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1Massive MIMO for 5G: From Theory to Practice

Massive MIMO for 5G: From Theory to Practice

Linglong Dai （戴凌龙）

Department of Electronic EngineeringTsinghua University

Dec. 2015


Content

5G and Massive MIMO1

Massive MIMO: Theoretical Performance2

Massive MIMO: Practical Solutions3

4 Future Research

5 Summary


How to realize 5G? Key requirement of 5G: 1000-fold capacity How to realize this goal from Shannon capacity? Three technical directions for 5G

C = D * W * M * log (1+SINR)

No. of APs Bandwidth

No. of antennas Interference mitigation


Use hundreds of BS antennas to simultaneously serve multiple users

Conventional MIMOM:2~8, K:1~4 (LTE-A)Conventional MIMOM:2~8, K:1~4 (LTE-A)

Massive MIMOM: ~100~1000, K: 16~64

Massive MIMOM: ~100~1000, K: 16~64

T. L. Marzetta, “Non-cooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas,” IEEE Transactions on Wireless Communications, vol. 9, no. 11, pp. 3590-3599, Nov. 2010. (2013 IEEE Marconi prize)

What is massive MIMO?


Content




4 Future Research

5 Summary


Spatial multiplexing‒ Rate: min , log 1 SNR

Reliability‒ ∼ SNR

Array gain (beamforming)‒ Several antennas can be used to transmit signals

Why Massive MIMO ?

No. of antennas Error Probability ( ) Capacity ( ), bps/Hz

1, 1 (SISO) ∼ SNR log 1 SNR

1, 1 (SIMO) ∼ SNR log 1 SNR

1, 1 (MIMO) ∼ SNR min , log 1 SNR

:Diversity Gain min , :Multplexing Gain

Massive , : significantly increased spectral efficiency


Theoretical Capacity Analysis MIMO link, channel ∈ with

log 1SNR

, SNR

If ∼ 1, then we have ∑ , so‒ Rank-1 channel (LoS): , ⋯ 0

‒ Full rank channel: ⋯ favorable propagation

If is i.i.d. and ≫ , then we have favorable propagation


Theoretical Capacity Analysis In massive MIMO, the channel matrix is decomposed

into two parts‒ Small-scale fading: of size , elements are i.i.d.‒ Large-scale fading: a diagonal matrix /

⋯ 0⋮ ⋱ ⋮0 ⋯

⋯ 0⋮ ⋱ ⋮0 ⋯

, ≫

Ideal massive MIMO channel has favorable propagation

Asymptotical orthogonality


Ideal Channels Massive MIMO has much larger ordered singular values

than conventional MIMO

F. Rusek, D. Persson, B. Lau, E. Larsson, T. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up MIMO: Opportunities and challenges with very large arrays,” IEEE Signal Processing Magazine, vol. 30, no. 1, pp. 40-60, Jan 2013.


Ideal Detection Optimal (coherent) uplink detector has complexity ~exp

min ∥ ∥ With favorable propagation in massive MIMO ( ≫ )

1

we can have

min ∥1

∥ ⇔ min

In massive MIMO, we can use simple (linear) detectors like MF, ZF with good enough performance and low complexity ∼

Similarly, simple (linear) precoders can be also used


Feb. 2012, Rice university & Bell labs, Argos, 64 antennas, 15 users, 85 bit/s/Hz, 1/64 power consumption

Sep. 2013, Rice university & Bell labs, ArgosV2, 96 antennas, 32 users

July 2013, Linköping & Lund University, 128 antennas, 36 users

Recent Advances of Massive MIMO


World’s First Massive MIMO PrototypeSamsung, 2014


Opportunities and challenges Advantages

‒ Improve the spectrum efficiency by orders of magnitude ‒ Improve the energy efficiency by orders of magnitude

Vision‒ Considered as a promising key technology for 5G

Challenges‒ Theoretical analysis with practical constraints‒ Reduce the power consumption of RF chains‒ Pilot contamination in the uplink‒ Efficient pilot design and channel estimation algorithm ‒ Efficient channel feedback mechanism‒ Low-complexity near-optimal signal detection algorithm


Content

5G in The World1



4 Future Research

5 Summary


Work 1: Performance Analysis of Massive MIMO with Practical Constraints Work 2: Pilot Decontamination Based on Graph Coloring Work 3: Efficient Pilot Design and Channel Estimation Based on Compressive Sensing Work 4: Low-Complexity Multi-User Detection for Uplink Massive SM-MIMO Work 5: Energy-Efficient SIC-Based Hybrid Precoding for Massive MIMO Work 6: Beamspace Massive MIMO

Practical Solutions for Massive MIMO


Practical Solutions: Work 1

Performance Analysis of Massive MIMO with Practical Constraints

Jiayi Zhang, Linglong Dai, Xinlin Zhang, Emil Björnson, and Zhaocheng Wang, “Ergodic Capacity of Massive MIMO Systems with Transceiver Hardware Impairments over Rician Fading Channels,” to appear in IEEE Transactions on Vehicular Technology.


Motivation The performance of massive MIMO systems is usually

limited by practical constraints‒ Hardware impairments

• Phase noise• I/Q imbalance• Amplifier non-linearities• Quantization errors

‒ Space constrained‒ Low-resolution ADC‒ Channel aging‒ Imperfect CSI‒ Inter-carrier interference‒ Co-channel interference‒ …


Motivation Huge spatial degrees-of-freedom of massive MIMO

systems are achieved by coherent processing over these massive arrays, which provide ‒ strong signal gains‒ resilience to imperfect channel knowledge ‒ and low interference

However, the hardware cost and circuit power consumption scale linearly/exponentially with the number of BS antennas, and practical constraints cannot be removed completely

Whether massive MIMO systems can provide robustnessto practical constraints?


The system model can be written as [1]

The additive distortion noise terms can be analytically approximated by the central limit theorem as

In LTE, the error vector magnitude (EVM) are in the range

Hardware Impairments

t ry = H x + η + η + n

21

2

0, diag , ,

0, tr

t

r

t N

r r N

CN q q

CN

tη

η Q I

0.08,0.175t

[1] T. Schenk, RF Imperfections in High-Rate Wireless Systems: Impact and Digital Compensation. Springer, 2008.


Normalized noise variance

The ergodic achievable rate R can be expressed as


22

22

1 ,

1 ,

t

r

Htr N t r

t

Htr N t r

t

N NN

N NN

H H IΦ

HH I

12

12

log det ,

log det ,

t

r

HN t r

t

HN t r

t

E N NN

R

E N NN

I H HΦ

I HH Φ


Proposition 1: The exact achievable rate of MIMO systems with residual hardware impairments over Ricianfading channels can be expressed as

,1 1 0

1 / 1 /b1 1

1

2 21

2 2

ln 2 1 1

1 1

min( , ), max( , )

1, ,

1 1 !

i

kq qn

n mn m k

p q m kK a K

p q m k t p q m k tt

t r t r

qt t i

t t t t

p q m kqGR Dk p q k

K Ke E e Ea b

q N N p N N

ea b G

N N p q

1

qj i

i j q



For → ∞, the achievable rate reduces to

For → ∞, the achievable rate reduces to

For and → ∞, the achievable rate reduces to


2 2 2log 11tN r

t r

R N

2 2

1log 1rN t

t

R N

2 2

2 22 2

1log det log det

1 1r r

t H HtN N

t r t r

R EN N

HH I HH I

2 2log det log det

1/ , 1/ ,

r r

r r

H HN N

N N

E a b

J a J b

HH I HH I

I I


A finite rate ceiling An increase in SNR tends

to increase achievable rate of both systems

The relative difference between the curves gets steadily larger

Higher K values yield lowerachievable rate

The gap decreases as K increases


0.15, 2t r t rN N

SNR [dB]-5 0 5 10 15 20 25 30 35 40

Ach

ieva

ble

Rat

e [b

its/s

/Hz]

0

5

10

15

20

25

30

Non-ideal (Analytical)Non-ideal (Simulations)Ideal capacity

K = 0, 10, 100

Ceiling


The ceiling disappears for large numbers of transmit and receive antennas

Larger values of K will decrease the rank of correlation matrix and the system’s achievable rate

Utilize ideal hardware at massive MIMO systems when operating over strong LoS environment


0.15, , 10t r t rN N

Number of Transmit/Receive Antennas (Nt=Nr)0 10 20 30 40 50 60

0

20

40

60

80

100

120

140

160

180Non-ideal (Analytical)Non-ideal (Simulations)Ideal Achievable Rate

K=0

K=100

K=10


A critical issue pertaining to practical massive MIMO systems is the dense deployment for a large number of antennas in a limited physical space

The channel vectors for different UEs will be asymptotically non-orthogonal

Therefore, a space-constrained massive MIMO architecture will suffer from increased spatial correlation, whose impact needs to be rigorously quantified and analyzed

Space Constrained


The received vector y at the BS is given by

Channel matrix / , where is the transmit steering matrix and is given by

With receiver matrix T, the achievable uplink rate is

Space Constrained

up y Gx n

1 2

2 2sin 1 sin

, , ,

1 1, , ,i i

P

Td dj j M

i e eP

A a a a

a

2

2 2 2log 1

Hu k k

k KH

u k l kl k

pR E

p

t g

t g t


Proposition 1: For space-constrained massive MIMO systems with MRC receivers, the approximated sum achievable rate is given by

where is the ith eigenvalue of A, and denotes the lth element of

Space Constrained

2 2

1MRC2

2

1

log 1

P

u i ki

K P

u l i kl k i

p MR

p M


denotes the normalized total antenna array space

The sum rate saturates with an increasing number of BS antennas

MRC suffers a substantial rate degradation when spatial correlation is high

the gap decreases as increases, which implies that the effect of becomes less pronounced

Space Constrained

0 , 12, 6d dM P K

50 100 150 200 250 300 350 400 450 5007

7.5

8

8.5

9

9.5

10

Monte-Carlo simulationAnalytical approximation


Proposition 2: For space-constrained massive MIMO systems with ZF receivers, the sum achievable rate is lower bounded by

Space Constrained

1 22

1log 1 exp

P

nK KP KZF n P K

L u k n P Pk n k j i j ii j i j

R p K n

YY

1

1,

,ln ,

qp

k qp qp p

q nq n

Y


Proposition 3: For space-constrained massive MIMO systems with ZF receivers, the sum achievable rate is upper bounded by

Space Constrained

2 12 1 1

1 1

12

1

K log1

ln 2

ZFU uK K K K

j i j ii i j i i j

P

nKn P K

Pn j ii j

R pK i K i

K n

Δ Δ

Y

12 p,q

2 p,q

, 1, , 1

, 2, ,

qp

qp

q P K

q P K q P K P

Ξ

Φ

1 1 1 2 2 21

1 p,q

1 p,q

,

,q 1, ,

1 ,q 1, ,

qp

qp

P K

q P K P K P

Δ ΞΦ Δ Ξ Φ

Ξ

Φ


The lower bounds can explicitly predict the exact sum rate Large antennas can improves the sum rate of the massive

MIMO ZF by suppressing thermal noise, even in the space constrained scenario

Space Constrained

0 4, 12d P

10 20 30 40 50 60 70 80 90 1004

6

8

10

12

14

16

18

ZF Lower BoundZF Upper BoundMonte-Carlo Simulation


The massive MIMO systems is more prone to practical constraints in strong LoS fading channel

For space-constrained massive MIMO systems, we derive ‒ approximated sum rate expression of MRC receivers‒ new lower and upper bounds on the sum rate of ZF

receivers The performance of ZF receivers is superior to the one of

MRC receivers for space-constrained massive MIMO systems

Conclusions


Graph Coloring Based Pilot Decontamination for Massive MIMO

Xudong Zhu, Linglong Dai, and Zhaocheng Wang, “Graph Coloring Based Pilot Allocation to Mitigate Pilot Contamination for Multi-Cell Massive MIMO Systems,” IEEE Communications Letters, vol. 19, no. 10, pp. 1842-1845, Oct. 2015.



Massive MIMO in Mobile Network A mobile cellular network with cells, each of singe-

antenna users and ( ≫ ) antennas BS– The system works in TDD protocol– Channel of -th user in -th BS to -th BS: , , , , , ,

– Large-scale fading coefficient: , , 0 (channel strength).– Small-scale fading vector: , , ∈ ,


What is Pilot Contamination (PC)? Channel estimation in uplink transmission

– User ⟨ , ⟩ utilizes pilot sequence ∈ for channel estimation ⋯ ∈ ,

– The channel estimation for user ⟨ , ⟩ will be contaminated by users in other cells with the same pilot

, , , , , , , , .

Uplink SINR limitation– By adopting MF detector, uplink SINR of user ⟨ , ⟩ is limited by PC

SINR ,, , , ,

∑ | , , , , | ,

→ , ,

∑ , ,

– , denotes the power of uncorrelated interference and noise


Example of PC Pilot reuse in adjacent cells

– Due to limited pilot resource, pilot reuse is unavoidable– PC to users in cell center is usually light– PC to users in cell edge is usually severe

– Total 3 pilots are utilized for 3 cells– User ⟨3,1⟩ suffers from slight PC– User 1,1 and 2,1 are

contaminated to each other


Existing Technology Frame structure design

– Using time-shifted pilots for asynchronous transmission among adjacent cells is able to mitigate pilot contamination

Exploiting channel properties– AoA (angle-of-arrival) based methods– Subspace partitioning based blind methods– Coordinated multi-cell precoding– ……


Motivation Potential PC among users in different cells

– Potential PC , ,⟨ , ⟩ is utilized to measure PC severity between two users when they are assigned with the same pilot

, ,⟨ , ⟩, ,

, ,

, ,

, ,.

– , ,⟨ , ⟩ is actually the ratio of the interference channel strength and the effective channel strength

– Larger , ,⟨ , ⟩ indicates more severe PC will be introduced between user ⟨ , ⟩ and ⟨ , ⟩ when they are assigned with the same pilot

– Key idea: Assign different pilots to users with a large


PC Graph Construction Potential PC threshold

– Based on a potential PC threshold , a binary potential PC matrix , , , can be generated as

, ,⟨ , ⟩

1, , ,1, , , , , ,0, otherwise.

– A PC graph can be constructed based on

, , , , , , , 1

Pilot reuse among connected users should be avoided.


Conventional Graph Coloring (GC) Minimizing the total number of colors

– Conventional GC algorithms aim to minimize the total number of colors to assign different colors to connected vertices

– To find the minimal number of colors for a certain is a NP problem– The total number of required colors is defined as– For various , is different and uncertain

Total 4 pilots are required


GC based Pilot Allocation (GC-PA) Under limited pilot resource

– For users within each cell, usually only pilots are available– Users are sorted according to their degrees in , and assign pilots to

these users in a sequential way– Assign different pilots to connected users as much as possible– For various , only pilots are utilized for pilot assignment

Total 3 pilots are utilized


Analysis of Threshold How to obtain

– The potential PC graph is constructed based on – The initial interval of can be easily obtained as

∈ ,

– min , ,⟨ , ⟩ and max , ,⟨ , ⟩

– By setting , only users within one cell are connected– By setting , all users are connected to each other– To obtain a near-optimal threshold , an iterative grid search (IGS)

algorithm is adopted

, , , .– denotes the number of grids in each search step– denotes the number of iterations


Iterative Grid Search (IGS) of IGS Algorithm

– The interval , is uniformly sampled by points in the first iteration

: 1 , ,

Δ , Δ 1 .

– By denoting as one out of that can achieve the best performance, a sub interval can be obtained after the first iteration, i.e.,

Δ2 ,

Δ2

– The sub interval will be further sampled, and this procedure is carried out in an iterative way for times

– Finally, a near-optimal threshold can be obtained


IGS Algorithm– System parameters: (1) 7; (2) 8; (3) 128– IGS parameters: (1) 20; (2) 2

Simulation result (1)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 110

12

14

16

18

(th-min)/max

Ave

rage

upl

ink

SIN

R (d

B)

First iteration of the IGS

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 117

17.05

17.1

17.15

(th-max(1) +(1)/2)/(max

(1) +(1)/2)

Ave

rage

upl

ink

SIN

R (d

B)

Second iteration of the IGS

The sub-interval for the nextiteration of the IGS process

The final threshold th=max(2)


CDF curve of users’ uplink achievable rate– System parameters: (1) 4; (2) 4; (3) 128– The optimal solution is obtained by exhaustive search


1 2 3 4 5 6 70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

User UL achievalable rate (bps/Hz)

CD

F

Classical scheme [1]Conventional GCAs [5]Proposed GC-PA schemeOptimal PA

The performance of the userssuffering from severe PC hasbeen significantly improved

The proposed GC-PA schemecan approach the optimal pilotallocation


Average uplink achievable rate against antenna number– System parameters: (1) 7; (2) 84; (3) 10 10000


101 102 103 1041.5

2

2.5

3

3.5

4

4.5

5

Number of BS antennas M

Ave

rage

UL

capa

city

per

use

r (bp

s/H

z)

Classical scheme [1]Conventional GCAs [5]Proposed GC-PA scheme

Gain of the significantly reducedPC by conventional GCA and theproposed GC-PA scheme

The performance of theclassical random schemeis limited by PC

Gain of the restricted pilotresource.


A graph coloring based pilot allocation (GC-PA) scheme isproposed to mitigate pilot contamination for massiveMIMO

Basic ideas– Construct potential PC graph for multi-cell multi-user network

– GC-PA: Assign different pilots to connected users in PC graph to mitigate severe PC as much as possible

– An iterative grid search (IGS) algorithm is proposed to obtain a near-optimal threshold for PC graph construction

Simulation result (2) has verified the near-optimal (0.1bps/Hz) performance of our method compared withoptimal solution through exhaustive search

Conclusions



Compressive Sensing Based Efficient Pilot Design and Channel Estimation

Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen, “Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO,” IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6169-6183, Dec. 2015.


Motivation Orthogonal pilots for LTE/LTE-A

Different channels are distinguished by orthogonal pilots 100% pilot !


Angle-Domain Massive MIMO Channels

Massive MIIMO channels is sparse in the angle domain due to angle spread is small (e.g., 10o) at the BS with limited scattersX. Rao and V. K. N. Lau, “Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMOsystems,” IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3261–3271, Jun. 2014.

T Tn n n n n B n ny w w x h x A h

Angle-domain channel

a DFT matrix for ULA with

180 / 1.406sf M

10 10 1.406 8

128M

suppn n aS M h

/ 2d


Spatially common sparsity holds due to spatial propagation characteristics of the channels within the system bandwidth are almost unchanged

X. Rao and V. K. N. Lau, “Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMOsystems,” IEEE Trans. Signal Process., vol. 62, no. 12, pp. 3261–3271, Jun. 2014.

K M

K M

K M

Spatially Common Sparsity

1 2supp supp supp N h h h


Compressive Sensing (CS)‒ (1949) Shannon-Nyquist sampling theory: sufficient condition for

perfect reconstruction of a bandwidth limited signals

‒ (2006) Compressive sensing: Acquire and reconstruct a sparse signal by a sampling rate much lower than the Nyquist rate

fs 2B

M N

Shannon

DonohoD. L. Donoho, “Compressed sensing”, IEEE Trans. Info. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006. (cited by 14106 times)

Background of Compressive Sensing


Key idea of CS‒ Conventional way: sampling the signal at a high rate first, and then compress the signal

to remove the redundancy‒ CS: directly sampling the inherent information of signals, and then reconstruct the high-

dimensional signal from low-dimensional measurement via optimization‒ Three steps of CS: 1) Spare representation; 2) Compression; 3) Recovery‒ Applications of CS: Image/Vedio processing, MRI, Radar, Wireless communications …

Compressive sensing is a breakthrough theory for signal processing, which has great potential impacts in many applied fields including wireless communications



Conventional orthogonal pilot proposed non-orthogonal pilots

Proposed Non-Orthogonal Pilots


Non-orthogonal pilots based channel estimation byexploiting CS theory

CS-Based Channel Estimation

N subc

arrier

s

K M

K M

K M

M BS antennas

1 2supp supp supp N h h h Spatially common sparsity:

Received pilot signal in G time slots: ( ) ( )[ , ] [ , ] * [ , ] [ , ] [ , ]T q qq G q G q G q G q G

p pp p B p p p r S A h v Φ h v


Proposed Distributed Sparsity AdapativeMatching Pursuit (DSAMP) Algorithm

Joint Processing


2

0, 1min .

l

L

ll lf

f Problem 1

D

Pilot design

Performance Analysis

i i i i i i i r S h S Ah Θ h

s.t. ,supp ,l l l l l d Φ f f

, ,[ , ]

,1 ,1t m pjq G

p t me t G m M S


Performance approaches the CRLB with substantially reduced pilot overhead !

Simulation parameters:

1. Carrier frequency 2 GHz

2. System bandwidth 10 MHz

3. FFT size 2048

4. BS 128 Tx

5. 15°angle spread

10 15 20 25 3010-3

10-2

10-1

100

SNR (dB)

MS

E

J-OMP, Fixed Time Overhead, T=18DSAMP, Fixed Time Overhead, T=18CRLB of Conventional Linear Algorithms

Simulation Results


Conclusions This paper focuses on the downlink pilot design and

channel estimation for massive MIMO systems At the transmitter, compared with standardized

orthogonal pilots (pilot overhead ∝ No. of Tx), weproposed the non-orthogonal pilots design based onstructured CS can effectively solve this issue (pilotoverhead ∝ small angle spread)

At the receiver, the proposed DSAMP algorithm canexploit the spatially common sparsity of massive MIMOchannels for reliable channel estimation

Moreover, the proposed scheme can be applied in theuplink to solve the issue of pilot contamination



Low-Complexity Multi-User Detection for Uplink Massive SM-MIMO

Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen, and Lajos Hanzo, “Compressive Sensing Based Multi-User Detector for Large-Scale SM-MIMO Uplink,” to appear in IEEE Transactions on Vehicular Technology.


Motivation and Background

Key requirements of 5G Spectrum Efficiency (SE) Energy Efficiency (EE)

Key techniques Massive MIMO improves SE at cost of low EE


Motivation and Background

Key requirements of 5G Spectrum Efficiency (SE) Energy Efficiency (EE)

Key techniques Massive MIMO improves SE at cost of low EE Spatial modulation (SM) MIMO improves EE at cost of low SE


System Model

Spatial Modulation (SM) MIMO No. of RF chains << No. of antennas


System Model

Spatial Modulation (SM) MIMO No. of RF chains << No. of antennas 3-D constellation set Spatial and signal constellation symbols


System Model

Massive SM-MIMO High SE

Large No. of antennasLow cost antennas

High EESmall number of RF chainsLow hardware cost Low power consumption


System Model

Challenges of Massive SM-MIMO Support multi-user transmission in the uplink

Only consider single-user scenario

Optimal for multi-user detection Large No. of users Large No. of antennas Optimal maximum like-hood (ML): High complexity Sphere decoding: High complexity

Low complexity signal detector LMMSE-based detector [Renzo’14]: poor performance CS-based detector [Liu’14]: poor performance

[Liu’14] W. Liu, N. Wang, M. Jin, and H. Xu, “Denoising detection for the generalized spatial modulation system using sparseproperty,” IEEE Commun. Lett., vol. 18, no. 1, pp. 22-25, Jan. 2014.

[Renzo’14] M. Di Renzo, H. Haas, A. Ghrayeb, S. Sugiura, and L. Hanzo, “Spatial modulation for generalized MIMO: Challenges,opportunities and implementation,” Proc. IEEE, vol. 102, no. 1, pp. 56-103, Jan. 2014.


Proposed Solutions

How to support multi-user transmission in uplink?

One RF chain but multiple antennas for each user !


Proposed Solutions

How to reduce the complexity of signal detector?

Sparsity of SM signals can be exploited!

1x

2x

Kx


Proposed Solutions


k k ksx eSignal constellation symbol (M-PSK, M-QAM)

Spatial constellation symbol

0 2supp , 1, 1.k k k e Q e e


Proposed Solutions

1 1

K K

k k kk k

y y w H x w

The kth user's MIMO channel matrix

The kth user's SM signal

AWGN


Kx

1x

2xx


Proposed Solutions

1 1

K K

k k kk k

y y w H x w

y Hx w

Compressive sensing problem !


Kx

1x

2xx


Compressive Sensing (CS)‒ (1949) Shannon-Nyquist sampling theory: sufficient condition for

perfect reconstruction of a bandwidth limited signals

‒ (2006) Compressive sensing: Acquire and reconstruct a sparse signal by a sampling rate much lower than the Nyquist rate

fs 2B

M N

Shannon

DonohoD. L. Donoho, “Compressed sensing”, IEEE Trans. Info. Theory, vol. 52, no. 4, pp. 1289–1306, Apr. 2006. (cited by 14106 times)



Key idea of CS‒ Conventional way: sampling the signal at a high rate first, and then compress the signal

to remove the redundancy‒ CS: directly sampling the inherent information of signals, and then reconstruct the high-

dimensional signal from low-dimensional measurement via optimization‒ Three steps of CS: 1) Spare representation; 2) Compression; 3) Recovery‒ Applications of CS: Image/Video processing, MRI, Radar, Wireless communications …

Compressive sensing is a breakthrough theory for signal processing, which has great potential impacts in many applied fields including wireless communications




Proposed Solutions

y Hx w

Standard compressive sensing

Uniquely sparsity

Block sparsity

Reduced complexity and improved performance

Kx

1x

2xx


Proposed Solutions

How to enhance the reliability of signal detector? Introduce the structured sparsity

1 2supp supp supp Jk k k x x x

,1 .j j j j j J y H x w

( ) ( )

1 1

22( ) ( )( ) ( ) ( ) ( )

21 1 1 2

min minJ Jj j

j j

J J Kj jj j j jkk

j j k

x x

y H x y H x

2( )

0s.t. 1,1 ,1 .

jk j J k K x 1 2supp supp supp J

k k k x x x

Kx

1x

2xx

3J


Proposed Solutions

How to enhance the reliablity of signal detector? Introduce the channel diversity by interleaving

1 2 J H H H

Temoral correlationof channels

Improved performance

Cyclic shiftInterleaving

Substantially improvedperformance

,1 .j j j j j J y H x w

1 2 J H H H


Example for Interleaving (J=2)


Proposed Group SP (GSP) algorithm


Proposed Solutions Complexity comparison

‒ The optimal ML detector suffers from high complexity‒ MMSE and CS detectors have low complexity but poor performance‒ Proposed GSP algorithm enjoys low complexity with good performance

Algorithm ML MMSE CS ProposedGSP

Complexity ( )KtO Ln 2 3

RF ( ) ( )( )t tO M n K n K 2 3RF2O M K K 2 3

RF2( )O M K K


Simulations

Obvious performance gain from interleaving! Point-to-point SM-MIMO:1. No. Tx 642. No. Rx 163. Correlation coff 0.4 for Tx/Rx4. One Tx RF chain5. 6 bit spatial constel. syb.6. 8-PSKsignal constel. syb.7. J=2




Simulations

Higher throughput than massive MIMO ! Near-optimal signal detection performance !

Multi-user SM-MIMO uplink:1. BS 64 Tx but 18 receive RF chains2. 8 Users each 4 Tx and 1 transmit RF chain3. Correlation coff 0.5 for Tx/Rx




Conclusions

This paper focuses on the multi-user detection for uplinkmassive SM-MIMO

A reliable and low-complexity multi-user signal detectoris proposed

Proposed signal detector can fully exploit the blocksparsity of equivalent SM signal for the reducedcomplexity

By introducing the SM signal interleaving, the signaldetection performance can be further improved

Simulation results have demonstrated the goodperformance of the proposed scheme



SIC-Based Energy-Efficient Hybrid Precoding for Massive MIMO

Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Robert Heath, “Energy-Efficient Hybrid Analog and Digital Precoding formmWave MIMO Systems with Large Antenna Arrays”, to appear in IEEE Journal on Selected Areas in Communications.


MmWave massive MIMO Why mmWave?

Why mmWave + massive MIMO?– Short wavelength enables large antenna array in massive MIMO– Massive MIMO provides sufficient gains to compensate the serious

path-loss by using precoding

mmWave

High frequencies Short wavelength Serious path-loss

Spectrum extension Massive MIMO Small cell

1000x capacity increase!


Challenges– Traditional MIMO: one dedicated RF chain for one antenna Enormous number of RF chains due to large antenna array High complexity in signal processing Unaffordable energy consumption (250 mW per RF chain) 64 antennas → 64 RF chains → 16 W !

How to reduce the requirednumber of RF chains?How to reduce the requirednumber of RF chains?

Challenges of mmWave massive MIMO


Precoding for mmWave massive MIMO Traditional precoding

– Preformed in digital domain with optimized performance – One RF chain is required to support one transmit antenna– Impractical in energy consumption for mmWave massive MIMO

250mW per RF chain, and 16W for 64 antennas [Amadori’15] !

Hybrid analog and digital precoding– Actual degree of freedom (i.e., #users) is much smaller than #antennas– Divide digital precoding with large size into:

Digital precoding with small size Analog precoding with large size (realized by phase shifter, PS)

– Significantly reduced number of RF chains – Power-efficient, low complexity, without obvious performance loss

[Amadori’15] P. Amadori and C. Masouros, “Low RF-complexity millimeter-wave beamspace-MIMO systems by beam selection,” IEEE Trans. Commun., vol. 63, no. 6, pp. 2212-2222, Jun. 2015.


Existing hybrid precoding architectures Fully-connected architecture

– RF chain is fully connected to all antennas Large number of PSs (N2M) Near-optimal but energy-intensive

– Spatially sparse precoding [Ayach’14]– Codebook-based hybrid precoding [Roh’14]

Sub-connected architecture– RF chain is partially connected to a subset

of antennas Smaller number of PSs (NM) More energy-efficient

– The optimal solution is unavailable Challenge: changed constraints

[Ayach’14] O. El Ayach S. Rajagopal, S. Abu-Surra,Z. Pi, and R.W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,”IEEE Trans. Wireless Commun., vol. 13, no. 3,pp. 1536-1276, Mar. 2014.

[Roh’14] W. Roh, et al., “Millimeter-wave beamforming as an enabling technology for 5G cellular communications: Theoretical feasibility andprototype results,” IEEE Commun. Mag., vol. 52, no. 2, pp. 0163-6804, Feb. 2014.


Problem formulation System model

Total achievable rate

2 2log .H HNR

N

I HPP H

, y HADs n HPs n

Three non-convex constraints– Structure constraint:– Amplitude constraint: All elements of have fixed amplitude – Power constraint:

Target– Jointly design A and D to maximize the achievable rate

FNP

1 1diag , , diag , ,N Nd d P AD a a ia 1/ M


SIC-based hybrid precoding Successive interference cancelation (SIC) for multi-user

signal detection4

( ) ( )1

i ii

y h x


1p 2p Np

1T 1NT0 NT I

where is the nth column of P, , and

SIC-based hybrid precoding Proposition 1: The total rate R can be decomposed as

SIC-based hybrid precoding– Total rate sub-rate of sub-antenna array– Optimize the sub-rate of each sub-antenna array one by one by exploiting the

concept of SIC for multi-user detection

12 12

1

log 1N

H Hn n n

n

RN

p H T Hp

2H H

n N n nN

T I HP P H 0 NT Inp


Find sufficiently close to to maximize the achievable sub-rate Find sufficiently close to to maximize the achievable sub-rate

Solution to the sub-rate optimization problem

We prove that it is equivalent to a simplified problem

Target– Optimize achievable rate of the nth sub-antenna array

– Consider non-zero elements

opt2 12arg max log 1

n

Hn n n nN

p

p p G p ，

2opt1 2

arg minn

n n

p

p v p ，

where 11 1

Hn n

G H T H

opt2 12arg max log 1

n

Hn n n n

F N

p

p p G p ，

where , 1 1H

n n G RG R 1 MM M n M M N n R 0 I 0

where is the first right singular vector of 1v 1nG

np 1v

Proposition 2. The optimization problem is equivalent to the following problem


Design of analog and digital precoder Problem

– As we have , equals to

Solution– Analog precoder: – Digital precoder:– Hybrid precoder:– Easy to check all the three constraint conditions are satisfied

n n ndp a 21 2nv p

1angle( )opt 1 / jn M e va

Summary of our method– SVD of to obtain – Compute for the nth sub-antenna array– Update for the (n+1)th sub-antenna array

2 221 12 1Re 1 Ren n

H Hn nd v p v va a

opt1 1 1

/Re Hn nd M v a v

1nG 1v 1angle( )opt

1 11 / j

n M e vp v

nG

1angle( )opt1 1

1 / jn M e vp v


Computation of – Only the first right singular vector of is required– Realized by power iteration algorithm with complexity

1v1nG

2M

Acquire the optimal precoder– The complexity is only to obtain 1angle( )opt

1 11 / j

n M e vp v

Update

– Corresponding complexity is

nG M

12

1 1 1 1 12 2 ,1 Hn n N N

G G v v

2M

Total complexity – Only 10% of [11] !

2 ( )M NS K

is largest singular value of 1 1nG

Complexity analysis

Proposition 2. The matrix can be simplified as nG


-30 -25 -20 -15 -10 -5 00

5

10

15

20

25

SNR (dB)

Ach

ieva

ble

rate

(bps

/Hz)

Optimal unconstrained precoding (full-connected)Spatially sparse precoding (full-connected) [6]Optimal unconstrained precoding (sub-connected)Proposed SIC-based precoding (sub-connected)Conventional analog precoding (sub-connected) [21]

-30 -25 -20 -15 -10 -5 00

5

10

15

20

25

30

SNR (dB)

Ach

ieva

ble

rate

(bps

/Hz)

Optimal unconstrained precoding (fully-connected)Spatially sparse precoding (fully-connected) [6]Optimal unconstrained precoding (sub-connected)Proposed SIC-based precoding (sub-connected)Conventional analog precoding (sub-connected) [21]

Simulation setup– Antennas: (1) (2)– RF chains: (1) (2) – Channel: Geometric Saleh-Valenzuela model

64 16NM K 128 32NM K 8N 16N

87%

SIC-based hybrid precoding is near-optimal！SIC-based hybrid precoding is near-optimal！

Simulation results


We proposed a hybrid precoding scheme with sub-connectedarchitecture for mmWave massive MIMO systems

Basic ideas– Decompose the total achievable rate into the sum of sub-rates

– Optimize the sub-rate of each sub-antenna array one by one by exploiting the concept of SIC for multi-user detection

The computational complexity of our method is ,only 10% of that of conventional scheme

Simulation results have verified the near-optimal (87%)performance of our method

2 ( )M NS K

Conclusions



Beamspace Massive MIMO

Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Channel Estimation and Beam Selection in Beamspace for Millimeter-Wave Massive MIMO,” to be submitted to IEEE Transactions on Signal Processing.


Advantages of mmWave massive MIMO

Advantages– Larger bandwidth: 50MHz → 1GHz More users and higher capacity

– Larger antenna array: 1~8 → 256~1024 Larger antenna gain to compensate serious path loss More data streams to improve spectral efficiency

mmWave


Spectrum expansion Large antenna array Small cell

1000x data rates increase!


Challenges– Traditional MIMO: one dedicated RF chain for one antenna Enormous number of RF chains due to large antenna array High complexity in signal processing Unaffordable energy consumption (250 mW per RF chain) 64 antennas → 64 RF chains → 16 W !

How to reduce the requirednumber of RF chains?How to reduce the requirednumber of RF chains?

Challenges of mmWave massive MIMO


Category 1: Hybrid beamforming

Basic idea [Ayach’14,Gao’15]– Decompose fully digital beamformer of large size: Digital beamformer with small size (realized by RF chains) Analog beamformer with large size (realized by phase shifters)

Performance– Reduce RF chains by signal

processing– Not obvious performance loss– Require complicated design– High computational complexity

[Ayach’14] O. El Ayach, et al., “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Trans. Wireless Commun., 2014.

[Gao’15] X. Gao, et al., “Energy-efficient hybrid analog and digital precoding for mmwave MIMO systems with large antenna arrays,”IEEE J. Sel. Areas Commun., 2015.


Category 2: Beamspace MIMO

Basic idea [Brady’14]– Transform spatial channel into beamspace channel (realized by lens) Limited scatters → beamspace channel is approximately sparse

– Select beams to reduce dimension (realized by switching network) – Digital beamformer with small size (realized by RF chains)

[Brady’14] J. Brady, et al., “Beamspace MIMO for millimeterwave communications: System architecture, modeling,analysis, and measurements,” IEEE Trans. Ant. and Propag., vol. 61, no. 7, pp. 3814–3827, Jul. 2013.

Performance– Reduce RF chains by discrete

lens array (DLA)– Classical beamformers can be

directly employed– Low computational complexity

A different but promising thought to reduce RF chainsA different but promising thought to reduce RF chains


System model– single-antenna users, BS with antennas, RF chains

– Saleh-Valenzuela channel model [Ayach’14]

where : spatial direction and : physical direction

– Transform the spatial channel into beamspace

where

Principle of beamspace MIMO

,H H y H x n H Ps n 1 2, , , N KK

H h h h NK

0 0

1

,L

i ik k k k k

i

h a a

21 ,j m

m Ne

N

a

LoS path NLoS paths ULA steering vector

sind

,H H H y H U Ps n H Ps n

DFT matrix realized by DLA

1 / 2, 0,1, , 1 ,N l N l N Beamspace channel

RFN K

1 2, , , ,H

N U a a a

1 / 2 / , 1,2, ,n n N N n N



Sparsity

– only has small number of dominantelements

– Approximately sparse

User index

Bea

m in

dex

2 4 6 8 10 12 14 16

10

20

30

40

50

60

1

2

3

4

5

6

7

8

9

10

11

12

Beamspace channel

1 2 1 2, , , , , ,K K H h h h UH Uh Uh Uh

Beam selection– Select a small number of dominant beams

– is the dimension-reduced precoder

– Only a small number of RF chains

kh

r r ,H y H P s n r ,:l

l

H H

rP


Challenges

1st : Channel estimation– should be estimated with only RF chains– Sparsity of should be fully utilized

2nd : Beam selection– Different users may select the same beam Severe interference Number of RF chains is uncertain and unfixed

– Near-optimal performance should be achieved with low complexity

HH

We propose an interference-aware (IA) beam selection scheme We propose an interference-aware (IA) beam selection scheme

HH

We propose a support detection (SD) based channel estimation algorithmWe propose a support detection (SD) based channel estimation algorithm

RFN K


Outline of SD-based channel estimation Technological process

– Consider TDD system– All the users transmit pilots to the BS– The BS employs analog combining to obtain measurement vectors– OMP algorithm is utilized to estimate the channel with low overhead– Channel reciprocity is utilized to obtain the downlink channel matrix

K


Measurements– All the users transmit orthogonal pilots over time slots – During the mth time block

– The BS employs a combining matrix of size

Consider the kth column of the measurement matrix

Measurements

Pilot:

mW K N

K M QK

UL , 1,2, , ,m m m m m m M Y UHΨ N HΨ N H Hm m m m KΨ Ψ Ψ Ψ I

ULm m m m m m m R W Y W HΨ W N effH

m m m m m Z R Ψ W H N

eff1, 1 1,

eff2, 2 2,

eff, ,

,

k k

k kk k k

M k M M k

k

z W nz W n

z

hz h W n

W n

A typical sparse signal recovery problem !A typical sparse signal recovery problem !

mZ


How to realize the combining matrix?

Beam selection is realized by switching network

Switching network can not be used for combing!


Proposed adaptive phase shifter network

We propose to replace switching network by adaptive phase shifter

network

The bridge to connect hybrid precoding and beamspace MIMO


How to realize beam selection by phase shifter network?– Based on , turn off some phase shifters (i.e., 0) and set some

phase shifters to shift the phase 0 degree (i.e., 1)

Beam selection via phase shifter network

opt

111 12

221 22

1 2

N

N

KNK K

jj j

jj j

jj j

e e ee e e

e e e

Channel estimation and beam selection can share the same moduleChannel estimation and beam selection can share the same module

Combining matrix (realized by phase shifter network) for

channel estimation

Switching matrix (realized by adaptively reconfiguring the phase shifter network) for beam selection

0

0

0

0 00 0

0 0

j

j

j

ee

e


Design of combinging matrix– i.i.d. Bernoulli random matrix enjoys satisfying estimate performance Each element of belongs to with equal probability

– Realized by phase shifters Only 1-bit phase shifters is required, low energy consumption

How to design combining matrix

W

Observation

Sparse beamspace channel vectorPerforms like the sensing matrix in CS

1/ 1, 1Q

Estimate the channel– Classical compressed sensing algorithms can be used– Poor performance when SNR is low


,kk k hz W n

W

More efficient algorithm is requiredMore efficient algorithm is required


SD-based channel estimation Problem: poor performance at low SNR Solution: exploiting asymptotical orthogonality and

structural characteristics of the beamspace channel

Insight of Proposition 1– The total channel estimation problem can be decomposed into a series

of independent sub-problems

Proposition 1. Represent the beamspace channel as ,where is the ith channel component of in the beamspace. Then, anytwo channel components and in the beamspace are asymptoticallyorthogonal when the number of antennas N in beamspace mmWave massiveMIMO systems tends to infinity, i.e.,

kh0

/ Lk ii

N L

h c i ic Uc kh

ic jc

lim 0, , 0,1, , , .Hi jN

i j L i j

c c


SD-based channel estimation Problem: poor performance at low SNR Solution: exploiting asymptotical orthogonality and

structural characteristics of the beamspace channel

Proposition 2. Consider the beamspace channel of the kth user. The ratiobetween the power of V strongest elements of and the total power of thechannel can be lower-bounded by

Once the position of the strongest element of is determined, other V-1strongest elements will uniformly locate around it.

khVP kh TP

/2

21 2

2 1 .2 1

sin2

VV

iT

PP N i

N

khn


Best caseWorst case

1 / N 1 / N

k1

1

sin2

NN

13sin2

NN

1 / 2N

Insight of Proposition 2– can be well-approximated by a sparse vector

– The support (positions of nonzero elements) of is determined by

kh

256, 8, / 95%V TN V P P

kh

We can directly obtain the support of at time according to We can directly obtain the support of at time according to kh

CS-based channel estimation

n

n


SD-based channel estimation

Insight


Problem– Only retain power without considering multi-user interferences – The number of required RF chains is uncertain and unfixed

Existing beam selection method

[Sayeed’14] A. Sayeed and J. Brady, “Beamspace MIMO for high-dimensional multiuser communication at millimeter-wave frequencies,” in Proc. IEEE GLOBECOM’13, Dec. 2013, pp. 3679–3684.

Magnitude maximization (MM) beam selection [Sayeed’14]– Select rows (beams) of with the largest magnitude– The corresponding beam indices set– The selected beams for all K users

khV 1 2= , , , V

k k k ks s s

1,2,= k

k K

Difficult to be realized in practical system!Difficult to be realized in practical system!


Interference-aware (IA) beam selection

Stage 1: Identify IUs and NIUs– Classify all K users into two user groups, i.e., interference-users (IUs)

and noninterference-users (NIUs), according to their strongest beams.

Stage 2: Search the best unshared beam– Propose an incremental algorithm to search the appropriate beam for

each IU

Motivation– Select the best beam for each user without repeat– The required number of RF chains is fixed as the number of users


Inspiration– The strongest beam of each user contains most of the total power– will also lead to unobvious multi-user interferences– Can we directly choose ?

Stage 1: Identify IUs and NIUs

kb

* * *1 2, , , Kb b b

kb

Lemma 5. Assume that spatial directions for follow the i.i.d.uniform distribution within . The probability Pr2 that there exist userssharing the same strongest beam is

!Pr 2 1 .

!K

NN N K

0k 1,2, ,k K

0.5,0.5

Definitions– NIUs: one user k is NIU if its strongest beam is different from any

other strongest beams, i.e., – IUs: any two users k1 and k2 are IUs if

kb

* * * * *1 1 1, , , , ,k k k Kb b b b b

* *1 2k kb b


Stage 2: Search the best unshared beam

Step 2: Search the optimal beam set of IUs– Select the beams for NIUs– Select beams from as – Combine and to form the set – Based on , select beams of beamspace channel– The dimension-reduced MIMO system

– Search the optimal by maximizing the achievable sum-rate

– Form the optimal set of selected beams for all K users

optI

IUCard IU

K

r r r, ,: ,H

ll

y H P s n H H

H

Dimension-reduced digital precoderoptI R

IU

optIU arg max ,R

2

1

log 1 ,K

kk

R

2

, ,2 2

, ,

Hr k r k

k Hr k r mm k

h p

h p

opt opt optIU NIU

opt *NIU NIU= |kb k *

NIU1,2, , \ |kN b k

IUoptNIU



Bea

m in

dex




Simulation setup System parameters

– Frequency: 28 GHz– MIMO configuration:– Total time slots: ( time slots for per user)– Select candidate beams in the first stage of IA beam selection– Digital beamformer: Zero forcing (ZF)

Channel parameters– Channel model: Saleh-Valenzuela model– Antenna array: ULA at BS, with antenna spacing– Multiple paths: One LoS component and two NLoS components– LoS component Amplitude: Spatial direction:

– NLoS components Amplitude: Spatial direction:

2L

256 16,N K RF 16N K

/ 2d

0 0,1k 0 1 1,2 2k

2~ 0,10ik

1 1,2 2

ik

1 i L

3V 128M QK 8Q


NMSE of channel estimation Observations

– CS-based channel estimation can achieve satisfying accuracy– The required number of RF chains is only instead of 256– The overhead can be reduced by 62.5% (96 instead of 256 time slots)

RF 16N

2

22

2

ˆNMSE=

k k

k

k

h h

h

0 5 10 15 20 25 3010-2

10-1

100

101

Uplink SNR (dB)

NM

SE

(dB

)

Conventional OMP-based channel estimationProposed SD-based channel estimation


Energy efficiency & sum-rate

0 5 10 15 20 25 300

10

20

30

40

50

60

70

80

90

100

Downlink SNR (dB)

Ach

ieva

ble

sum

-rate

(bits

/s/H

z)

Fully digital systemIA beam selction with perfect CSISD-based channel estimation (uplink SNR = 0 dB)OMP-based channel estimation (uplink SNR = 0 dB)SD-based channel estimation (uplink SNR = 10 dB)OMP-based channel estimation (uplink SNR = 10 dB)SD-based channel estimation (uplink SNR = 20 dB)OMP-based channel estimation (uplink SNR = 20 dB)

8 10 20 30 40 50 60 640

2

4

6

8

10

12

14

16

18

Number of users K

Ene

rgy

effic

ient

(bps

/Hz/

W)

Fully digital systemConventional MM beam selection (2 beams per user)Proposed IA beam selection (1 beam per user)

Observations– IA beam selection can achieve much higher energy efficiency – IA beam selection with SD-based channel estimation is near-optimal


Conclusions We solve two challenging problems in beamspace MIMO The proposed CS-based channel estimation scheme may

be the first work to address the challenging channel estimation problem

The proposed CS-based channel estimation is realized by exploiting asymptotical orthogonality and structural characteristics of the beamspace channel

We design an adaptive selecting network to adaptively realize channel estimation and beam selection for beamspace MIMO systems

The proposed IA beam selection requires fixed number of RF chains, and achieves the performance quite close to the fully digital system


Content

5G in The World1



4 Future Research

5 Summary


Advantages– Larger bandwidth: 50MHz → 1GHz More users and more traffic

– Larger antenna array: 1~8 → 64~256 Larger antenna gain to compensate serious path loss More data streams to improve spectral efficiency

mmWave


Spectrum expansion Large antenna array Small cell

1000x data rates increase!

Millimeter-Wave Massive MIMO

Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Robert Heath, “Energy-Efficient Hybrid Analog and Digital Precoding for mmWave MIMO Systems with Large Antenna Arrays”, to appear in IEEE Journal on Selected Areas in Communications, available at: http://arxiv.org/abs/1507.04592


Basic idea [Brady’14]– Transform (realized by lens) into beamspace channel Limited scatters → beamspace channel is sparse

– Select beams (realized by switching network) to reduce dimension– Digital beamformer with small size (realized by RF chains)

Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Channel Estimation and Beam Selection forBeamspace Millimeter-Wave Massive MIMO Systems,” submitted to IEEE Transactions on Signal Processing.

Performance– Reduce RF chains by hardware

architecture– Classical beamformers can be

directly employed– Low computational complexity

A different but promising thought to reduce RF chainsA different but promising thought to reduce RF chains

Beamspace Massive MIMO


Spatial Modulation (SM)– Exploit antenna selection pattern to transmit extra data– Energy efficient but spectrum inefficient

Massive SM-MIMO– Exploit more antennas to increase the spectrum efficiency– Key challenges: signal detection and channel estimation

Massive MIMO with Spatial Modulation

Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen, and Lajos Hanzo, “Compressive Sensing Based Multi-User Detector for Large-Scale SM-MIMO Uplink,” to appear in IEEE Transactions on Vehicular Technology.


Massive MIMO with 1-Bit ADC Performance analysis

‒ MmWave channel model‒ Different antenna array (ULA, UPA, UCA)‒ Hardware impairment‒ Imperfect CSI

Signal detector‒ 1-bit detection performance‒ Design optimization

Jiayi Zhang, Linglong Dai, Shengyang Sun, and Zhaocheng Wang, “On The Spectral Efficiency of Massive MIMO Systems with Low-Resolution ADCs,” submitted to IEEE Communications Letters.


Massive MIMO for THz

Massive MIMO and NOMA

Massive MIMO for UDN

Massive MIMO for Energy Harvesting

Massive MIMO for Wireless Power Transfer

Extension of Massive MIMO

Linglong Dai, Bichai Wang, Yifei Yuan, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Non-Orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends,” IEEE Communications Magazine, vol. 53, no. 9, pp. 74-81, Sep. 2015.


Content

Introduction1


Seven Proposals for Massive MIMO3

4 Future Research Plan

5 Summary


Summary Massive MIMO is very promising technology for 5G wireless

communications Theoretically, massive MIMO can increase the spectrum and

energy efficiency by orders of magnitude We have proposed several practical solutions to address

challenging problems to realize massive MIMO– Performance analysis with practical constraints – Pilot decontamination based on graph coloring– Efficient pilot design and channel estimation based on CS– Low-complexity multi-user detection for uplink massive SM-MIMO– Energy-efficient SIC-based hybrid precoding– Beamspace massive MIMO

Future research directions– mmWave massive MIMO, massive SM-MIMO, beamspace massive

MIMO– Massive MIMO for NOMA, UDN, THz, energy harvesting, wireless

power transfer…


Reference and Recent Publications1. Xinyu Gao, Linglong Dai, Shuangfeng Han, Chih-Lin I, and Robert Heath, “Energy-Efficient Hybrid Analog and

Digital Precoding for mmWave MIMO Systems with Large Antenna Arrays”, to appear in IEEE Journal on Selected Areas in Communications, 2015. (IF: 4.138)

2. Zhen Gao, Linglong Dai, Zhaocheng Wang, Sheng Chen, and Lajos Hanzo, “Compressive Sensing Based Multi-User Detector for Uplink Large-Scale SM-MIMO,” to appear in IEEE Transactions on Vehicular Technology, 2015.

3. Xinyu Gao, Linglong Dai, Chau Yuen, and Zhaocheng Wang, “Turbo-Like Beamforming Based on Tabu Search Algorithm for Millimeter-Wave Massive MIMO Systems,” to appear in IEEE Transactions on Vehicular Technology, 2015.

4. Wenqian Shen, Linglong Dai, Byonghyo Shim, Shahid Mumtaz, and Zhaocheng Wang, “Joint CSIT Acquisition Based on Low-Rank Matrix Completion for FDD Massive MIMO Systems,” to appear in IEEE Communications Letters, 2015.

5. Zhen Gao, Linglong Dai, Zhaocheng Wang, and Sheng Chen, “Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO”, IEEE Transactions on Signal Processing, vol. 63, no. 23, pp. 6169-6183,. Dec. 2015. (IF: 3.198)

6. Linglong Dai, Xinyu Gao, Xin Su, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Low-Complexity Soft-Output Signal Detection Based on Gauss-Seidel Method for Uplink Multi-User Large-Scale MIMO Systems,” IEEE Transactions on Vehicular Technology, vol. 64, no. 10, pp. 4839-4845, Oct. 2015.

7. Zhen Gao, Linglong Dai, De Mi, Zhaocheng Wang, Muhammad Ali Imran, and Muhammad Zeeshan Shakir, “MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network,” IEEE Wireless Communications, vol. 22, no. 5, pp. 13-21, Oct. 2015. (IF: 6.524)


Reference and Recent Publications8. Xudong Zhu, Linglong Dai, and Zhaocheng Wang, “Graph Coloring Based Pilot Allocation to Mitigate Pilot

Contamination for Multi-Cell Massive MIMO Systems,” IEEE Communications Letters, vol. 19, no. 10, pp. 1842-1845, Oct. 2015.

9. Wenqian Shen, Linglong Dai, Xudong Zhu, and Zhaocheng Wang, “Compressive Sensing Based Differential Channel Feedback for Massive MIMO,” Electronics Letters, vol. 51, no. 22, pp. 1824-1826, Oct. 2015.

10. Zhen Gao, Linglong Dai, Chau Yuen, and Zhaocheng Wang, “Asymptotic Orthogonality Analysis of Time-Domain Sparse Massive MIMO Channels,” IEEE Communications Letters, vol. 19, no. 10, pp. 1826-1829, Oct. 2015.

11. Linglong Dai, Bichai Wang, Yifei Yuan, Shuangfeng Han, Chih-Lin I, and Zhaocheng Wang, “Non-Orthogonal Multiple Access for 5G: Solutions, Challenges, Opportunities, and Future Research Trends,” IEEE Communications Magazine, vol. 53, no. 9, pp. 74-81, Sep. 2015. . (IF: 4.460)

12. Jiayi Zhang, Linglong Dai, Wolfgang H. Gerstacker, and Zhaocheng Wang, “Effective capacity of communication systems over κ-µ shadowed fading channels,” Electronics Letters, vol. 51, no. 19, pp. 1540-1542, Sep. 2015. (1.068)

13. Xinyu Gao, Linglong Dai, Yuting Hu, Yu Zhang, and Zhaocheng Wang, “A Low-Complexity Signal Detection Algorithm for Large-Scale MIMO in Optical Wireless Communications,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1903-1912, Sep. 2015. (IF: 4.138)

14. Jiayi Zhang, Linglong Dai, Yanjun Han, Yu Zhang, and Zhaocheng Wang, “On the Ergodic Capacity of MIMO Free-Space Optical Systems over Turbulence Channels,” IEEE Journal on Selected Areas in Communications, vol. 33, no. 9, pp. 1925-1934, Sep. 2015. (IF: 4.138)


Reference and Recent Publications15. Jiayi Zhang, Linglong Dai, Yu Zhang, and Zhaocheng Wang, “Unified Performance Analysis of Mixed Radio

Frequency/Free-Space Optical Dual-Hop Transmission Systems,” IEEE/OSA Journal of Lightwave Technology, vol. 33, no. 11, pp. 2286-2293, June 2015.

16. Wenqian Shen, Linglong Dai, Zhen Gao, and Zhaocheng Wang, “Spatially Correlated Channel Estimation Based on Block Iterative Support Detection for Massive MIMO,” Electronics Letters, vol. 51, no.7, pp. 587-588, Apr. 2015.

17. Xinyu Gao, Linglong Dai, Yongkui Ma, and Zhaocheng Wang, “Low-Complexity Near-Optimal Signal Detection for Uplink Large-Scale MIMO Systems,” Electronics Letters, vol. 50, no. 18, pp. 1326-1328, Aug. 2014.

18. Zhen Gao, Linglong Dai, Zhaohua Lu, Chau Yuen, and Zhaocheng Wang, “Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations,” IEEE Communications Letters, vol. 18, no. 7, pp. 1266-1269, Jul. 2014.

19. Zhen Gao, Linglong Dai, and Zhaocheng Wang, “Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems,” Electronics Letters, vol. 50, no. 12, pp. 896-898, Jun. 2014.

20. Linglong Dai, Zhengyuan Xu, and Zhaocheng Wang, “Flexible Multi-Block OFDM Transmission for High-Speed Fiber-Wireless Networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 12, pp. 788-796, Dec. 2013. (IF: 4.138)

21. Linglong Dai, Jintao Wang, Zhaocheng Wang, Paschalis Tsiaflakis, and Marc Moonen, “Spectrum- and Energy-Efficient OFDM Based on Simultaneous Multi-Channel Reconstruction,” IEEE Transactions on Signal Processing, vol. 61, no. 23, pp. 6047-6059, Dec. 2013. (IF: 3.198)

22. Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Compressive Sensing Based Time Domain Synchronous OFDM Transmission for Vehicular Communications,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 9, pp. no. 460-469, Sep. 2013. (IF: 4.138)


Reference and Recent Publications23. Linglong Dai, Zhaocheng Wang, Jun Wang, and Zhixing Yang, “Joint Time-Frequency Channel Estimation for Time

Domain Synchronous OFDM Systems,” IEEE Transactions on Broadcasting, vol. 59, no. 1, pp. 168-173, Mar. 2013.

24. Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Spectrally Efficient Time-Frequency Training OFDM for Mobile Large-Scale MIMO Systems,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 2, pp. 251-263, Feb. 2013. (IF: 4.138)

25. Linglong Dai, Chao Zhang, Zhengyuan Xu, and Zhaocheng Wang, “Spectrum-Efficient Coherent Optical OFDM for Transport Networks,” IEEE Journal on Selected Areas in Communications, vol. 31, no. 1, pp. 62-74, Jan. 2013. (IF: 4.138)

26. Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Time-Frequency Training OFDM with High Spectral Efficiency and Reliable Performance in High Speed Environments,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 4, pp. 695-707, May 2012. (IF: 4.138)

27. Linglong Dai, Zhaocheng Wang, and Zhixing Yang, “Next-Generation Digital Television Terrestrial Broadcasting Systems: Key Technologies and Research Trends,” IEEE Communications Magazine, vol. 50, no. 6, pp. 150-158, Jun. 2012. (IF: 4.460)

28. Linglong Dai, Zhaocheng Wang, Changyong Pan, and Sheng Chen, “Wireless Positioning Using TDS-OFDM Signals in Single-Frequency Networks,” IEEE Transactions on Broadcasting, vol. 58, no. 2, pp. 236-246, Jun. 2012.

29. Linglong Dai, Zhaocheng Wang, and Jian Song, “TDS-OFDMA: A Novel Multiple Access System Based on TDS-OFDM,” IEEE Transactions on Consumer Electronics, vol. 57, no. 4, pp. 1528-1534, Nov. 2011.

30. Linglong Dai, Zhaocheng Wang, and Cheng Shen, “A Novel Uplink Multiple Access Scheme Based on TDS-FDMA,” IEEE Transactions on Wireless Communications, vol. 10, no. 3, pp. 757-761, Mar. 2011.


Jiayi Zhang, Zhen Gao, Xudong Zhu, Wenqian Shen, Xinyu Gao

MOST (973, 863), NSFC

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