Principles of Millimeter Wave Communications for V2X Stefano Buzzi - - PowerPoint PPT Presentation

Principles of Millimeter Wave Communications for V2X

Stefano Buzzi

University of Cassino and Southern Lazio, Cassino, Italy

London, June 11th, 2018

About myself and the University of Cassino...

• Associate Professor at the University
• f Cassino and Southern Latium
• 20 years of experience in academic

teaching and research

• Currently working on 5G systems

About myself and the University of Cassino...

• Associate Professor at the University
• f Cassino and Southern Latium
• 20 years of experience in academic

teaching and research

• Currently working on 5G systems

University of Cassino...

• About 10K students, 350 Faculty,

500+ researchers

• Engineering, Economics, Laws,

Humanities

• M.Sc. in Telecommunications

Engineering (taught in English)

V2X Communications

• Vehicle-to-everything (V2X) communications refer to the

communication among vehicles, and among vehicles and any entity that may be interacting with the vehicle:

V2X Communications

• Vehicle-to-everything (V2X) communications refer to the

communication among vehicles, and among vehicles and any entity that may be interacting with the vehicle:

• V2I: Vehicle-to-Infrastructure
• V2V: Vehicle-to-Vehicle
• V2P: Vehicle-to-Pedestrian
• V2D: Vehicle-to-Device
• V2G: Vehicle-to-Grid

V2X Communications

• Vehicle-to-everything (V2X) communications refer to the

communication among vehicles, and among vehicles and any entity that may be interacting with the vehicle:

• V2I: Vehicle-to-Infrastructure
• V2V: Vehicle-to-Vehicle
• V2P: Vehicle-to-Pedestrian
• V2D: Vehicle-to-Device
• V2G: Vehicle-to-Grid
• V2X has been around for a while, so is older than 5G
• IEEE 802.11p dates back to 2010, and uses 10MHz bandwidth at 5.9 GHz
• Currently many cars equipped with LTE transceivers

V2X Communications

• Vehicle-to-everything (V2X) communications refer to the

communication among vehicles, and among vehicles and any entity that may be interacting with the vehicle:

• V2I: Vehicle-to-Infrastructure
• V2V: Vehicle-to-Vehicle
• V2P: Vehicle-to-Pedestrian
• V2D: Vehicle-to-Device
• V2G: Vehicle-to-Grid
• V2X has been around for a while, so is older than 5G
• IEEE 802.11p dates back to 2010, and uses 10MHz bandwidth at 5.9 GHz
• Currently many cars equipped with LTE transceivers
• V2X will be a key (if not killer...) application of 5G networks

V2X Use cases

Some V2X use cases include

• Forward collision warning
• General warnings (traffic jam ahead, pedestrians ahead, etc...)
• Infrastructure-assisted driving
• Platooning
• Autonomous driving
• In-car entertainment

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

• However, things are lately changing....

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

• However, things are lately changing....
• Connected cars will send 25GB of data to the cloud every hour - that is

55Mbit/s!!

• A four-lane highway in normal conditions will require an aggregate

throughput of tens of Gbit/s per kilometer

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

• However, things are lately changing....
• Connected cars will send 25GB of data to the cloud every hour - that is

55Mbit/s!!

• A four-lane highway in normal conditions will require an aggregate

throughput of tens of Gbit/s per kilometer

• On top of that, we could want to provide in-car entertainment to passengers

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

• However, things are lately changing....
• Connected cars will send 25GB of data to the cloud every hour - that is

55Mbit/s!!

• A four-lane highway in normal conditions will require an aggregate

throughput of tens of Gbit/s per kilometer

• On top of that, we could want to provide in-car entertainment to passengers
• For providing these services, mmWave carrier frequencies are needed!

Millimeter Wave and V2X

• For obvious reasons tied to reliability and coverage, sub-6 GHz frequencies

have been the by default choice for V2X applications

• However, things are lately changing....
• Connected cars will send 25GB of data to the cloud every hour - that is

55Mbit/s!!

• A four-lane highway in normal conditions will require an aggregate

throughput of tens of Gbit/s per kilometer

• On top of that, we could want to provide in-car entertainment to passengers
• For providing these services, mmWave carrier frequencies are needed!
• The research community is already tackling this challenge (e.g.

5G-MiEdge, 5GCAR, plus privately-funded research)

Millimeter Waves (mmWaves)

One of the ”key pillars” of 5G networks Refers to above-6Ghz frequencies Regulators worldwide are releasing spectrum chunks at frequencies up to 100GHz The main benefit here is the availability of large bandwidths

Millimeter Waves (mmWaves)

One of the ”key pillars” of 5G networks Refers to above-6Ghz frequencies Regulators worldwide are releasing spectrum chunks at frequencies up to 100GHz The main benefit here is the availability of large bandwidths However, there are some key challenges that are to be faced to realize effective wireless communications with mmWave frequencies

The Propagation Challenge

• Friis’ Law: PR = PTGTGR

λ 4πd 2

The Propagation Challenge

• Friis’ Law: PR = PTGTGR

λ 4πd 2

• We may have heavy shadowing losses:

brick, concrete > 150 dB Human body: Up to 35 dB

The Propagation Challenge

• Friis’ Law: PR = PTGTGR

λ 4πd 2

• We may have heavy shadowing losses:

brick, concrete > 150 dB Human body: Up to 35 dB

NLOS propagation mainly relies on reflections There are heavy blockage effects

Increased atmospheric absorption

Small-sized arrays help! However...

Small-sized arrays help! However...

• For a constant physical area, GT and GR ∝ λ−2
• Otherwise stated, the number of antennas that can be packed in a given

area increases quadratically with the frequency

Small-sized arrays help! However...

• For a constant physical area, GT and GR ∝ λ−2
• Otherwise stated, the number of antennas that can be packed in a given

area increases quadratically with the frequency

• The free-space path loss is well-compensated by the antenna gains =

⇒ mmWaves must be used in conjunction with MIMO

The case for doubly massive MIMO at mmWaves

• At fc = 30GHz, the wavelength λ = 1cm
• Assuming λ/2 spacing, ideally, more than 180 antennas can be placed in

an area as large as a credit card

The case for doubly massive MIMO at mmWaves

• At fc = 30GHz, the wavelength λ = 1cm
• Assuming λ/2 spacing, ideally, more than 180 antennas can be placed in

an area as large as a credit card The number climbs up to 1300 at 80GHz!!

The case for doubly massive MIMO at mmWaves

• At fc = 30GHz, the wavelength λ = 1cm
• Assuming λ/2 spacing, ideally, more than 180 antennas can be placed in

an area as large as a credit card The number climbs up to 1300 at 80GHz!! Although clearly not feasible in today’s mobile phones, doubly massive MIMO systems are a perfect match for V2X communications

Other challenges/difficulties

• The MIMO channel at mmWaves is not so generous as in sub-6GHz bands

Other challenges/difficulties

• The MIMO channel at mmWaves is not so generous as in sub-6GHz bands
• ADC/DAC bottleneck: forget all-digital beamforming and use alternative

solutions: (hybrid analog/digital beamformers, lens antenna arrays, single-RF chain architectures, etc.)

Other challenges/difficulties

• The MIMO channel at mmWaves is not so generous as in sub-6GHz bands
• ADC/DAC bottleneck: forget all-digital beamforming and use alternative

solutions: (hybrid analog/digital beamformers, lens antenna arrays, single-RF chain architectures, etc.)

• Power consumption issues (not so relevant for V2X)

Other challenges/difficulties

• The MIMO channel at mmWaves is not so generous as in sub-6GHz bands
• ADC/DAC bottleneck: forget all-digital beamforming and use alternative

solutions: (hybrid analog/digital beamformers, lens antenna arrays, single-RF chain architectures, etc.)

• Power consumption issues (not so relevant for V2X)
• Low efficiency of power amplifiers (moderately relevant for V2X)

Other challenges/difficulties

• The MIMO channel at mmWaves is not so generous as in sub-6GHz bands
• ADC/DAC bottleneck: forget all-digital beamforming and use alternative

solutions: (hybrid analog/digital beamformers, lens antenna arrays, single-RF chain architectures, etc.)

• Power consumption issues (not so relevant for V2X)
• Low efficiency of power amplifiers (moderately relevant for V2X)
• Need for efficient beam-alignment and tracking (positioning may help...)

Lecture Outline

We now focus on:

Lecture Outline

We now focus on: The MIMO channel at mmWaves

Lecture Outline

We now focus on: The MIMO channel at mmWaves Hybrid (analog/digital) beamforming architectures

Lecture Outline

We now focus on: The MIMO channel at mmWaves Hybrid (analog/digital) beamforming architectures (Briefs on) Cellular networking for V2X

The clustered channel model

• The rich scattering environment assumption typically assumed for sub-6

GHz does not hold at mmWaves. The following no longer holds:

Channel matrix with i.i.d. entries Channel matrix with full rank with probability 1

At mmWaves, a “clustered” channel model is more representative of the physical propagation mechanism

Ncl scattering clusters Each cluster contributes with Nray propagation paths

The clustered channel model has an implication on the maximum rank of the channel matrix

The clustered channel model

The clustered channel model

Just a sample of recent papers - by different set of authors - that have embraced the clustered channel model:

References [1]

• O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter

wave MIMO systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499–1513,

• Mar. 2014

[2]

• A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding for

millimeter wave cellular systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5,

• pp. 831–846, 2014

[3]

• S. Haghighatshoar and G. Caire, “Enhancing the estimation of mm-Wave large array channels by exploit-

ing spatio-temporal correlation and sparse scattering,” in Proc. of 20th International ITG Workshop on Smart Antennas (WSA 2016), 2016 [4]

• T. E. Bogale and L. B. Le, “Beamforming for multiuser massive MIMO systems: Digital versus hybrid

analog-digital,” in 2014 IEEE Global Communications Conference (GLOBECOM). IEEE, 2014, pp. 4066–4071 [5]

• L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,”

IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, 2014 [6]

• J. Lee, G.-T. Gil, and Y. H. Lee, “Exploiting spatial sparsity for estimating channels of hybrid MIMO

systems in millimeter wave communications,” in 2014 IEEE Global Communications Conference (GLOBE- COM). IEEE, 2014, pp. 3326–3331 [7] C.-E. Chen, “An iterative hybrid transceiver design algorithm for millimeter wave MIMO systems,” IEEE Wireless Communications Letters, vol. 4, no. 3, pp. 285–288, 2015

The clustered channel model

• A (quite) detailed clustered channel model is presented in [8], where
• The multipath delays also descend from the system geometry;
• We include in the model a distance-dependent loss;
• We account for a non-zero probability that a Line-of-Sight (LOS) link exists

between the transmitter and the receiver;

• The proposed statistical channel model also accommodates time-varying

scenarios (not considered in this talk).

References [8]

• S. Buzzi and C. D’Andrea, “On clustered statistical MIMO millimeter wave channel simulation,” ArXiv

e-prints [Online] Available: https://arxiv.org/abs/1604.00648, May 2016

The clustered channel model

H(τ) = γ

Ncl

• i=1

Nray,i

• l=1

αi,l

• L(ri,l)ar(φr

i,l, θr i,l)×

aH

t (φt i,l, θt i,l)h(τ − τi,l) + HLOS(τ) .

(1)

The clustered channel model

γ

Ncl

• i=1

Nray,i

• l=1

αi,l

• L(ri,l)ar(φr

i,l, θr i,l)aH t (φt i,l, θt i,l)h(τ − τi,l)

αi,l ∼ CN(0, 1) complex path gain L(ri,l) path loss ri,l link length τi,l = ri,l/c propagation delay ar(φr

i,l, θr i,l)

normalized receive array response vectors γ =

• NRNT

NclNray normalization factor

Channel Generation routine available

Matlab scripts for generating the described clustered channel model are available here https://github.com/CarmenDAndrea/mmWave Channel Model

Link

Channel Generation routine available

Matlab scripts for generating the described clustered channel model are available here https://github.com/CarmenDAndrea/mmWave Channel Model

Link

However, you may also want to check:

• QuaDRiGa (QUAsi Deterministic RadIo channel GenerAtor) model

Link

• 3GPP TR 38.901 document (range 0.5 - 100 GHz)

mmWave channel versus sub-6GHz

MIMO Channel at mmWave behaves differently from what we may believe for analogy with MIMO channels conventional (sub-6 GHz) cellular frequencies

References [9] ——, “Massive MIMO 5G cellular networks: mm-wave vs. µ-wave frequencies,” ZTE Communications,

• vol. 15, no. S1, pp. 41 – 49, 2017

[10]

• E. Bj¨
• rnson, L. V. der Perre, S. Buzzi, and E. G. Larsson, “Massive MIMO in sub-6 GHz and mmwave:

Physical, practical, and use-case differences,” vol. arxiv.org/abs/1803.11023, 2018

Difference #1: mmWave systems may be doubly massive

Difference #1: mmWave systems may be doubly massive

• We have already commented on this issue

Difference #1: mmWave systems may be doubly massive

• We have already commented on this issue
• Near-term applications may be backhaul link and V2X communications

Difference #1: mmWave systems may be doubly massive

• We have already commented on this issue
• Near-term applications may be backhaul link and V2X communications
• In the long-term wireless cellular communications may become another

application: Mobile devices with a massive number of antennas thus will not be available in few years, but, given the intense pace of technological progress, sooner or later they will become reality

Difference #2: Analog (beam-steering) beamforming may be optimal

Focusing, for simplicity, on the use of an uniform-linear-array, it is easily seen that, in the frequency-flat case, the channel is represented by a matrix expressed as H = γ

N

• i=1

αiar(θi

r)aH t (θi t)

Difference #2: Analog (beam-steering) beamforming may be optimal

Focusing, for simplicity, on the use of an uniform-linear-array, it is easily seen that, in the frequency-flat case, the channel is represented by a matrix expressed as H = γ

N

• i=1

αiar(θi

r)aH t (θi t)

Given the continuous random location of the scatterers, the departure and arrival angles will be different with probability 1, and, for large number of antennas, the vectors

• ar(θi

r)

N

i=1 will become orthogonal. The same can be

said for the vectors in the set

• at(θi

t)

N

i=1.

Difference #2: Analog (beam-steering) beamforming may be optimal

Focusing, for simplicity, on the use of an uniform-linear-array, it is easily seen that, in the frequency-flat case, the channel is represented by a matrix expressed as H = γ

N

• i=1

αiar(θi

r)aH t (θi t)

Given the continuous random location of the scatterers, the departure and arrival angles will be different with probability 1, and, for large number of antennas, the vectors

• ar(θi

r)

N

i=1 will become orthogonal. The same can be

said for the vectors in the set

• at(θi

t)

N

i=1.

These vectors thus tend to coincide with the left and right singular vectors of the channel matrix H, and purely analog (beam-steering) beamforming tends to be optimal.

Difference #2: Analog (beam-steering) beamforming may be optimal

Figure: Spectral Efficiency of a mm-wave MIMO wireless link versus received SNR for CM-FD beamforming and AN (beam-steering) beamforming, for two different values

• f the number of transmit and receive antennas and of the multiplexing order M of

the system.

Difference #3: The rank of the channel does not increase with NT and NR

Difference #3: The rank of the channel does not increase with NT and NR

• At µ-wave frequencies, the i.i.d. assumption for the small-scale fading

component of the channel matrix H, guarantees that with probability 1 the matrix has rank equal to min(NT, NR).

Difference #3: The rank of the channel does not increase with NT and NR

• At µ-wave frequencies, the i.i.d. assumption for the small-scale fading

component of the channel matrix H, guarantees that with probability 1 the matrix has rank equal to min(NT, NR).

• At mmWave frequencies, instead, the validity of the clustered channel

model directly implies that, including the LOS component, the channel has at most rank NclNray + 1

Difference #3: The rank of the channel does not increase with NT and NR

• At µ-wave frequencies, the i.i.d. assumption for the small-scale fading

component of the channel matrix H, guarantees that with probability 1 the matrix has rank equal to min(NT, NR).

• At mmWave frequencies, instead, the validity of the clustered channel

model directly implies that, including the LOS component, the channel has at most rank NclNray + 1

• At mmWave the multiplexing capabilities of the channel depend on the

number of scatterers and not on the number of antennas.

Difference #4: Channel estimation is simpler

Difference #4: Channel estimation is simpler

• In µ-Wave massive MIMO systems channel estimation is a rather difficult

and resource-consuming task, since it requires the separate estimation of each entry of the matrix H; it thus follows that in a multiuser system with K users equipped with NR antennas each , the number of parameters to be estimated is KNRNT. The attendant computational complexity needed to perform channel estimation is a growing function of the number of used antennas.

• Additionally, the increase of the number of antennas NR at the mobile

devices has a direct impact on the network capacity.

Difference #4: Channel estimation is simpler

• In µ-Wave massive MIMO systems channel estimation is a rather difficult

and resource-consuming task, since it requires the separate estimation of each entry of the matrix H; it thus follows that in a multiuser system with K users equipped with NR antennas each , the number of parameters to be estimated is KNRNT. The attendant computational complexity needed to perform channel estimation is a growing function of the number of used antennas.

• Additionally, the increase of the number of antennas NR at the mobile

devices has a direct impact on the network capacity.

• At mmWave frequencies, instead, the clustered channel model is basically

a parametric model, and the number of parameters is essentially independent of the number of antennas. The computational complexity of the channel estimation schemes at mm-waves may be smaller than that at µ-waves.

Difference #4: Channel estimation is simpler

• Among the several existing approaches to perform channel estimation at

mm-wave, the most considered ones rely either on compressed sensing or

• n subspace methods. As an example, the paper [11] shows that at

mm-waves, for increasing number of antennas, the most significant components of the received signal lie in a low-dimensional subspace due to the limited angular spread of the reflecting clusters.

• Other papers considering the problem of channel estimation at mmWave

frequencies are reported below

References [11]

• S. Haghighatshoar and G. Caire, “Massive MIMO channel subspace estimation from low-dimensional

projections,” IEEE Transactions on Signal Processing, Oct. 2016 [12]

• H. Ghauch, T. Kim, M. Bengtsson, and M. Skoglund, “Subspace estimation and decomposition for large

millimeter-wave MIMO systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3,

• pp. 528–542, Apr. 2016

[13]

• O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. Heath, “Spatially sparse precoding in millimeter

wave MIMO systems,” vol. 13, no. 3, pp. 1499–1513, Mar. 2014 [14]

• S. Buzzi and C. D’Andrea, “Subspace tracking algorithms for millimeter wave MIMO channel estimation

with hybrid beamforming,” in Proc. 21st International ITG Workshop on Smart Antennas, 2017

Difference #5: Pilot contamination can be less critical

• Pilot contamination is the ultimate disturbance in massive MIMO systems
• perating at µ-waves.

Difference #5: Pilot contamination can be less critical

• Pilot contamination is the ultimate disturbance in massive MIMO systems
• perating at µ-waves.
• It is due to the fact that in a system where the number of users is larger

than the number of training symbols devoted to training, not enough

• rthogonal pilots are available

Difference #5: Pilot contamination can be less critical

• Pilot contamination is the ultimate disturbance in massive MIMO systems
• perating at µ-waves.
• It is due to the fact that in a system where the number of users is larger

than the number of training symbols devoted to training, not enough

• rthogonal pilots are available
• At mmWave frequencies pilot contamination is a much less studied topic.
• However, it can be envisioned that pilot contamination at mmWave can be

a less critical problem, mainly due to the short-range nature of mmWave communications and to the expected smaller number of users in each cell.

mmWave versus µ-wave massive MIMO systems

The said differences ultimately lead to different use-cases a) Providing very large data-rates to few users with limited mobility support b) Multiplexing a large number of users in the same time-frequency slot with full mobility support

mmWave versus µ-wave massive MIMO systems

The said differences ultimately lead to different use-cases a) Providing very large data-rates to few users with limited mobility support b) Multiplexing a large number of users in the same time-frequency slot with full mobility support Using mmWaves for V2X is thus a major challenge, since it is an use-case that does not naturally fit with the intrinsic characteristics of mmWave frequencies.

Transceiver Complexity at mmWaves

• We have seen that mmWave systems may have a fairly large number of

antennas

Transceiver Complexity at mmWaves

• We have seen that mmWave systems may have a fairly large number of

antennas

• In a fully digital (FD) system, this would require a number of RF chains

equal to the number of antennas

Transceiver Complexity at mmWaves

• We have seen that mmWave systems may have a fairly large number of

antennas

• In a fully digital (FD) system, this would require a number of RF chains

equal to the number of antennas

• This is of course prohibitive for mmWave applications
• So, lower complexity beamforming structures are to be designed

Hybrid (HY) Analog-Digital Beamforming

In order to reduce hardware complexity with respect to the FD beamforming, in hybrid structures the (NT × M)−dimensional pre-coding matrix is written as Qopt = QRFQBB , where QRF is the (NT × NRF

T )-dimensional RF precoding matrix and QBB is

the (NRF

T

× M)−dimensional baseband precoding matrix. Since the RF precoder is implemented using phase shifters, the entries of the matrix QRF have all the same magnitude (equal to

1

√

NT ), and just differ for the phase.

Of course we have M ≤ NRF

T

≤ NT

HY Beamforming

The matrices QRF and QBB can be found by using the Frobenius norm as a distance metric and solving the following optimization problem: (Q∗

RF, Q∗ BB) =

arg min

QRF,QBB

||Qopt − QRFQBB||F subject to |QRF(i, j)| =

1

√

NT ,

∀i, j ||QRFQBB||2

F ≤ M .

(2)

HY Beamforming

Similarly, with regard to the design of the post-coding beamforming matrix, the

• ptimal FD beamformer Dopt that we would use in case of no hardware

complexity constraints is approximated by the product DRFDBB, where DRF is the (NR × NRF

R )−dimensional RF post-coding matrix and DBB is the

(NRF

R

× M)−dimensional baseband post-coding matrix.

HY Beamforming

Similarly, with regard to the design of the post-coding beamforming matrix, the

• ptimal FD beamformer Dopt that we would use in case of no hardware

complexity constraints is approximated by the product DRFDBB, where DRF is the (NR × NRF

R )−dimensional RF post-coding matrix and DBB is the

(NRF

R

× M)−dimensional baseband post-coding matrix. The entries of the RF post-coder DRF are constrained to have norm equal to

1

√

NR . The matrices DRF and DBB can be then found solving the following

• ptimization problem

(D∗

RF, D∗ BB) =

arg min

DRF,DBB

||Dopt − DRFDBB||F subject to |DRF(i, j)| =

1

√

NR ,

∀i, j . (3)

HY Beamforming

It is easy to show that optimization problems (2) and (3) are not convex

• ptimization problem; inspired by [12], we thus resort to the Block Coordinate

Descent for Subspace Decomposition (BCD-SD) algorithm, that basically is based on a sequential iterative update of the analog part and of the baseband part of the beamformers.

HY Beamforming

It is easy to show that optimization problems (2) and (3) are not convex

• ptimization problem; inspired by [12], we thus resort to the Block Coordinate

Descent for Subspace Decomposition (BCD-SD) algorithm, that basically is based on a sequential iterative update of the analog part and of the baseband part of the beamformers.

HY Beamforming

HY Beamforming is a very active research topic and several other algorithms are available.

References [2]

• A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding for

millimeter wave cellular systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5,

• pp. 831–846, 2014

[4]

• T. E. Bogale and L. B. Le, “Beamforming for multiuser massive MIMO systems: Digital versus hybrid

analog-digital,” in 2014 IEEE Global Communications Conference (GLOBECOM). IEEE, 2014, pp. 4066–4071 [7] C.-E. Chen, “An iterative hybrid transceiver design algorithm for millimeter wave MIMO systems,” IEEE Wireless Communications Letters, vol. 4, no. 3, pp. 285–288, 2015 [15]

• S. Han, I. Chih-Lin, Z. Xu, and C. Rowell, “Large-scale antenna systems with hybrid analog and digital

beamforming for millimeter wave 5G,” IEEE Communications Magazine, vol. 53, no. 1, pp. 186–194, 2015 [5]

• L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser MIMO systems,”

IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, 2014 [16]

• R. M´

endez-Rial, C. Rusu, A. Alkhateeb, N. Gonz´ alez-Prelcic, and R. W. Heath, “Channel estimation and hybrid combining for mmwave: Phase shifters or switches?” in Information Theory and Applications Workshop (ITA), 2015. IEEE, 2015, pp. 90–97 [17]

• F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale antenna arrays,”

IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 501–513, Jan. 2016

Other low-complexity beamforming approaches

Other low-complexity beamforming approaches

• Purely-analog (beam-steered) beamformers

Other low-complexity beamforming approaches

• Purely-analog (beam-steered) beamformers
• Beamformers with quantized phase-shifts

Other low-complexity beamforming approaches

• Purely-analog (beam-steered) beamformers
• Beamformers with quantized phase-shifts
• Switch-based beamformers

Other low-complexity beamforming approaches

• Purely-analog (beam-steered) beamformers
• Beamformers with quantized phase-shifts
• Switch-based beamformers
• FD post-coding beamforming based on low-resolution ADC

(Briefs on) Cellular Networking Deployments

• Millimeter wave are essentially a short-range communication technology
• Realizing a stand-alone mmWave cellular network for V2X requires a very

dense deployment

Figure: Coverage versus node-density [18]

References [18]

• M. Giordani, A. Zanella, and M. Zorzi, “Technical report - millimeterwave communication in vehicular

networks: Coverage and connectivity analysis,” CoRR, vol. abs/1705.06960, 2017

Cell-free massive MIMO networking architectures [19, 20]

• A recently introduced communication architecture
• It is the scalable way to implement network MIMO

References [19]

• H. Q. Ngo, A. Ashikhmin, H. Yang, E. G. Larsson, and T. L. Marzetta, “Cell-free massive MIMO versus

small cells,” IEEE Transactions on Wireless Communications, vol. 16, no. 3, pp. 1834–1850, 2017 [20]

• S. Buzzi and C. D’Andrea, “Cell-free massive MIMO: User-centric approach,” IEEE Wireless Commu-

nications Letters, vol. 6, no. 6, pp. 706–709, Dec 2017

Cell-free massive MIMO

• It is a viable architecture for providing mmWave broadband V2X
• Vehicles can be simultaneously served by more than one AP
• There is inherent macro-diversity, which is helpful against blockages
• Can be coupled with MEC-based applications with low latency
• Many interesting problems arise here: vehicle-AP association rule, spacing

among the APs, how to distribute antennas, etc...

References [21]

• M. Alonzo and S. Buzzi, “Cell-free and user-centric massive MIMO at millimeter wave frequencies,” in

2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communica- tions (PIMRC), Oct 2017, pp. 1–5 [22]

• M. Alonzo, S. Buzzi, and A. Zappone, “Energy-efficient downlink power control in mmwave cell-free

and user-centric massive MIMO,” in 2018 IEEE 5G World Forum, Jul 2018, pp. 1–4

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications Limited to short-range communications: they will complement and not substitute conventional sub-6 GHz frequencies

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications Limited to short-range communications: they will complement and not substitute conventional sub-6 GHz frequencies Channel characteristics are different from those at sub-6GHz frequencies, and also hardware constraints may be more stringent

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications Limited to short-range communications: they will complement and not substitute conventional sub-6 GHz frequencies Channel characteristics are different from those at sub-6GHz frequencies, and also hardware constraints may be more stringent This implies that the achievable spectral efficiency may not be as large as at sub-6 GHz frequencies, but of course this is overweighted by the availability of one order of magnitude larger bandwidth

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications Limited to short-range communications: they will complement and not substitute conventional sub-6 GHz frequencies Channel characteristics are different from those at sub-6GHz frequencies, and also hardware constraints may be more stringent This implies that the achievable spectral efficiency may not be as large as at sub-6 GHz frequencies, but of course this is overweighted by the availability of one order of magnitude larger bandwidth Intense research on low-complexity beamforming structures, for now. FD structure may come back sometime in the future

Lecture wrap-up: Take-home points

MmWaves: one of the pillars of 5G revolution, and, eventually, of V2X communications Limited to short-range communications: they will complement and not substitute conventional sub-6 GHz frequencies Channel characteristics are different from those at sub-6GHz frequencies, and also hardware constraints may be more stringent This implies that the achievable spectral efficiency may not be as large as at sub-6 GHz frequencies, but of course this is overweighted by the availability of one order of magnitude larger bandwidth Intense research on low-complexity beamforming structures, for now. FD structure may come back sometime in the future Being limited to short-range communications, their use in vehicular-environments with high-mobility is a great challenge.

THANK YOU!!

Stefano Buzzi, Ph.D. Universit` a di Cassino e del Lazio Meridionale buzzi@unicas.it

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