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}}
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
And there is also increased atmospheric absorption...
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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!!
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The MIMO Eldorado...
MIMO communications have been around for more than two decades.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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}.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
But for mmWaves...
Many of the results that hold for sub-6Ghz frequencies do nottranslate to mmWave frequencies
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at 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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
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)
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
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 (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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
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.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
- 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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
The clustered channel model
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Channel Generation routine available
Matlab scripts for generating the described clustered channel model areavailable here
https://github.com/CarmenDAndrea/mmWave Channel Model Link
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
mmWave MIMO is not like sub-6GHz MIMO
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) .
- 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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Impact of increasing NT and NR
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) .
- 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 .
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Impact of increasing NT and NR
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) .
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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)
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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) .
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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).
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
ASE with QPSK inputs
Multiplexing order M = 2; d = 30m; SRRC pulses with roll-off 0.22.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
ASE with QPSK inputs
Multiplexing order M = 2; PT = 0dBW; SRRC pulses with roll-off 0.22.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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!
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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)
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Spectral EE with Gaussian inputs
Multiplexing order M = 2; d = 30m; SRRC pulses with roll-off 0.22.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Spectral EE with QPSK inputs
Multiplexing order M = 2; d = 30m; SRRC pulses with roll-off 0.22.
Remark: Very similar behaviour to the case of Gaussian inputs
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
Spectral EE
BIG QUESTION:Can we find antenna arrays with sub-linear power consumption?
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
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.
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies
THANK YOU!!
Stefano Buzzi, Ph.D.University of Cassino and Lazio Meridionale
Stefano Buzzi, University of Cassino and Lazio Meridionale Doubly-massive MIMO at mmWave Frequencies