6G: Towards a Fully Digital and Connected World
6G Wireless Summit, Levi, FinlandMarch 26th, 2019
Marco Giordani◦, Michele Polese◦, Marco Mezzavilla†, Sundeep Rangan†, Michele Zorzi◦◦University of Padova, Department of Information Engineering, Italy
† NYU WIRELESS, Tandon School of Engineering, New York University, Brooklyn, NY, USA
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
Ø Introduction and Motivations
Ø 6G Key Performance Indices (KPIs)
Ø 6G Potential Applications
Ø 6G Enabling Technologies• Disruptive Communication Technologies • Novel 6G Communication Enabler• Innovative Network Architectures
• Integrating Intelligence in the Network
Ø Conclusions and Research Directions
Outline
2
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
Introduction and Motivations
3
• From 1G to 5G, passing through UMTS and LTE innovations, each generation of mobile technology has tried to meet the needs of network operators and final consumers
• The rapid development of data-centric and automated processes may exceed even the capabilities of emerging 5G systems, thereby calling for a new wireless generation
time
1G
Voice callingMassive broadband Internet of Things
1-10 Gbps
1980
inno
vati
on
64 Kbps
SMS
1990
Internet
2 Mbps
2000
Internet of Applications
100-1000 Mbps
2010 2020
2.4 Kbps
2G3G
4G
5G
6G
2025-2030
Towards a Fully Digital and Connected World
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
Introduction and Motivations
4
5G is always associated with trade-offs: 6G will contribute to fill the gap between beyond-2020 societal and business demands and what 5G (and its predecessors) can support
• Consider potential applications for future connected systems and estimate the key requirements in terms of throughput, latency, connectivity and other factors.
• Identify use cases beyond the performance of 5G systems under development today.
• Survey emerging technologies that are not available in networks today but have significant promise for future 6G systems (including developments at all layers of the protocol stack)
Disruptive Technologies Energy Efficiency
Artificial Intelligence
Disaggregation/ virtualization
Cell-less Networks
New Spectrum
6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
5
6G: Towards a Fully Digital and Connected World 6G Applications and Use Cases
6G Wireless Summit, Levi, FinlandMarch 26th, 2019
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Use Cases
6
Massive Scale Communication
Tactile Internet
Autonomous Driving
Virtual Reality
Industry 4.0
Smart Cities
E-Health
High-Speed Mobility
Holographic Telepresence
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
7
INDOOR COVERAGE
• 80% of the mobile traffic is generated indoor
• 5G current cellular networks never really targeted indoor coverageØ High-frequencies cannot penetrate solid materialØ 5G densification through femtocells (proposed as a solution) presents scalability
issues and high deployment and management costs for operators
6G should target cost-aware indoor connectivity solutions autonomously deployed by end-users
and managed by the network operators (e.g., wireless relays coupled with
indoor communications)
6G
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
8
MASSIVE SCALE COMMUNICATIONS
• 5G networks are designed to support more than 1’000’000 connections per km2
• Mobile traffic will grow 3-fold from 2016 to 2021, pushing the number of connected devices to the extreme (> 500 billion connected things worldwide by 2030)
• This will stress already congested networks, which will not guarantee the required QoS
6G targets capacity expansion to offer high throughput and continuous connectivity,
even when civil communication infrastructures may be compromised (public safety is main requirement)
6G
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
9
eHEALTH
• OBJECTIVE: revolutionize the health-care sector, e.g., eliminating time and space barriers through remote surgery and guaranteeing health-care workflow optimizations.
• Current communication technologies cannot be applied in future health-careØ high cost and lack of real-time tactile feedback
Ø mmWaves can support low-latency, but do not guarantee connection continuity.
6G enhancements will unleash the potential of eHealth applications through innovations like mobile edge computing, virtualization
and artificial intelligence
6G
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
10
INDUSTRY 4.0 and ROBOTICS
• OBJECTIVE: digital transformation of manufacturing through Cyber Physical Systems (CPS) and Internet of Things (IoT) services.
• Enabling, among other things, Internet-based diagnostics, maintenance, operation, and direct Machine to Machine (M2M) communications in a cost-effective, flexible and efficient way.
• CPS will break the boundaries between the physical factory and the cyber space computation.
6G will foster the Industry 4.0 revolution through new semiconductor and IC innovations (e.g., terahertz scale electronic packaging solutions)
6G
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
11
SMART CITY
• OBJECTIVE: life quality improvements, environmental monitoring, traffic control and city management automation
• Current cellular systems have been mainly developed for broadband applications
• Smart city applications build upon data generated by low-cost and low-energy consuming sensors, which efficiently interact with each other.
6G will seamlessly include support for user-centric M2M communication, promoting
ultra-long battery lifetime combined with energy harvesting approaches
6G
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6G Applications and Use Cases
12
HOLOGRAPHIC TELEPRESENCE
• OBJECTIVE: remotely connect with an increasing amount of digital accuracy
• ISSUE: raw hologram, without any optimization nor compression, with colors, full parallax, and 30 fps, would require a daunting 4.32 Tbps data rate.
• ISSUE: latency requirement will hit the sub-millisecond, and thousands of synchronized view angles will be necessary (2 tiles for 4K/8K HD, and 12 tiles for VR/AR)
6G will develop architectures and network designs able to digitalize and transfer all the
5 human senses, increasing the overall target data rate
6G
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Applications and Use Cases
13
UNMANNED MOBILITY (Autonomous Driving)
• OBJECTIVE: fully autonomous connected and intelligent transportation systems, offering safer traveling, improved traffic management, and support for infotainment applications (>7000B$)
• Unprecedented levels of communication reliability and low end-to-end latency, even in ultra-high mobility scenarios (up to an impressive 1000 km/h).
• Sensors (more than 200 per vehicle by 2020) will demand increasing data rates (in the order of terabytes per driving hour), saturating the capacity of traditional technologies.
6G will pave the way for the coming era of connected vehicles through hardware and
software advancements and new technologies to eliminate typical 5G
latency/reliability/throughput trade offs
6G
6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
14
6G: Towards a Fully Digital and Connected World 6G Technologies and Innovations
6G Wireless Summit, Levi, FinlandMarch 26th, 2019
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
15
Disruptive Communication Technologies – Terahertz
4
Increasing energy and bandwidth
Increasing wavelength
Legacy Spectrum Millimeter Waves TeraHertz Visible Light6 GHz 30 GHz 300 GHz 300 GHz 10 THz 430 THz 770 THz
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.020 W 0.02 WReceived Power [W]0 dB 150 dBPathloss [dB]
10510-5
0.1
10-2
102
Frequency [THz]
Dist
ance
[m]
6 150 30010
100
200
Frequency [GHz]
Dist
ance
[m]
70 dB 150 dBPathloss [dB]
24
68
24
68
X Room [m]Y Room [m]
Rece
ived
Pow
er [W
] LED1
LED2
Distance [m]100 500 1000
20
80
120
Path
loss
[dB]
20 dB 140 dBPathloss [dB]
Micro Macro Smart City
Fig. 3: Pathloss for sub-6 GHz, mmWave and terahertz bands, and received power for Visible Light Communications (VLC). Notice that thelimits of the axis and the legends are different in each frequency band, to better illustrate the differences and the possible scenarios in whicheach band could be exploited. The sub-6 GHz and mmWave pathloss is computed using 3GPP models and considers both Line-of-Sight(LOS) and Non-Line-of-Sight (NLOS) conditions, while LOS-only is considered for terahertz (with the model from [8]) and VLC (usingthe model described in [9]).
mature than that on terahertz communications, also thanksto a lower cost and higher availability of experimentalplatforms. A standard for VLC (i.e., IEEE 802.15.7 [1])has also been defined; however, this technology has neverbeen considered so far for inclusion in a cellular networkstandard. As reported in Fig. 3, VLC have limited cover-age range, require an illumination source and suffer fromshot noise from other light sources (e.g., the sun), thuscan be mostly used indoors [12]. Moreover, they needto be complemented by RF for the uplink. Nonetheless,VLC could be used to introduce cellular coverage inindoor scenarios, which, as mentioned in Sec. II, is ause case that has not been properly addressed by cellularstandards. In indoor scenarios, VLC can exploit a verylarge unlicensed band, and be deployed without cross-interference among different rooms and with relativelycheap hardware.
Besides the new spectrum, 6G will also transform wirelessnetworks by leveraging a set of technologies that have beenrecently enabled by advancement in physical layer and circuitsresearch, but are not part of 5G. The following will be keyenablers for 6G:
• Integration of full-duplex capabilities in the com-
munication stack. With full-duplex communications,the transceiver in base stations and User Equipments(UEs) will be capable of receiving a signal while alsotransmitting, thanks to self-interference-suppression cir-
cuits [13]. This enables continuous downlink transmissionwith uplink acknowledgments or control messages (orvice versa), to increase the multiplexing capabilities andthe overall system throughput without using additionalbandwidth. Nonetheless, 6G networks will need carefulplanning in the design of allowed full-duplex proceduresand deployments to avoid interference, and novel designsof the cellular network schedulers [13].
• Novel channel estimation techniques (e.g., out-of-band
estimation and compressed sensing). The channel esti-mation for Initial Access (IA) and beam tracking will be akey component of ultra-high frequencies communicationsin a cellular context, as for mmWaves. However, it isdifficult to design efficient procedures for directionalcommunications, considering multiple frequency bandsand possibly a very large bandwidth. Therefore, 6Gsystems will need new channel estimation techniques.Recently, out-of-band estimation (e.g., for the angulardirection of arrival of the signal) has been proposed toimprove the reactiveness of beam management schemes,by exploiting the omnidirectional propagation of sub-6 GHz signals and mapping the channel estimation tommWave frequencies [14]. Similarly, given the sparsityin terms of angular directions of mmWave and terahertzchannels, it is possible to exploit compressive sensing toestimate the channel using a reduced number of samples.
• Sensing and network-based localization. The usage ofRF signals to enable simultaneous localization and map-
• Frequency bands between 100 GHz and 1 THz à bring to the extreme the potentials and challenges of high-frequency communications.
• Huge data rates (possible to allocate contiguous chunks of up to 200 GHz of spectrum)
PROPAGATION LOSS (compensated using directional antenna arrays, also enabling spatial multiplexing without increasing the interference)
MOLECULAR ABSORPTION (it is important to choose deployments in frequency bands not severely affected by molecular absorption)
4
Increasing energy and bandwidth
Increasing wavelength
Legacy Spectrum Millimeter Waves TeraHertz Visible Light6 GHz 30 GHz 300 GHz 300 GHz 10 THz 430 THz 770 THz
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.020 W 0.02 WReceived Power [W]0 dB 150 dBPathloss [dB]
10510-5
0.1
10-2
102
Frequency [THz]
Dist
ance
[m]
6 150 30010
100
200
Frequency [GHz]
Dist
ance
[m]
70 dB 150 dBPathloss [dB]
24
68
24
68
X Room [m]Y Room [m]
Rece
ived
Pow
er [W
] LED1
LED2
Distance [m]100 500 1000
20
80
120
Path
loss
[dB]
20 dB 140 dBPathloss [dB]
Micro Macro Smart City
Fig. 3: Pathloss for sub-6 GHz, mmWave and terahertz bands, and received power for Visible Light Communications (VLC). Notice that thelimits of the axis and the legends are different in each frequency band, to better illustrate the differences and the possible scenarios in whicheach band could be exploited. The sub-6 GHz and mmWave pathloss is computed using 3GPP models and considers both Line-of-Sight(LOS) and Non-Line-of-Sight (NLOS) conditions, while LOS-only is considered for terahertz (with the model from [8]) and VLC (usingthe model described in [9]).
mature than that on terahertz communications, also thanksto a lower cost and higher availability of experimentalplatforms. A standard for VLC (i.e., IEEE 802.15.7 [1])has also been defined; however, this technology has neverbeen considered so far for inclusion in a cellular networkstandard. As reported in Fig. 3, VLC have limited cover-age range, require an illumination source and suffer fromshot noise from other light sources (e.g., the sun), thuscan be mostly used indoors [12]. Moreover, they needto be complemented by RF for the uplink. Nonetheless,VLC could be used to introduce cellular coverage inindoor scenarios, which, as mentioned in Sec. II, is ause case that has not been properly addressed by cellularstandards. In indoor scenarios, VLC can exploit a verylarge unlicensed band, and be deployed without cross-interference among different rooms and with relativelycheap hardware.
Besides the new spectrum, 6G will also transform wirelessnetworks by leveraging a set of technologies that have beenrecently enabled by advancement in physical layer and circuitsresearch, but are not part of 5G. The following will be keyenablers for 6G:
• Integration of full-duplex capabilities in the com-
munication stack. With full-duplex communications,the transceiver in base stations and User Equipments(UEs) will be capable of receiving a signal while alsotransmitting, thanks to self-interference-suppression cir-
cuits [13]. This enables continuous downlink transmissionwith uplink acknowledgments or control messages (orvice versa), to increase the multiplexing capabilities andthe overall system throughput without using additionalbandwidth. Nonetheless, 6G networks will need carefulplanning in the design of allowed full-duplex proceduresand deployments to avoid interference, and novel designsof the cellular network schedulers [13].
• Novel channel estimation techniques (e.g., out-of-band
estimation and compressed sensing). The channel esti-mation for Initial Access (IA) and beam tracking will be akey component of ultra-high frequencies communicationsin a cellular context, as for mmWaves. However, it isdifficult to design efficient procedures for directionalcommunications, considering multiple frequency bandsand possibly a very large bandwidth. Therefore, 6Gsystems will need new channel estimation techniques.Recently, out-of-band estimation (e.g., for the angulardirection of arrival of the signal) has been proposed toimprove the reactiveness of beam management schemes,by exploiting the omnidirectional propagation of sub-6 GHz signals and mapping the channel estimation tommWave frequencies [14]. Similarly, given the sparsityin terms of angular directions of mmWave and terahertzchannels, it is possible to exploit compressive sensing toestimate the channel using a reduced number of samples.
• Sensing and network-based localization. The usage ofRF signals to enable simultaneous localization and map-
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
16
Disruptive Communication Technologies – Visible Light Communications
• Frequency bands between 430 GHz and 770 THz à complement RF communications by piggybacking on the wide adoption of LED luminaries
• VLC devices can switch between different light intensities to modulate a signal
• More mature research than THz (standard for VLC – IEEE 802.15.7 – has been defined)
4
Increasing energy and bandwidth
Increasing wavelength
Legacy Spectrum Millimeter Waves TeraHertz Visible Light6 GHz 30 GHz 300 GHz 300 GHz 10 THz 430 THz 770 THz
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.020 W 0.02 WReceived Power [W]0 dB 150 dBPathloss [dB]
10510-5
0.1
10-2
102
Frequency [THz]
Dist
ance
[m]
6 150 30010
100
200
Frequency [GHz]
Dist
ance
[m]
70 dB 150 dBPathloss [dB]
24
68
24
68
X Room [m]Y Room [m]
Rece
ived
Pow
er [W
] LED1
LED2
Distance [m]100 500 1000
20
80
120
Path
loss
[dB]
20 dB 140 dBPathloss [dB]
Micro Macro Smart City
Fig. 3: Pathloss for sub-6 GHz, mmWave and terahertz bands, and received power for Visible Light Communications (VLC). Notice that thelimits of the axis and the legends are different in each frequency band, to better illustrate the differences and the possible scenarios in whicheach band could be exploited. The sub-6 GHz and mmWave pathloss is computed using 3GPP models and considers both Line-of-Sight(LOS) and Non-Line-of-Sight (NLOS) conditions, while LOS-only is considered for terahertz (with the model from [8]) and VLC (usingthe model described in [9]).
mature than that on terahertz communications, also thanksto a lower cost and higher availability of experimentalplatforms. A standard for VLC (i.e., IEEE 802.15.7 [1])has also been defined; however, this technology has neverbeen considered so far for inclusion in a cellular networkstandard. As reported in Fig. 3, VLC have limited cover-age range, require an illumination source and suffer fromshot noise from other light sources (e.g., the sun), thuscan be mostly used indoors [12]. Moreover, they needto be complemented by RF for the uplink. Nonetheless,VLC could be used to introduce cellular coverage inindoor scenarios, which, as mentioned in Sec. II, is ause case that has not been properly addressed by cellularstandards. In indoor scenarios, VLC can exploit a verylarge unlicensed band, and be deployed without cross-interference among different rooms and with relativelycheap hardware.
Besides the new spectrum, 6G will also transform wirelessnetworks by leveraging a set of technologies that have beenrecently enabled by advancement in physical layer and circuitsresearch, but are not part of 5G. The following will be keyenablers for 6G:
• Integration of full-duplex capabilities in the com-
munication stack. With full-duplex communications,the transceiver in base stations and User Equipments(UEs) will be capable of receiving a signal while alsotransmitting, thanks to self-interference-suppression cir-
cuits [13]. This enables continuous downlink transmissionwith uplink acknowledgments or control messages (orvice versa), to increase the multiplexing capabilities andthe overall system throughput without using additionalbandwidth. Nonetheless, 6G networks will need carefulplanning in the design of allowed full-duplex proceduresand deployments to avoid interference, and novel designsof the cellular network schedulers [13].
• Novel channel estimation techniques (e.g., out-of-band
estimation and compressed sensing). The channel esti-mation for Initial Access (IA) and beam tracking will be akey component of ultra-high frequencies communicationsin a cellular context, as for mmWaves. However, it isdifficult to design efficient procedures for directionalcommunications, considering multiple frequency bandsand possibly a very large bandwidth. Therefore, 6Gsystems will need new channel estimation techniques.Recently, out-of-band estimation (e.g., for the angulardirection of arrival of the signal) has been proposed toimprove the reactiveness of beam management schemes,by exploiting the omnidirectional propagation of sub-6 GHz signals and mapping the channel estimation tommWave frequencies [14]. Similarly, given the sparsityin terms of angular directions of mmWave and terahertzchannels, it is possible to exploit compressive sensing toestimate the channel using a reduced number of samples.
• Sensing and network-based localization. The usage ofRF signals to enable simultaneous localization and map-
INTERFERENCE (limited coverage range, require an illumination source and suffer from shot noise from other light sources)
MULTI-CONNECTIVITY: need to be complemented by RF for the uplink
4
Increasing energy and bandwidth
Increasing wavelength
Legacy Spectrum Millimeter Waves TeraHertz Visible Light6 GHz 30 GHz 300 GHz 300 GHz 10 THz 430 THz 770 THz
0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 0.020 W 0.02 WReceived Power [W]0 dB 150 dBPathloss [dB]
10510-5
0.1
10-2
102
Frequency [THz]
Dist
ance
[m]
6 150 30010
100
200
Frequency [GHz]
Dist
ance
[m]
70 dB 150 dBPathloss [dB]
24
68
24
68
X Room [m]Y Room [m]
Rece
ived
Pow
er [W
] LED1
LED2
Distance [m]100 500 1000
20
80
120
Path
loss
[dB]
20 dB 140 dBPathloss [dB]
Micro Macro Smart City
Fig. 3: Pathloss for sub-6 GHz, mmWave and terahertz bands, and received power for Visible Light Communications (VLC). Notice that thelimits of the axis and the legends are different in each frequency band, to better illustrate the differences and the possible scenarios in whicheach band could be exploited. The sub-6 GHz and mmWave pathloss is computed using 3GPP models and considers both Line-of-Sight(LOS) and Non-Line-of-Sight (NLOS) conditions, while LOS-only is considered for terahertz (with the model from [8]) and VLC (usingthe model described in [9]).
mature than that on terahertz communications, also thanksto a lower cost and higher availability of experimentalplatforms. A standard for VLC (i.e., IEEE 802.15.7 [1])has also been defined; however, this technology has neverbeen considered so far for inclusion in a cellular networkstandard. As reported in Fig. 3, VLC have limited cover-age range, require an illumination source and suffer fromshot noise from other light sources (e.g., the sun), thuscan be mostly used indoors [12]. Moreover, they needto be complemented by RF for the uplink. Nonetheless,VLC could be used to introduce cellular coverage inindoor scenarios, which, as mentioned in Sec. II, is ause case that has not been properly addressed by cellularstandards. In indoor scenarios, VLC can exploit a verylarge unlicensed band, and be deployed without cross-interference among different rooms and with relativelycheap hardware.
Besides the new spectrum, 6G will also transform wirelessnetworks by leveraging a set of technologies that have beenrecently enabled by advancement in physical layer and circuitsresearch, but are not part of 5G. The following will be keyenablers for 6G:
• Integration of full-duplex capabilities in the com-
munication stack. With full-duplex communications,the transceiver in base stations and User Equipments(UEs) will be capable of receiving a signal while alsotransmitting, thanks to self-interference-suppression cir-
cuits [13]. This enables continuous downlink transmissionwith uplink acknowledgments or control messages (orvice versa), to increase the multiplexing capabilities andthe overall system throughput without using additionalbandwidth. Nonetheless, 6G networks will need carefulplanning in the design of allowed full-duplex proceduresand deployments to avoid interference, and novel designsof the cellular network schedulers [13].
• Novel channel estimation techniques (e.g., out-of-band
estimation and compressed sensing). The channel esti-mation for Initial Access (IA) and beam tracking will be akey component of ultra-high frequencies communicationsin a cellular context, as for mmWaves. However, it isdifficult to design efficient procedures for directionalcommunications, considering multiple frequency bandsand possibly a very large bandwidth. Therefore, 6Gsystems will need new channel estimation techniques.Recently, out-of-band estimation (e.g., for the angulardirection of arrival of the signal) has been proposed toimprove the reactiveness of beam management schemes,by exploiting the omnidirectional propagation of sub-6 GHz signals and mapping the channel estimation tommWave frequencies [14]. Similarly, given the sparsityin terms of angular directions of mmWave and terahertzchannels, it is possible to exploit compressive sensing toestimate the channel using a reduced number of samples.
• Sensing and network-based localization. The usage ofRF signals to enable simultaneous localization and map-
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
17
Disruptive Communication Technologies – Full-Duplex
• The transceiver in base stations and UEs will be capable of TX a signal while also TX
• Continuous downlink transmission with uplink acknowledgments or control messages àincrease multiplexing and system throughput without using additional bandwidth.
• IDEA: leverage channel state information acquired at a lower frequency as a form of side information on a higher frequency channel.
• Need to define a “transformation function” to relate the spatial correlation matrix derived at one frequency to another at a much different frequency
Disruptive Communication Technologies – OBB channel estimation
11
Gaussian or truncated Laplacian), and the complex coefficients ↵rc,i according to a suitable
fading model (e.g., Rayleigh or Ricean).
III. SYSTEM AND CHANNEL MODEL
We consider a multi-band MIMO system shown in Fig. 1, where ULAs of isotropic point
sources are used at the TX and the RX. The ULAs are considered for ease of exposition, whereas,
the proposed strategies can be extended to other array geometries with suitable modifications. We
assume that the sub-6 GHz and mmWave arrays are co-located, aligned, and have comparable
apertures. Both sub-6 GHz and mmWave systems operate simultaneously.FOR SUBMISSION TO IEEE 14
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freq
uenc
ies,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[47]
P.K
y,I.
Car
ton,
A.K
arst
ense
n,W
.Fan
,G.F
.Ped
erse
net
al.,
“Fre
quen
cyde
pend
ency
ofch
anne
lpa
ram
eter
sin
urba
n
LOS
scen
ario
for
mm
wav
eco
mm
unic
atio
ns,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[48]
K.
Han
eda,
J.-i.
Taka
da,
and
T.K
obay
ashi
,“E
xper
imen
tal
Inve
stig
atio
nof
Freq
uenc
yD
epen
denc
ein
Spat
io-T
empo
ral
Prop
agat
ion
Beh
avio
ur,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2007
,pp.
1–6.
[49]
V.N
urm
ela
etal
.,“M
ETIS
Cha
nnel
Mod
els,”
Mob
ilean
dw
irele
ssco
mm
unic
atio
nsEn
able
rsfo
rth
eTw
enty
-twen
ty
Info
rmat
ion
Soci
ety,
Tech
.Rep
.,20
15.
[50]
A.M
.Say
eed,
“Dec
onst
ruct
ing
mul
tiant
enna
fadi
ngch
anne
ls,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
50,n
o.10
,pp.
2563
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9,
2002
.
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A.A
lkha
teeb
,G.L
eus,
and
R.W
.Hea
thJr
.,“C
ompr
esse
dse
nsin
gba
sed
mul
ti-us
erm
illim
eter
wav
esy
stem
s:H
owm
any
mea
sure
men
tsar
ene
eded
?”in
Proc
.IEE
EIn
t.C
onf.
Acou
st.,
Spee
chSi
gnal
Proc
ess.
(IC
ASSP
),A
pril
2015
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2909
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3.
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M.
L.B
ench
eikh
,Y.
Wan
g,an
dH
.H
e,“P
olyn
omia
lro
otfin
ding
tech
niqu
efo
rjo
int
DO
AD
OD
estim
atio
nin
bist
atic
MIM
Ora
dar,”
Sign
alPr
oces
s.,vo
l.90
,no.
9,pp
.272
3–27
30,2
010.
[53]
M.B
engt
sson
and
B.O
tters
ten,
“Low
-com
plex
ityes
timat
ors
ford
istri
bute
dso
urce
s,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.48
,
no.8
,pp.
2185
–219
4,20
00.
[54]
R.
Key
s,“C
ubic
conv
olut
ion
inte
rpol
atio
nfo
rdi
gita
lim
age
proc
essi
ng,”
IEEE
Tran
s.Ac
oust
.,Sp
eech
,Si
gnal
Proc
ess.,
vol.
29,n
o.6,
pp.1
153–
1160
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1.
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Z.G
ao,L
.Dai
,Z.W
ang,
and
S.C
hen,
“Spa
tially
com
mon
spar
sity
base
dad
aptiv
ech
anne
lest
imat
ion
and
feed
back
for
FDD
mas
sive
MIM
O,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
63,n
o.23
,pp.
6169
–618
3,20
15.
[56]
M.M
isha
lian
dY.
C.E
ldar
,“R
educ
ean
dbo
ost:
Rec
over
ing
arbi
trary
sets
ofjo
intly
spar
seve
ctor
s,”IE
EETr
ans.
Sign
al
Proc
ess.,
vol.
56,n
o.10
,pp.
4692
–470
2,20
08.
[57]
I.F.
Gor
odni
tsky
and
B.D
.Rao
,“Sp
arse
sign
alre
cons
truct
ion
from
limite
dda
taus
ing
FOC
USS
:Are
-wei
ghte
dm
inim
um
norm
algo
rithm
,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.45
,no.
3,pp
.600
–616
,199
7.
Mm
Wav
eSy
stem
Sub-
6GHz
Syst
em
RF
Cha
in
DA
C
FOR
SUB
MIS
SIO
NTO
IEEE
30
[40]
R.J
.Wei
ler,
M.P
eter
,T.K
hne,
M.W
isot
zki,
and
W.K
eusg
en,“
Sim
ulta
neou
smill
imet
er-w
ave
mul
ti-ba
ndch
anne
lsou
ndin
g
inan
urba
nac
cess
scen
ario
,”in
Proc
.Eur
.Con
f.An
tenn
asPr
opag
.(Eu
CAP
),M
ay20
15,p
p.1–
5.
[41]
A.
S.Po
onan
dM
.H
o,“I
ndoo
rm
ultip
le-a
nten
nach
anne
lch
arac
teriz
atio
nfr
om2
to8
GH
z.”
inPr
oc.
IEEE
Int.
Con
f.
Com
mun
.(IC
C),
2003
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3519
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3.
[42]
S.Ja
ecke
l,M
.Pet
er,K
.Sak
aguc
hi,W
.Keu
sgen
,and
J.M
edbo
,“5G
Cha
nnel
Mod
els
inm
m-W
ave
Freq
uenc
yB
ands
,”in
Proc
.Eur
.Wire
less
Con
f.,M
ay20
16,p
p.1–
6.
[43]
A.O
.Kay
a,D
.Cal
in,a
ndH
.Vis
wan
atha
n.(2
016)
28G
Hz
and
3.5
GH
zW
irele
ssC
hann
els:
Fadi
ng,D
elay
and
Ang
ular
Dis
pers
ion.
[44]
R.C
.Qiu
and
I.-T.
Lu,“
Mul
tipat
hre
solv
ing
with
freq
uenc
yde
pend
ence
for
wid
e-ba
ndw
irele
ssch
anne
lmod
elin
g,”
IEEE
Tran
s.Ve
h.Te
chno
l.,vo
l.48
,no.
1,pp
.273
–285
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9.
[45]
K.H
aned
a,A
.Ric
hter
,and
A.F
.Mol
isch
,“M
odel
ing
the
freq
uenc
yde
pend
ence
oful
tra-w
ideb
and
spat
io-te
mpo
rali
ndoo
r
radi
och
anne
ls,”
IEEE
Tran
s.An
tenn
asPr
opag
.,vo
l.60
,no.
6,pp
.294
0–29
50,2
012.
[46]
D.D
uple
ich,
R.S
.Tho
m,G
.Ste
inb,
J.Lu
o,E.
Schu
lz,X
.Lu,
G.W
ang
etal
.,“S
imul
tane
ous
mul
ti-ba
ndch
anne
lsou
ndin
g
atm
m-W
ave
freq
uenc
ies,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[47]
P.K
y,I.
Car
ton,
A.
Kar
sten
sen,
W.
Fan,
G.
F.Pe
ders
enet
al.,
“Fre
quen
cyde
pend
ency
ofch
anne
lpa
ram
eter
sin
urba
n
LOS
scen
ario
for
mm
wav
eco
mm
unic
atio
ns,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[48]
K.
Han
eda,
J.-i.
Taka
da,
and
T.K
obay
ashi
,“E
xper
imen
tal
Inve
stig
atio
nof
Freq
uenc
yD
epen
denc
ein
Spat
io-T
empo
ral
Prop
agat
ion
Beh
avio
ur,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2007
,pp.
1–6.
[49]
V.N
urm
ela
etal
.,“M
ETIS
Cha
nnel
Mod
els,”
Mob
ilean
dw
irele
ssco
mm
unic
atio
nsEn
able
rsfo
rth
eTw
enty
-twen
ty
Info
rmat
ion
Soci
ety,
Tech
.Rep
.,20
15.
[50]
A.M
.Say
eed,
“Dec
onst
ruct
ing
mul
tiant
enna
fadi
ngch
anne
ls,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
50,n
o.10
,pp.
2563
–257
9,
2002
.
[51]
A.A
lkha
teeb
,G.L
eus,
and
R.W
.Hea
thJr
.,“C
ompr
esse
dse
nsin
gba
sed
mul
ti-us
erm
illim
eter
wav
esy
stem
s:H
owm
any
mea
sure
men
tsar
ene
eded
?”in
Proc
.IEE
EIn
t.C
onf.
Acou
st.,
Spee
chSi
gnal
Proc
ess.
(IC
ASSP
),A
pril
2015
,pp.
2909
–291
3.
[52]
M.
L.B
ench
eikh
,Y.
Wan
g,an
dH
.H
e,“P
olyn
omia
lro
otfin
ding
tech
niqu
efo
rjo
int
DO
AD
OD
estim
atio
nin
bist
atic
MIM
Ora
dar,”
Sign
alPr
oces
s.,vo
l.90
,no.
9,pp
.272
3–27
30,2
010.
[53]
M.B
engt
sson
and
B.O
tters
ten,
“Low
-com
plex
ityes
timat
ors
ford
istri
bute
dso
urce
s,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.48
,
no.8
,pp.
2185
–219
4,20
00.
[54]
R.
Key
s,“C
ubic
conv
olut
ion
inte
rpol
atio
nfo
rdi
gita
lim
age
proc
essi
ng,”
IEEE
Tran
s.Ac
oust
.,Sp
eech
,Si
gnal
Proc
ess.,
vol.
29,n
o.6,
pp.1
153–
1160
,198
1.
[55]
Z.G
ao,L
.Dai
,Z.W
ang,
and
S.C
hen,
“Spa
tially
com
mon
spar
sity
base
dad
aptiv
ech
anne
les
timat
ion
and
feed
back
for
FDD
mas
sive
MIM
O,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
63,n
o.23
,pp.
6169
–618
3,20
15.
[56]
M.M
isha
lian
dY.
C.E
ldar
,“R
educ
ean
dbo
ost:
Rec
over
ing
arbi
trary
sets
ofjo
intly
spar
seve
ctor
s,”IE
EETr
ans.
Sign
al
Proc
ess.,
vol.
56,n
o.10
,pp.
4692
–470
2,20
08.
[57]
I.F.
Gor
odni
tsky
and
B.D
.Rao
,“Sp
arse
sign
alre
cons
truct
ion
from
limite
dda
taus
ing
FOC
USS
:Are
-wei
ghte
dm
inim
um
norm
algo
rithm
,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.45
,no.
3,pp
.600
–616
,199
7.
Mm
Wav
eSy
stem
Sub-
6GHz
Syst
em
RF
Cha
in
DA
C
FOR
SUB
MIS
SIO
NTO
IEEE
30
[40]
R.J
.Wei
ler,
M.P
eter
,T.K
hne,
M.W
isot
zki,
and
W.K
eusg
en,“
Sim
ulta
neou
smill
imet
er-w
ave
mul
ti-ba
ndch
anne
lsou
ndin
g
inan
urba
nac
cess
scen
ario
,”in
Proc
.Eur
.Con
f.An
tenn
asPr
opag
.(Eu
CAP
),M
ay20
15,p
p.1–
5.
[41]
A.S
.Poo
nan
dM
.Ho,
“Ind
oor
mul
tiple
-ant
enna
chan
nel
char
acte
rizat
ion
from
2to
8G
Hz.
”in
Proc
.IEE
EIn
t.C
onf.
Com
mun
.(IC
C),
2003
,pp.
3519
–352
3.
[42]
S.Ja
ecke
l,M
.Pet
er,K
.Sak
aguc
hi,W
.Keu
sgen
,and
J.M
edbo
,“5G
Cha
nnel
Mod
els
inm
m-W
ave
Freq
uenc
yB
ands
,”in
Proc
.Eur
.Wire
less
Con
f.,M
ay20
16,p
p.1–
6.
[43]
A.O
.Kay
a,D
.Cal
in,a
ndH
.Vis
wan
atha
n.(2
016)
28G
Hz
and
3.5
GH
zW
irele
ssC
hann
els:
Fadi
ng,D
elay
and
Ang
ular
Dis
pers
ion.
[44]
R.C
.Qiu
and
I.-T.
Lu,“
Mul
tipat
hre
solv
ing
with
freq
uenc
yde
pend
ence
forw
ide-
band
wire
less
chan
nelm
odel
ing,
”IE
EE
Tran
s.Ve
h.Te
chno
l.,vo
l.48
,no.
1,pp
.273
–285
,199
9.
[45]
K.H
aned
a,A
.Ric
hter
,and
A.F
.Mol
isch
,“M
odel
ing
the
freq
uenc
yde
pend
ence
oful
tra-w
ideb
and
spat
io-te
mpo
rali
ndoo
r
radi
och
anne
ls,”
IEEE
Tran
s.An
tenn
asPr
opag
.,vo
l.60
,no.
6,pp
.294
0–29
50,2
012.
[46]
D.D
uple
ich,
R.S
.Tho
m,G
.Ste
inb,
J.Lu
o,E.
Schu
lz,X
.Lu,
G.W
ang
etal
.,“S
imul
tane
ous
mul
ti-ba
ndch
anne
lsou
ndin
g
atm
m-W
ave
freq
uenc
ies,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[47]
P.K
y,I.
Car
ton,
A.K
arst
ense
n,W
.Fan
,G.F
.Ped
erse
net
al.,
“Fre
quen
cyde
pend
ency
ofch
anne
lpa
ram
eter
sin
urba
n
LOS
scen
ario
for
mm
wav
eco
mm
unic
atio
ns,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2016
,pp.
1–5.
[48]
K.
Han
eda,
J.-i.
Taka
da,
and
T.K
obay
ashi
,“E
xper
imen
tal
Inve
stig
atio
nof
Freq
uenc
yD
epen
denc
ein
Spat
io-T
empo
ral
Prop
agat
ion
Beh
avio
ur,”
inPr
oc.E
ur.C
onf.
Ante
nnas
Prop
ag.(
EuC
AP),
2007
,pp.
1–6.
[49]
V.N
urm
ela
etal
.,“M
ETIS
Cha
nnel
Mod
els,”
Mob
ilean
dw
irele
ssco
mm
unic
atio
nsEn
able
rsfo
rth
eTw
enty
-twen
ty
Info
rmat
ion
Soci
ety,
Tech
.Rep
.,20
15.
[50]
A.M
.Say
eed,
“Dec
onst
ruct
ing
mul
tiant
enna
fadi
ngch
anne
ls,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
50,n
o.10
,pp.
2563
–257
9,
2002
.
[51]
A.A
lkha
teeb
,G.L
eus,
and
R.W
.Hea
thJr
.,“C
ompr
esse
dse
nsin
gba
sed
mul
ti-us
erm
illim
eter
wav
esy
stem
s:H
owm
any
mea
sure
men
tsar
ene
eded
?”in
Proc
.IEE
EIn
t.C
onf.
Acou
st.,
Spee
chSi
gnal
Proc
ess.
(IC
ASSP
),A
pril
2015
,pp.
2909
–291
3.
[52]
M.
L.B
ench
eikh
,Y.
Wan
g,an
dH
.H
e,“P
olyn
omia
lro
otfin
ding
tech
niqu
efo
rjo
int
DO
AD
OD
estim
atio
nin
bist
atic
MIM
Ora
dar,”
Sign
alPr
oces
s.,vo
l.90
,no.
9,pp
.272
3–27
30,2
010.
[53]
M.B
engt
sson
and
B.O
tters
ten,
“Low
-com
plex
ityes
timat
ors
ford
istri
bute
dso
urce
s,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.48
,
no.8
,pp.
2185
–219
4,20
00.
[54]
R.
Key
s,“C
ubic
conv
olut
ion
inte
rpol
atio
nfo
rdi
gita
lim
age
proc
essi
ng,”
IEEE
Tran
s.Ac
oust
.,Sp
eech
,Si
gnal
Proc
ess.,
vol.
29,n
o.6,
pp.1
153–
1160
,198
1.
[55]
Z.G
ao,L
.Dai
,Z.W
ang,
and
S.C
hen,
“Spa
tially
com
mon
spar
sity
base
dad
aptiv
ech
anne
lest
imat
ion
and
feed
back
for
FDD
mas
sive
MIM
O,”
IEEE
Tran
s.Si
gnal
Proc
ess.,
vol.
63,n
o.23
,pp.
6169
–618
3,20
15.
[56]
M.M
isha
lian
dY.
C.E
ldar
,“R
educ
ean
dbo
ost:
Rec
over
ing
arbi
trary
sets
ofjo
intly
spar
seve
ctor
s,”IE
EETr
ans.
Sign
al
Proc
ess.,
vol.
56,n
o.10
,pp.
4692
–470
2,20
08.
[57]
I.F.
Gor
odni
tsky
and
B.D
.Rao
,“Sp
arse
sign
alre
cons
truct
ion
from
limite
dda
taus
ing
FOC
USS
:Are
-wei
ghte
dm
inim
um
norm
algo
rithm
,”IE
EETr
ans.
Sign
alPr
oces
s.,vo
l.45
,no.
3,pp
.600
–616
,199
7.
Mm
Wav
eSy
stem
Sub-
6GHz
Syst
em
RF
Cha
in
DA
C
FOR
SUB
MIS
SIO
NTO
IEEE
30
[40]
R.J
.Wei
ler,
M.P
eter
,T.K
hne,
M.W
isot
zki,
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eusg
en,“
Sim
ulta
neou
smill
imet
er-w
ave
mul
ti-ba
ndch
anne
lsou
ndin
g
inan
urba
nac
cess
scen
ario
,”in
Proc
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f.An
tenn
asPr
opag
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CAP
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ay20
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onan
dM
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o,“I
ndoo
rm
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le-a
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arac
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atio
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om2
to8
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edbo
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Cha
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Mod
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inm
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ave
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n.(2
016)
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esse
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sed
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erm
illim
eter
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esy
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owm
any
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ityes
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ors
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istri
bute
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urce
s,”IE
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mas
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alre
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dda
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USS
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ghte
dm
inim
um
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,”IE
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ans.
Sign
alPr
oces
s.,vo
l.45
,no.
3,pp
.600
–616
,199
7.
Mm
Wav
eSy
stem
Sub-
6GHz
Syst
em
RF
Cha
in
DA
C
FOR SUBMISSION TO IEEE 32
[51] M. L. Bencheikh, Y. Wang, and H. He, “Polynomial root finding technique for joint DOA DOD estimation in bistatic
MIMO radar,” Signal Process., vol. 90, no. 9, pp. 2723–2730, Sep. 2010.
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no. 8, pp. 2185–2194, Aug. 2000.
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vol. 29, no. 6, pp. 1153–1160, Dec. 1981.
[54] Z. Gao, L. Dai, Z. Wang, and S. Chen, “Spatially common sparsity based adaptive channel estimation and feedback for
FDD massive MIMO,” IEEE Trans. Signal Process., vol. 63, no. 23, pp. 6169–6183, Dec. 2015.
[55] M. Mishali and Y. C. Eldar, “Reduce and boost: Recovering arbitrary sets of jointly sparse vectors,” IEEE Trans. Signal
Process., vol. 56, no. 10, pp. 4692–4702, Oct. 2008.
[56] I. F. Gorodnitsky and B. D. Rao, “Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum
norm algorithm,” IEEE Trans. Signal Process., vol. 45, no. 3, pp. 600–616, Mar. 1997.
[57] “IEEE standard for information technology–Telecommunications and information exchange between systems–Local and
metropolitan area networks–Specific requirements-Part 11: Wireless LAN medium access control (MAC) and physical
layer (PHY) specifications amendment 3: Enhancements for very high throughput in the 60 GHz band,” pp. 1–628, Dec.
2012.
[58] M. Grant, S. Boyd, and Y. Ye, “CVX: Matlab software for disciplined convex programming,” 2008. [Online]. Available:
http://www.stanford.edu/ boyd/cvx
Transmitter Receiver
FOR SUBMISSION TO IEEE 32
[51] M. L. Bencheikh, Y. Wang, and H. He, “Polynomial root finding technique for joint DOA DOD estimation in bistatic
MIMO radar,” Signal Process., vol. 90, no. 9, pp. 2723–2730, Sep. 2010.
[52] M. Bengtsson and B. Ottersten, “Low-complexity estimators for distributed sources,” IEEE Trans. Signal Process., vol. 48,
no. 8, pp. 2185–2194, Aug. 2000.
[53] R. Keys, “Cubic convolution interpolation for digital image processing,” IEEE Trans. Acoust., Speech, Signal Process.,
vol. 29, no. 6, pp. 1153–1160, Dec. 1981.
[54] Z. Gao, L. Dai, Z. Wang, and S. Chen, “Spatially common sparsity based adaptive channel estimation and feedback for
FDD massive MIMO,” IEEE Trans. Signal Process., vol. 63, no. 23, pp. 6169–6183, Dec. 2015.
[55] M. Mishali and Y. C. Eldar, “Reduce and boost: Recovering arbitrary sets of jointly sparse vectors,” IEEE Trans. Signal
Process., vol. 56, no. 10, pp. 4692–4702, Oct. 2008.
[56] I. F. Gorodnitsky and B. D. Rao, “Sparse signal reconstruction from limited data using FOCUSS: A re-weighted minimum
norm algorithm,” IEEE Trans. Signal Process., vol. 45, no. 3, pp. 600–616, Mar. 1997.
[57] “IEEE standard for information technology–Telecommunications and information exchange between systems–Local and
metropolitan area networks–Specific requirements-Part 11: Wireless LAN medium access control (MAC) and physical
layer (PHY) specifications amendment 3: Enhancements for very high throughput in the 60 GHz band,” pp. 1–628, Dec.
2012.
[58] M. Grant, S. Boyd, and Y. Ye, “CVX: Matlab software for disciplined convex programming,” 2008. [Online]. Available:
http://www.stanford.edu/ boyd/cvx
Transmitter Receiver
Fig. 1: The multi-band MIMO system with co-located sub-6 GHz and mmWave antenna arrays.The sub-6 GHz channel is H and the mmWave channel is H.
A. Sub-6 GHz system and channel model
The sub-6 GHz system is shown in Fig. 2. Note that, we underline all sub-6 GHz variables
to distinguish them from the mmWave variables. The sub-6 GHz system has one RF chain per
antenna and as such, fully digital precoding is possible. We assume narrowband signaling at sub-
6 GHz. Extending the proposed approach to wideband sub-6 GHz systems is straight forward
because only the directional information is retrieved from sub-6 GHz, which is not expected
to vary much across the channel bandwidth. We adopt a geometric channel model for H based
on (1). The MIMO channel matrix for sub-6 GHz can be written as
H =
sMRXMTX
⇢pl
CX
c=1
RcX
rc=1
↵rcp(�⌧ c � ⌧ rc)aRX(✓c + #rc)a⇤TX(�c
+ 'rc), (3)
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
18
Disruptive Communication Technologies – Channel Sparsity
• Estimating the mmWave channel is equivalent to estimating the parameters of the channel paths, i.e., the AoA, the AoD, and the gain of each path.
• IDEA: exploit the poor scattering nature of the mmWave channel to formulate the mmWave channel estimation problem as a sparse compressed sensing problem: the channel power is concentrated in a few entries of a virtual channel matrix
• It is sufficient to estimate the AoAs and AoDsof the dominant paths to be resolved.
6
(a) Grayscale of angular-delay domain (b) Surface plot of angular-delay domain
Fig. 4. An illustration of a 6-path SFW channel, where M = 128, N = 128, d/�c = 0.5, fs/fc = 0.2, and SNR = 10dB.
D. Sparse Channel Representation and Angular-Delay Or-
thogonality
Define the vectorizing SFW channel hp , vec(Hp) as
hp =
Lp�1X
l=0
↵p,l
⇥vec (⇥( p,l)) � vec
�a( p,l)b
T (⌧p,l)�⇤
=
Lp�1X
l=0
↵p,ldiag(vec(⇥( p,l))) (b(⌧p,l)⌦ a( p,l))
,Lp�1X
l=0
↵p,lp( p,l, ⌧p,l) 2 CMN⇥1, (18)
where p( p,l, ⌧p,l) , diag (vec (⇥( p,l))) (b(⌧p,l)⌦ a( p,l))is the corresponding item and serves as the basis vector forspanning hp. The following theorem proved in Appendix Cindicates the orthogonality of the basis vectors of hp.
Theorem 2: When M ! 1, N ! 1, the followingproperty holds
limM,N!1
1
MNp( 1, ⌧1)
Hp( 2, ⌧2) =
(1 1 = 2, ⌧1 = ⌧2
0 otherwise.
(19)
Based on Theorem 2, if any two users do not share thesame path (paths with both the same DOA and the same timedelay), then their vectorized SFW channels are asymptoticallyorthogonal. This phenomenon is called the angular-delay
orthogonality, which indicates the orthogonality for users atdifferent locations, or for users at the same location but withdifferent path delays. Interestingly, as shown in Fig. 4, twosquares in Fig. 4(a) corresponding to two different paths maypartially overlap with each other, which, however, does notbreak the angular-delay orthogonality in terms of Theorem 2unless the two squares locate at the exact identical position.
An important application of Theorem 2 is in channel esti-mation and user scheduling. By exploiting the angular-delayorthogonality, orthogonal users in angular-delay domain can
be simultaneously scheduled without pilot contamination ormutual interference, as demonstrated in the next section.
IV. CHANNEL ESTIMATION FOR DUAL-WIDEBANDMMWAVE MASSIVE MIMO SYSTEMS
The above discussion has demonstrated that the SFWchannel model of the massive MIMO system is not just anextension of the traditional MIMO channel model. This crit-ical difference demands for redesign of most communicationstrategies, such as channel estimation, signal detection, beam-forming, precoding, user scheduling, etc. Due to the spacelimitation, we present a simple channel estimation algorithmin this section to deal with the dual-wideband effects by theaid of array signal processing.
A. Preamble for Initial Uplink Channel Estimation
In the preamble phase, we apply the conventional leastsquare MIMO-OFDM channel estimation algorithm [39] foreach antenna at the BS. Denote bHp as the uplink preamblechannel between the pth user and the BS. The main purposeof the preamble is to obtain the initial DOA and the timedelay of each path for each user and facilitate the subsequentuplink and downlink channel estimations with a small numberof pilot resources.
B. Extracting Angular-Delay Signature
From Theorem 1, #p,l, ⌧p,l, and Lp can be immediatelyobtained from the non-zero square of bGp = F
H
MbHpF
⇤N
.However, when M and N are finite in practice, the regionof the non-zero square will be expanded due to the powerleakage effect [15], [31]. Hence, #p,l, ⌧p,l, and Lp should beobtained by a more sophisticatedly designed way.
Denote
M (� p,l) = diag⇣1, ej� p,l , . . . , ej(M�1)� p,l
⌘(20)
Example of 6 dominant paths (the location of each square reflects the AoA and time delay of each path)
B. Wang, et al., "Spatial- and Frequency-Wideband Effects in Millimeter-Wave Massive MIMO Systems," in IEEE Trans. Sig. Proc., vol. 66, no. 13, pp. 3393-3406, Jul. 2018.
Fare clic per modificare stile
6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
19
Innovative Network Architectures – Disaggregation/virtualization
• IDEA: decouple network/control plane (CP) and forwarding/user plane (UP).Ø SDN offers network programmability and centralization of the control
Ø SDN is agile and responsive (traffic flow meets fluctuating needs and demands).
Ø SDN is standards-based (e.g., OpenFlow) and vendor-neutral.
• IDEA: replace network services provided by dedicated hardware (e.g., network switches) with virtualized software.
Ø NFV saves capital and operating expenses
• C. J. Bernardos et al., "An architecture for software defined wireless networking," in IEEE Wireless Communications, vol. 21, no. 3, pp. 52-61, June 2014.• R. Mijumbi, et al., "Network Function Virtualization: State-of-the-Art and Research Challenges," in IEEE COMST, vol. 18, no. 1, pp. 236-262, 2016
NFV and SDN are complementary technologies(SDN executes on an NFV infrastructure)
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
20
Innovative Network Architectures – Access/Backhaul integration
• Massive 6G data rates technologies àadequate growth of the backhaul capacity
• THz and VLC deployments will call for a massive increase in the density of access points, which should be provided with backhaul connectivity to the core network à expensive
• IDEA: deploy a fraction of BSs with traditional fiber-like backhaul capabilities and the rest of the BSs connecting to the fiber infrastructures wirelessly.
• 6G deployments will introduce new challenges and opportunities• The networks will need higher autonomous configuration capabilities• Out-of-band IAB can be realized to increase the overall network throughput.
• M. Polese, et al. , "End- to-End Simulation of Integrated Access and Backahul at mmWaves,” to appear on IEEE CAMAD, Sep. 2018.• M. Polese, et al., "Distributed Path Selection Strategies for Integrated Access and Backhaul at mmWaves", to appear on IEEE GLOBECOM, Dec. 2018
Donor gNB
IAB nodeIAB node
MACRLC
PDCP
MACRLC
PDCP
Backhaul Access
SCHED
IAB node stack
PHY PHY
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6G Technologies and Innovations
21
Integrating Intelligence in the Network – Learning
• BACKGROUND: the signal received at multiple BSs renders a defining signature for the user location and its interaction with the surrounding environment.
• BACKGROUND: UEs typically move through predefined paths, and some movements are impossible due to the presence of obstacles, e.g., buildings, walls.
• IDEA: account for previous access statistics and use machine learning tools to predict the network behaviors (e.g., by remembering/observing consequences of previous decisions).
SUPERVISED LEARNINGThe amount of data generated will be massive, thus labelingthe data may be infeasible. UNSUPERVISED LEARNING
Does not need labeling, used to autonomously build complex
network representations
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Technologies and Innovations
22
Integrating Intelligence in the Network – Knowledge sharing and learning
• 5G: At high frequency, the massive bandwidth and spatial degrees of freedom are unlikely to be fully used by any one cellular operator. Spectrum can be shared, in time and in space, with several performance and energy benefits:§ Reducing deployment costs, if operators share bands and infrastructures
§ Inter-operators access and interference coordination
• 6G: operators and users may also be interested in sharing learned representations of specific network deployments and/or use cases§ Speed up the network configuration in new markets
§ Better adapt to new unexpected scenarios which may emerge during network operations
Controllers
Reference Planner
Mission Planning
Behavioral Planner
Perception
Lane Centering
ACC Controller
Merge Assist
Fig. 3: The proposed behavioral planning framework
III. BEHAVIORAL PLANNING FRAMEWORK
As discussed in Section II, there are shortcomings ofboth the current hierarchical and parallel robot decisionmaking architectures. Due to real-time constraints, the mo-tion planner cannot consider the effects of imperfect vehi-cle controllers or cooperation between cars. In this paper,we propose a novel behavioral planning framework thatcombines the strengths of the hierarchical and parallel ar-chitectures. It is based on the hierarchical architecture sothat fully autonomous driving with high-level intelligencecan be achieved. However, it also uses the independentcontrollers as in the parallel autonomous vehicle architectureto ensure basic performance and driving quality. The designgreatly reduces the necessary search space for the motionplanner without sacrificing any performance due to coarsergranularity. The proposed framework is shown in Figure 3.
A. Mission PlanningThe mission planning module takes charge of decompos-
ing driving missions such as “go from point A to point B”into lane-level sub-missions such as which lane we should bein, whether we need to stop at an intersection, etc. It takes ahuman drivers desired destination and computes the shortestpath to it from the robots current position. It outputs a setof future lane-level sub-missions which describe the desiredlane and turn of the vehicle at each intersection. In addition,this module also controls the transition of goals. When thevehicle completes the current lane-level sub-mission, it willautomatically send the next set of goals to the lower layers.When there are intersections with stop signs, traffic lightsor yielding requirements, this module directly talks to theperception system to decide whether the car can proceed tothe next sub-mission.
B. Traffic-free Reference PlanningThe reference planning layer takes the lane-level sub-
missions, and outputs a path and speed profile for theautonomous vehicle to drive [5]. In this layer, the plannerassumes there is no traffic on the road. It uses non-linearoptimization to find a smooth and human-like path and speedwith consideration of the kinematics and dynamics of theautonomous vehicle, as well as the geometry of the road.
Fig. 4: Reference planners result: the desired path cut cornersat the turn to minimize path length and generate betterhandling
Candidate Strategy Generation
Prediction Engine
Cost function-based Evaluation
Best Strategy
Multiple candidate strategies
Predicted surroundingtraffic scenarios
Progress Comfort SafetyFuel
Consum-ption
Fig. 5: The block diagram of Prediction- and Cost-functionBased algorithm (PCB)
For example, if the road is not straight, as shown in Figure4, instead of driving exactly in the center of the road, ahuman driver will drive slightly offset from the center line tominimize the required steering maneuver, which is emulatedin this module. In addition, traffic rules such as speed limitsare applied in this layer.
C. Behavioral Planning
Since the road geometry has been considered in theprevious layer, the behavior planner focuses on handling on-road traffic, including moving obstacles and static objects.It takes the traffic-free reference, moving obstacles andall road blockages as input. It outputs controller directivesincluding lateral driving bias, the desired leading vehiclewe should follow, the aggressiveness of distance keeping,and maximum speed. In this module, a Prediction- andCost-function Based (PCB) algorithm is implemented [17].There are three primary steps: candidate strategy generation,prediction, and cost function-based evaluation, as shown inFigure 5.
In the PCB algorithm, the world is abstracted as shownin Equation 1. V Shost is the state vector for the hostvehicle and surrounding vehicles, with station (longitudinaldistance along the reference path), velocity, acceleration andlateral offset information. V Sother is the state vector foreach surrounding vehicle. Compared with V Shost, it hasadditional information about the intention and probability ofintention. Sroad is the state vector of the reference path withmaximum speed information at each of M stations. SPCB
is the state vector for the behavioral planner.
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6G: Towards a Fully Digital and Connected World 6G Wireless Summit, 26th March 2019
6G Challenges
23
5
Disaggregated and virtualized RANThe networking equipment will not require dedicated hardware
Cell-less architectureThe UE connects to the RAN and not to a single cell
Extreme multi-connectivityExploit THz, VLC, mmWave and sub-6 GHz links
Efficient and low-power network operationsEnergy will be at the core of 6G protocols design
Generic hardware
Virtual MAC
Virtual PHY
EdgeCloud
Ener
gy h
arve
stin
g
Low
-pow
er n
odes
Fig. 4: Architectural innovations introduced in 6G networks.
ping has been widely studied [15], but such capabilitieshave never been deeply integrated with the operations andprotocols of cellular networks. 6G networks will exploita unified interface for localization and communicationsto (i) improve control operations, which can rely oncontext information (without the need for exchangingGPS coordinates with the user) to control beamformingpatterns, reduce interference, predict handovers; and (ii)offer innovative user services, e.g., for vehicular andeHealth applications.
B. Innovative Network Architectures
The disruption brought by the communication technologiesdescribed in Sec. III-A will require new 6G network architec-ture, but also potentially require structural updates with respectto current mobile network designs. For example, the densityand the high access data rate of terahertz communications willcreate constraints on the underlying transport network, whichhas to provide both more points of access to fiber and a highercapacity than today’s backhaul networks.
The main architectural innovations that 6G will introduceare described in Fig. 4. In this context, we envision theintroduction and/or deployment of the following architecturalparadigms:
• Cell-less architecture and tight integration of multiple
frequencies and communication technologies. 6G willbreak the current boundaries of cells, with UEs connectedto the network as a whole and not to a single cell.This will guarantee a seamless mobility support, withoutoverhead due to handovers (which might be frequentwhen considering systems at terahertz frequencies), andwill provide Quality of Service (QoS) guarantees even inchallenging mobility scenarios such as vehicular ones.
The overcoming of the cell concept will also enablea tight integration of the different 6G communicationtechnologies. The users will be able to seamlessly tran-sition among sub-6 GHz, mmWave, terahertz or VLClinks without manual interventions or configurations inthe device, which will automatically select the best avail-able communication technology. Finally, according to thespecific use case, the UE may also concurrently use dif-ferent network interfaces to exploit their complementarycharacteristics, e.g., the sub-6 GHz layer for control, andterahertz link for the data plane.
• Disaggregation and virtualization of the networking
equipment: from the physical layer to NFV. Networkshave recently started to transition towards the disaggre-gation of once-monolithic networking equipments: forexample, 5G networks base stations can be deployedwith distributed units with the lower layer of the protocolstack, and centralized units in data centers at the edge.Following this direction, 6G networks will adopt an evenmore disruptive architecture, where the units deployedon the ground will contain just the physical antennas andthe lowest amount of processing units possible. Moreover,virtualization will be brought to the extreme, thanks tothe advances in the capabilities of general purpose pro-cessors: 6G will virtualize additional components, suchas those related to the Medium Access Control (MAC)and Physical (PHY) layers, which currently require ded-icated hardware implementations. The virtualization willdecrease the costs of networking equipment, making amassively dense deployment economically feasible.
• Advanced access-backhaul integration. The massivedata rates provided by the new 6G access technologieswill require an adequate growth of the backhaul capacity.
• Circuit design, high propagation loss • Limited coverage, need for RF uplink• Need for reliable frequency mapping
• Scheduling, need for new network design • Scalability, and interference management
• Slower network operations/security concerns• CU/UP functions are tightly coupled
• Integrate energy characteristics in protocols • Energy vs. high-deployment / MIMO
6G: Towards a Fully Digital and Connected World
6G Wireless Summit, Levi, FinlandMarch 26th, 2019
Marco Giordani◦, Michele Polese◦, Marco Mezzavilla†, Sundeep Rangan†, Michele Zorzi◦◦University of Padova, Department of Information Engineering, Italy
† NYU WIRELESS, Tandon School of Engineering, New York University, Brooklyn, NY, USA