered to detect

particular events?

– What number of sensor nodes is needed and where

should they be placed physically?




Page 55




Deployment Issues! What Does it all mean?

• Adequate deployment simplifies other tasks

– Clustering, access to the medium, routing, etc.

• Deployment problem can be complicated if

– Cost minimization is required

– Some parts of the area need to be covered better than

others

– Nodes of different types and sensing capabilities are

present

– The area is not a two dimensional plane




Page 56




Research Challenges

• Relocation of nodes positions and may roles

– A strategy for inter-nodes communication to reposition

themselves is essential (self-organization efficient

network)

• Nodes deployment in three dimensional space

– Most existing solutions assume the deployment to take

place in two dimensional space

– In real terrain, the deployment is three dimensional and is

a NP hard complexity problem




Page 57




Channel Access Control (MAC)




Page 58




Requirements

• MAC mechanism should provide

– Energy conservation

– Fairness

– High throughput and low delay

– Scalability

– Robustness against frequent topology change

– High degree of self-organization

– …..

• Restrictions

– Limited energy, computational, and communication resources in WSNs




Page 59



www.tu-ilmenau.de/ics 59

POWER CONSUMPTION

SENSOR

0

5

10

15

20

Po

wer

(mW

)

CPU TX RX IDLE SLEEP

RADIO




Page 60




Taxonomy

• Contention-based protocols

– Allow nodes to independently access to a shared medium

– Nodes are not required to form a cluster or certain topology

– Suitable for applications with rather unpredictable events

occurrence, network topology and network mobility

– Pros

• Good scalability in term of new nodes joining the network

– Cons

• Organization of sleep and wake-up phases is complicated

• For energy efficiency, control overhead is required to keep neighbors

synchronized

• Idle listening, collisions, overhearing, etc.




Page 61




Taxonomy

• Schedule-based (TDMA-based) protocols

– Time is divided into time slots, each is assigned to a node

– Suitable for stationary networks with almost predictable traffic

– Pros

• Avoid collisions, idle listening and schedules sleep without overhead

– Cons

• Dynamically changing the frame length and time slot assignments in

a cluster in difficult (node changes or inclusions)

• Poor scalability and poor mobility

• Effective slot assignment in multi-hop networks is also challenging.

Moreover, inter-cluster communication is complicated

• High quality time synchronization is required




Page 62




• Contention-based protocol

• S-MAC provides mechanisms to circumvent idle

listening, collisions, and overhearing

• Does not require more than 1 wireless interface

S-MAC




Page 63




S-MAC

• Each node alternates between a fixed-length listen and a

fixed-length sleep period according to its schedule

• The listen period of S-MAC can be used to receive and

transmit packets

• Neighbors are coordinate, so that their listen periods

start at the same time




Page 64




S-MAC

A

C

B Synch

Synch

Each neighbor (B or C) wishing to transmit a SYNCH packet picks one of the time slots randomly and starts transmitting if no signal was received in any of the previous slots

B and C goes back into sleep mode and waits for A’s next wakeup

A knows a neighbor B and C’s schedule, A can wake at appropriate times and send its own SYNCH packet to B/C

Phase 1

Synch phase

A listens for RTS packets from

neighboring nodes.

Node A transmits a CTS packet if an

RTS packet was received in the

previous phase

Phase 2

RTS phase

Phase 3

CTS phase

RTS

CTS




Page 65




S-MAC

• Nodes use RTS/CTS handshake and maintains a NAV

variable

• NAV is used to switch off a node to avaoid overhearing

• Schedule of node A and it„s neighbours can be

synchronized, i.e A can reach all with a single synch

• S-MAC allows the neighbouring nodes to agree on the

same schedule and thus forms a virtual cluster

• Virtual cluster solely refers to the exchange of

schedules, not data




Page 66




S-MAC

• Pros

– Circumvents idle listening, collision and overhearing

– Message passing approach of S-MAC reduces the latency

of passing and entire message

• Cons

– In message passing approach, a single node can block the

medium for a long time

– It is hard to adapt the length of the wakeup period to

changing load situations, since this length is essentially

fixed (as is the length of the listen period)




Page 67




Routing Schemes




Page 68




Constraints & Requirements

• Constraints

– Energy and bandwidth constrains

– Mostly no global addressing

• Requirements

– Energy-efficient and reliable routing mechanisms

– Maximizing the network lifetime

– Minimizing or even eliminating data traffic redundancy

– Efficient resource management

– Satisfying stationary as well as mobile sensor networks regardless if the events monitored are static or dynamic

– Cope with different data delivery modules (event-driven, query-driven, continuous and hybrid)




Page 69




Taxonomy

Routing protocols

Data-centric routing protocols

Hierarchical routing protocols

Location-based routing protocols

Network flow and QoS-aware routing protocols




Page 70




Data-Centric Routing Protocols

• Basic idea

– Sink sends queries to certain regions and waits data from

sensors located in that region

• Attribute-based naming is necessary to specify the

properties of data required

• Pros

– Can be used for periodic monitoring

– Energy efficient by removing redundant transmissions

• Cons

– Not good for tracking application




Page 71




Hierarchical Routing Protocols

• Basic idea

– Build a hierarchy among nodes and assign different roles to

nodes

• Aim at maintaining energy consumption of sensor nodes

either by enabling multi-hop communication within a

particular cluster or by aggregation and fusion of data

• Pros

– Scalability

– Reduced number of transmissions

– Energy efficiency

– Data aggregation




Page 72




Hierarchical Routing Protocols

• Cons

– Cluster head selection and cluster formation

• Example: LEACH




Page 73




r

r

Phase 1: Cluster Head Selection

Phase 2: Intra-cluster communication

Phase 3: Inter-cluster communication

BS

Clustering based Hierarichal Routing




Page 74




Location-Based Routing Protocols

Geographic Routing

Local information

Position,localization errors, Dead ends

D1

D2

D2>D1

Send data to N42-(x1,y1)

?

(x1,y1) Node Position

N32 (x,y)

N51 (x,y)




Page 75





• Basic idea

– Distance between two nodes is calculated using location information

– Energy consumption resulting from transmitting a packet to a particular sensor node can be estimated (efficient energy utilization)

• Protocols designed for Ad hoc networks with mobility in mind may be applicable for sensor networks as well

• Pros

– Better routing decisions

– Table less

– Guaranteed delivery




Page 76





• Cons

– Getting location information is a costly operation

– Local maximum problem




Page 77




Research Challenges

• Energy efficiency and robustness against node mobility

• Self-optimization and self-healing capabilities of routing

mechanisms




Page 78




Summary (what do I need to know)

• What is wireless sensor network?

• Some Applications of WSNs

• Characteristics of Sensor Networks?

• Why Self-organization WSN?

– Why Self-org in MAC

– Why self-org in Deployment and topology control




Page 79




References

• I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A Survey on Sensor Networks,” IEEE Communications Magazine, vol. 40, no. 8, pp. 102–116, August 2002.

• H. Karl and A. Willig, Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons, 2005.

• A. Awad, C. Sommer, R. German, and F. Dressler, “Virtual Cord Protocol (VCP): A Flexible DHT-like Routing Service for Sensor Networks,” in 5th IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS 2008). Atlanta, GA: IEEE, September 2008, pp. 133–142.

• A. Awad, R. German and F. Dressler, "Exploiting Virtual Coordinates for Improved Routing Performance in Sensor Networks," IEEE Transactions on Mobile Computing, vol. 10 (9), pp. 1214-1226, September 2011.

• M. Caesar, M. Castro, E. B. Nightingale, G. O‟Shea, and A. Rowstron, “Virtual Ring Routing: Network routing inspired by DHTs,” in ACM SIGCOMM 2006. Pisa, Italy: ACM, September 2006.

• Jin, Zhang, Jian-Ping, Yu, Si-Wang, Zhou, Ya-Ping, Lin, Guang, Li. 2009. "A Survey on Position-Based Routing Algorithms in Wireless Sensor Networks“, Algorithms 2, no. 1: 158-182.

• Akkaya, K. and Younis, M., “A survey of routing protocols in wireless sensor networks”, Elsevier Ad Hoc Networks 2007, Vol. 3 I (3). pp. 325-349.

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