Doubly-Massive MIMO Systems at mmWave Frequencies: Opportunities and Research Challenges

Doubly-Massive MIMO Systems at mmWave Frequencies:Opportunities and Research Challenges

Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies

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If you use concepts and ideas from these slides, please acknowledge it by citing:

S. Buzzi, “Doubly-massive MIMO systems at mmWave frequencies:Opportunities and research challenges,” IEEE WCNC’2016 Workshop on Greenand Sustainable 5G Wireless Networks, keynote talk, Doha (Qatar), April 2016

Bibtex entry:@Conference{buzziWCNC2016keynote,Title = {{Doubly-massive MIMO systems at mmWave frequencies:Opportunities and research challenges}},Author = {S. Buzzi},Booktitle = {IEEE WCNC’2016 Workshop on Green and Sustainable 5GWireless Networks},Year = {2016},Address = {Doha, Qatar},Month = {April},Note = {keynote talk}}


Millimeter Waves (mmWaves)

One of the ”key pillars” of 5G networks

Refers to above-6Ghz frequencies

Regulators worldwide are starting releasing spectrum chunks at frequenciesup to 100GHz

The main benefit here is the availability of large bandwidths


The Path-Loss Challenge...

- Friis’ Law: PR = PTGTGR

(λ

4πd

)2

- We may have heavy shadowing losses:brick, concrete > 150 dBHuman body: Up to 35 dB

NLOS propagation mainly relies on reflections


And there is also increased atmospheric absorption...


The Path-Loss: a not-so-hard challenge...

However...

- For a constant physical area, GT and GR ∝ λ−2

- Otherwise stated, the number of antennas that can be packed in a givenarea 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 inan area as large as a credit card

The number climbs up to 1300 at 80GHz!!


Some words of wisdom...

We have some serious/challenging practical and physical impairments:

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

- ADC bottleneck: forget all-digital beamforming

- Power consumption issues

- Low efficiency of power amplifiers


The MIMO Eldorado...

MIMO communications have been around for more than two decades.


The MIMO Eldorado...

...started with these landmark papers:

References

[2] J. H. Winters, J. Salz, and R. D. Gitlin, “The impact of antenna diversity on the capacity of wirelesscommunication systems,” IEEE Transactions on Communications, vol. 42, no. 234, pp. 1740–1751, 1994

[3] G. J. Foschini, “Layered space-time architecture for wireless communication in a fading environmentwhen using multi-element antennas,” Bell labs technical journal, vol. 1, no. 2, pp. 41–59, 1996

[4] P. W. Wolniansky, G. J. Foschini, G. Golden, and R. A. Valenzuela, “V-blast: An architecture for realizingvery high data rates over the rich-scattering wireless channel,” in 1998 URSI International Symposiumon Signals, Systems, and Electronics, 1998. ISSSE 98. IEEE, 1998, pp. 295–300

[5] V. Tarokh, N. Seshadri, and A. R. Calderbank, “Space-time codes for high data rate wireless communi-cation: Performance criterion and code construction,” IEEE Transactions on Information Theory, vol. 44,no. 2, pp. 744–765, 1998

The main and striking result was that capacity increased linearly withmin{NT ,NR}.


But for mmWaves...

Many of the results that hold for sub-6Ghz frequencies do nottranslate to mmWave frequencies


The clustered channel model

- The rich scattering environment assumption typically assumed for sub-6GHz does not hold at mmWaves. The following no longer holds:

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

At mmwaves, a “clustered” channel model is more representative of thephysical propagation mechanism

Ncl scattering clustersEach cluster contributes with Nray propagation paths

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





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

References

[6] O. El Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding inmillimeter wave MIMO systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp.1499–1513, 2014

[7] A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel estimation and hybrid precoding formillimeter wave cellular systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 5,pp. 831–846, 2014

[8] S. Haghighatshoar and G. Caire, “Enhancing the estimation of mm-Wave large array channels by ex-ploiting spatio-temporal correlation and sparse scattering,” in Proc. of 20th International ITG Workshopon Smart Antennas (WSA 2016), 2016

[9] S. Buzzi, C. D’Andrea, T. Foggi, A. Ugolini, and G. Colavolpe, “Spectral efficiency of MIMO millimeter-wave links with single-carrier modulation for 5G networks,” in Proc. of 20th International ITG Workshopon Smart Antennas (WSA 2016), 2016

[10] T. E. Bogale and L. B. Le, “Beamforming for multiuser massive MIMO systems: Digital versus hybridanalog-digital,” in 2014 IEEE Global Communications Conference (GLOBECOM). IEEE, 2014, pp.4066–4071

[11] 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

[12] J. Lee, G.-T. Gil, and Y. H. Lee, “Exploiting spatial sparsity for estimating channels of hybrid MIMO sys-tems in millimeter wave communications,” in 2014 IEEE Global Communications Conference (GLOBE-COM). IEEE, 2014, pp. 3326–3331

[13] C.-E. Chen, “An iterative hybrid transceiver design algorithm for millimeter wave MIMO systems,” IEEEWireless Communications Letters, vol. 4, no. 3, pp. 285–288, 2015


Our clustered channel model...

- Detailed in [14], in the clustered channel model used here...- the departure and arrival angles of the rays are tied by the geometry of the

system;- The number of clusters is not fixed a-priori but is a function of the link

length;- 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

[14] S. Buzzi and C. D’Andrea, “A clustered statistical MIMO millimeter wave channel model,” IEEE WirelessCommunications Letters, submitted., 2016



H(τ) = γ

Ncl∑i=1

Nray∑l=1

αi,lΛr (φri,l , θ

ri,l)Λt(φ

ti,l , θ

ti,l)×

L(ri,l)ar (φri,l , θ

ri,l)aH

t (φti,l , θ

ti,l)h(τ − τi,l) + HLOS(τ) . (1)



γ

Ncl∑i=1

Nray∑l=1


ri,l)Λt(φ

ti,l , θ

ti,l)L(ri,l)ar (φ

ri,l , θ

ri,l)aH

t (φti,l , θ

ti,l)h(τ − τi,l)

αi,l ∼ CN (0, 1) complex path gainL(ri,l) path lossri,l link lengthτi,l = ri,l/c propagation delayΛr (φ

ri,l , θ

ri,l) receive antenna element gains

ar (φri,l , θ

ri,l) normalized receive array response vectors

γ =

√NRNT

NclNraynormalization factor



Number of clusters depending on the TX-RX distance

Ncl(d) =

⌈Nmin

cl +Nmax

cl − Nmincl

d3d3

⌉d ≤ d ,

Nmaxcl d > d ,

(2)

Suggested values are Nclmin = 10, Nmaxcl = 50 and d = 200m. The fixed value

Nray = 8 is used.The angles φt

i,l , l = 1, . . . ,Nray have a Laplacian distribution whose meanφti ∼ U [0, 2π], and with standard deviation σφ = 5deg.

The angles, θti,l are again conditionally Laplacian with a mean θti uniformlydistributed in [−π/2, π/2] and variance σθ = 5deg.



- For the path loss L(ri,l) we have [15]:

L(ri,l)dB = β + 10α log10(ri,l) , (3)

with values β = 50 dB and α = 3.3.

- Transmitter’s and receiver’s antenna element are modeled as being idealsectored elements, so Λr (φ

ri,l , θ

ri,l) and Λt(φ

ti,l , θ

ti,l) are expressed as

Λx(φxi,l , θ

xi,l) =

{1 φx

i,l ∈ [φxmin, φ

xmax], θxi,l ∈ [θxmin, θ

xmax],

0 otherwise ,(4)

where x may be either r or t.

References

[15] S. Singh, M. N. Kulkarni, A. Ghosh, and J. G. Andrews, “Tractable model for rate in self-backhauledmillimeter wave cellular networks,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 10,pp. 2196–2211, 2015



For a planar array with YZ antennas, the array response vectors ar (φri,l , θ

ri,l)

and at(φti,l , θ

ti,l) are

ax(φxi,l , θ

xi,l) =

1√YxZx

[1, . . . , e−jkd(m sinφxi,l sin θxi,l+n cos θxi,l ),

. . . , e−jkd((Yx−1) sinφxi,l sin θxi,l+(Zx−1) cos θxi,l )] , (5)

- x may be either r or t

- k = 2π/λ

- d is the inter-element spacing


The LOS component...

- Let φrLOS, φt

LOS, θrLOS, and θtLOS be the departure angles corresponding tothe LOS link

- We have

HLOS(τ) = ILOS(d)√NRNT e

jδar (φrLOS, θ

rLOS)aH

t (φtLOS, θ

tLOS)h(τ − τLOS)

withδ ∼ U(0, 2π)ILOS(d) is a random variate indicating if a LOS link exists betweentransmitter and receiverWe assume that ILOS(d) = 1 with probability 0.5 for d < 5m, and withprobability 0.11 for d < 100m, while being zero in all the remaining cases


Channel Generation routine available

Matlab scripts for generating the described clustered channel model areavailable here

https://github.com/CarmenDAndrea/mmWave Channel Model Link


https://github.com/CarmenDAndrea/mmWave_Channel_Model

mmWave MIMO is not like sub-6GHz MIMO

H(τ) = γ

Ncl∑i=1

Nray∑l=1


ri,l)Λt(φ

ti,l , θ

ti,l)×


ri,l)aH

t (φti,l , θ

ti,l)h(τ − τi,l) + HLOS(τ) .

- Neglecting the LOS component, the channel has at most rank NclNray

- The channel rank depends by the geometry, but is independent of thenumber of antennas

- This has an impact on the multiplexing capabilities of the channel


Impact of increasing NT and NR

H(τ) = γ

Ncl∑i=1

Nray∑l=1


ri,l)Λt(φ

ti,l , θ

ti,l)×


ri,l)aH

t (φti,l , θ


- The array response vectors

ax(φxi,l , θ

xi,l) =

1

Nx[1, . . . , e−jkd(m sinφx

i,l sin θxi,l+n cos θxi,l ),

. . . , e−jkd((Yx−1) sinφxi,l sin θxi,l+(Zx−1) cos θxi,l )] , (6)

for increasing number of antennas, tend to an orthogonal sets.

- Otherwise stated, for large NT , the vectors at(φti,l , θ

ti,l), for all i and l ,

provided that the departure angles are different, converge to anorthogonal set.

- The same applies to the vectors ar (φri,l , θ

ri,l) for large values of NR .


Impact of increasing NT and NR

H(τ) = γ

Ncl∑i=1

Nray∑l=1


ri,l)Λt(φ

ti,l , θ

ti,l)×


ri,l)aH

t (φti,l , θ


For large NT and NR , and distinct arrival and departure angles, the arrayresponse vectors become the right and left singular vectors of the matrixchannel.

Matrix Algebra

Recall indeed that H = UΛVH =∑i

λiuivHi


Take-Home Points

- The number of clusters and rays has an impact on the channel rank (and,hence, multiplexing and diversity channel capabilities)

- Large values of NT and NR just help in increasing the received power(scales linearly with the product NTNR), and in sharpening the beams ofthe radiation patterns

- For large values of NT and NR , and separate departure angles, the transmitand receive array responses tend to be orthogonal. The most favourablechannel eigen-direction is the one pointing to the strongest scatterer


Transceiver model with TDE

r(n) = DHy(n) =

eP−1∑`=0

DHH(`)Qs(n − `) + DHw(n) . (7)

A block linear MMSE equalizer is applied to remove intersymbol interference.

s(n) = EH reP(n) , (8)


Transceiver model with FDE

yCP(n) = H(n) ~ xCP(n) + w(n) , n = 1, . . . , k (9)

RCP(n) = H(n)XCP(n) + W(n) , (10)

ZCP(n) = EH(n)RCP(n) = SCP(n) + (H(n)Q)−1W(n) .


Considerations on Complexity

TDE structure: the computation of the equalization matrix E requires theinversion of the covariance matrix of the vector reP(n), with a

computational burden proportional to (PM)3; then, implementing Eq. (8)requires a matrix vector product, with a computational burdenproportional to (PM2); this latter task must be made k times in order toprovide the soft vector estimates for all values of n = 1, . . . , k.

FDE structure: 2M FFTs of length k are required, with a complexityproportional to 2Mk log2 k; in order to compute the zero-forcing matrix,

the FFT of the matrix-valued sequence H(n) must be computed, with acomplexity proportional to MNtT (k log2 k); computation of the matrix

(H(n)Q) and of its inverse, for n = 1, . . . , k, finally requires acomputational burden proportional to k(NTM

2 + M3).


Hybrid precoding and decoding

We use here hybrid digital/analog beamforming

The number of RF chains is equal to the multiplexing order

The used algorithm is the Block Coordinate Descent for SubspaceDecomposition [16]

References

[16] H. Ghauch, M. Bengtsson, T. Kim, and M. Skoglund, “Subspace estimation and decomposition forhybrid analog-digital millimetre-wave mimo systems,” in 2015 IEEE 16th International Workshop onSignal Processing Advances in Wireless Communications (SPAWC). IEEE, 2015, pp. 395–399


ASE with QPSK inputs

Multiplexing order M = 2; d = 30m; SRRC pulses with roll-off 0.22.


ASE with QPSK inputs

Multiplexing order M = 2; PT = 0dBW; SRRC pulses with roll-off 0.22.


ASE with Gaussian inputs

Multiplexing order M = 2; d = 30m; SRRC pulses with roll-off 0.22.By means of an horizontal shift, the curves can be made (almost) perfectlyoverlapping!


ASE with Gaussian inputs

The curve corresponding to the configuration 50× 100 has been shifted to theleft of 10 log10(10)The curve corresponding to the configuration 10× 50 has been shifted to theleft of 10 log10(30)


ASE with Gaussian inputs: impact of number of clusters

NR × NT = 50× 100Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies

What about energy efficiency?

- Dividing the ASE by the consumed power we obtain a measure of energyefficiency, which we nickname the spectral energy efficiency [bit/J/Hz]

- Multipliying the spectral EE by the signal bandwidth we obtain theconventional EE (measured in bit/J)

Spectral EE =ASE

NTPc + ηPT

Remarks

1 We are considering the energy consumed at the transmitter only

2 EE heavily depends on Pc


Spectral EE with Gaussian inputs



Spectral EE with QPSK inputs


Remark: Very similar behaviour to the case of Gaussian inputs


Spectral EE

BIG QUESTION:Can we find antenna arrays with sub-linear power consumption?


Conclusions

- Mmwaves are an exciting field for wireless research

- They promise to make true the multi-gigabit experience for everyone

- Their use implies radically new ways of leveraging the performancebenefits granted by the use of multiple antennas

- Hardware/Complexity/Energy constraints are to be seriously taken intoaccount

- There is need for accurate energy consumption models

- There is need of extensive measurements campaign (already ongoing froma while) to validate the clustered model

Acknowledgement

Special thanks to Ms. Carmen D’Andrea, Ph.D. student at UNICAS, forproducing most of the figures shown here.


THANK YOU!!

Stefano Buzzi, Ph.D.University of Cassino and Lazio Meridionale

[email protected]


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Doubly-Massive MIMO Systems at mmWave Frequencies: Opportunities and Research Challenges