channel estimation and multiple access in massive mimo · pdf filechannel estimation and...

1

Channel Estimation and Multiple Access

in Massive MIMO Systems

Junjie Ma, Chongbin Xu and Li Ping

City University of Hong Kong, Hong Kong

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Li Ping, Lihai Liu, Keying Wu, and W. K. Leung, "Interleave Division

Multiple-Access," IEEE Trans. Wireless Commun., vol. 5, no. 4, pp. 938-947, Apr. 2006.

Peng Wang, Jun Xiao, and Li Ping, "Comparison of Orthogonal and Non-Orthogonal Approaches to Future Wireless Cellular Systems," IEEE Vehicular Technology Magazine, vol. 1, no. 3, pp. 4-11, Sept. 2006.

Junjie Ma and Li Ping, “Data-aided channel estimation in large antenna systems”, IEEE Trans Signal Processing, June 2014.

Chongbin Xu, Peng Wang, Zhonghao Zhang, and Li Ping, "Transmitter design for uplink MIMO systems with antenna correlation," IEEE Trans. Wireless Commun., vol. 14, no. 4, pp. 1772-1784, Apr. 2015.

Main references

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Contents

Introduction

Channel estimation at the BS

Channel estimation at MTs

Multiple access: OFDMA, SDMA, IDMA and NOMA

Conclusions

4

Contents

Introduction




Conclusions

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H. Andoh, M. Sawahashi and F. Adachi, “Channel estimation filter using time-

multiplexed pilot channel for coherent RAKE combining in DS-CDMA mobile

radio”, IEICE Transactions on Communications 81 (7), 1517-1526, 1998.

D. Ishihara, J. Takeda, and F. Adachi, “Iterative channel estimation for

frequency-domain equalization of DSSS signals,” IEICE Trans. Commun., vol.

E90-B, no. 5, pp. 1171–1180, May 2007.

G. Gui, W. Peng and F. Adachi, “Improved adaptive sparse channel estimation

based on the least mean square , ‘IEEE, Wireless Communications and

Networking Conference (WCNC), 2013.

Inspiration from Professor Adachi

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CDMA systems

Professor Adachi is a pioneer in CDMA systems. He made tremendous

contributions in the development of 3G CDMA systems in Japan. In principle,

there is interference among different users in CDMA. Therefore, CDMA can

be also seen as a non-orthogonal multiple access system.

F Adachi, M Sawahashi and H Suda ”Wideband DS-CDMA for next-generation mobile

communications systems”, IEEE Communications Magazine, 1998.

interference among users

7

Iterative channel estimation

Professor Adachi is also a pioneer in iterative channel estimation. He has made

inflation contributions using the frequency domain equalization approach. The

complexity of his approach is surprisingly low, which provides an attractive

option for practice.

D. Ishihara, J. Takeda, and F. Adachi, “Iterative channel estimation for frequency-domain equalization

of DSSS signals,” IEICE Trans. Commun., vol. E90-B, no. 5, pp. 1171–1180, May 2007.

8

SDMA and multi-user gain

The current OFDMA system is orthogonal. How about the future evolution path?

Orthogonal or non-orthogonal? How to optimize multiple access techniques in

massive MIMO environments?

The following is from information theory the capacity for a SDMA system:

sum-rate ~ min(NBS, K × NMT) ∙ log(1+SNR),

where K is the number of users. This gain can be achieved using multi-user

transmission.

Peng Wang, and Li Ping, "On maximum eigenmode beamforming and multi-user gain," IEEE Trans.

Inform. Theory, vol. 57, no. 7, pp. 4170-4186, Jul. 2011.

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However, the accuracy of CSIT is crucial here. This will be the

focus of my talk.

Impact of CSIT

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Assumptions: CSIT for the downlink

For the downlink, decoding is done individually. Accurate CSIT is crucial

so as to avoid interference. The system should be orthogonal.

BS

correlation

channel

MT 1

correlation

MT K

correlation

MT k

correlation

individual detection

joint beamforming

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Assumptions: CSIT for the uplink

For the uplink, accurate CSIT is not crucial. Interference can be suppressed

at the BS via joint detection. The system can be non-orthogonal.

BS

correlation

channel

MT 1

correlation

MT K

correlation

MT k

correlation

joint detection

individual beamforming

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Difference between down-link and up-link

Assume TDD.

Accurate CSIT is possible for the down-link. Therefore down-link can be orthogonal.

Accurate CSIT is difficult for the up-link. Therefore up-link should be non-orthogonal.

interference down-link up-link

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Contents

Introduction




Conclusions

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Accurate channel estimation is crucial at the

BS

Accurate CSIT is required for the downlink.

With TDD, downlink can be estimated at the BS.

Pilot contamination is a serious problem for channel estimation in massive MIMO.

Data aided channel estimation provides an efficient solution to the pilot contamination problem.

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Pilot contamination

In a multi-cell system, the pilot symbols from neighboring cells may

interference each other, which reduces the accuracy of channel estimation. This

effect is refereed to as pilot contamination.

pilot data

user 1

user 2

user 3

user 4

interference

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Pilot contamination and capacity

The received power is seriously affected by pilot contamination.

number of BS antennas

received power with

antenna contamination

number of BS antennas

received SNR with

accurate CSIT

17

Impact of pilot length

Pilot contamination is caused by the correlation among pilots. Its effect reduces

with pilot length as:

In principle, pilot contamination can be mitigated by increasing the pilot

sequence length Jp. In practice, this is not desirable, because increasing Jp will

reduce the effective data rate.

Can we reduce pilot contamination without affecting data rate?

2H

1 2

2H

1 1

E 1

E pJ

p p

p p

Jp pilot symbols Jd data symbols

frame length < channel coherent time

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Data aided channel estimation

The key of the pilot contamination problem is the correlation among pilots.

The data signals have much lower interference (since their length is longer). More

accurate results can be obtained by using data for channel estimation.

pilot data

user 1

user 2

user 3

user 4

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Iterative data aided channel estimation

decoder

data

detector

channel

estimator

1

dy

1 1,py p

1d

1d

1h

pilot symbol data symbols

However, data symbols are not known initially. We can use partially detected

data for channel estimation based an iterative procedure.

Junjie Ma and Li Ping, “Data-aided channel estimation in large antenna systems”, IEEE Trans

Signal Processing, June 2014.

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Data-aided channel estimation

Channel estimation phase:

Data detection phase:

H H

1 1 1 1 1 2 2ˆ + noise h d d h d d h

HH H1 1 11 1 1 2

1 1 2H H H

1 1 1 1 1 1

ˆ ˆˆ ˆnoise

ˆ ˆ ˆ ˆ ˆ ˆ

jd j d j d j

h h hh y h h

h h h h h h

self contamination cross contamination

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Cross-contamination

Cross contamination is now caused by the correlation among data rather

than pilot.

The cross-contamination effect is inversely proportional

• to data length Jd , and

• also to the a priori reliability (1-vd. ).

H

1 2

H HH1

H H

1 1 11 1

1 2 2 2ˆ other terms

0 when .ˆ ˆ other terms

N

d d hh h

d d h hh h

h

2H

1 2

2H

1 1

E 1.

1E d dJ v

d d

d d

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Self-contamination

When d1 is not perfectly known, and are NOT independent.

Such dependency can be modelled as

where z is independent of and is a random variable. Then

Hence self-interference does not vanish when N. We call this effect

“self-contamination”

For the pilot based scheme, =1 and no such effect exists.

1 1ˆh h1h

1 1 1ˆ ˆ1 z h h h

H H H1 1 1 1 1 1

H H

1 1 1 1

ˆ ˆ ˆ ˆ ˆ11 ,when

ˆ ˆ ˆ ˆN

z

h h h h h h

h h h h

1h

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Impact of correlation among pilots

Cross contamination:

Self-contamination: (unique to a data-aided scheme)

• Both are inversely proportional to Jd

• When vd = 0, self-contamination vanishes while cross contamination

converges to a positive constant.

2H

21 2

2

2 2H

11 1

ˆE 1

ˆ ˆ 1E d dJ v

h h

h h

β1: large scale factor of h1;

β2: large scale factor of h2

2H

1 1 1

2H

1 1

ˆ ˆE

ˆ ˆ 1E

d

d d

v

J v

h h h

h h

Junjie Ma and Li Ping, “Data-aided channel estimation in large antenna systems”, IEEE Trans

Signal Processing, June 2014.

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SINR performance

Consider an L-cell System. Each cell contains one user

• Contamination induced distortion 1/Jd

• Conventional cross-cell interference 1/M

4

1

2 2 2H H

1 1 1 1 1 0

1

noiseself-interferencecross-interfernece

2 2

11 0 1

1 1

contamination

ˆESINR

ˆ ˆ ˆ ˆE E E

1

/ 1/ /

1 1

L

i

i

L Ld i

i

i id x d

N

vN

v v J

h

h h h h h h

conventional interference

1

M

=

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Simulation results: 1 user per cell B

ER

SNR (dB)

conventional pilot-based

SVD blind estimation

data-aided 4 iterations

perfect CSI

-4 -2 0 2 4 6

10-4

10-3

10-2

10-1

100

10-5

r = 1

simulation

prediction

r = 100

r = 1

r = 1

strong interference even

for an extremely high

pilot power

power of pilot symbol

power of data symbolrr =

Settings: 1=1, i=0.2 for i1, N=128, Jp=1, 64-QAM. {i} are large scale

fading factors. The SVD method is from the following reference.

R. R. Muller, et al, “Blind Pilot Decontamination”, IEEE Journal of Selected Topics in Signal

Processing on Signal Processing for Large-Scale MIMO Communications, 2013.

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Contents

Introduction




Conclusions

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Accurate channel estimation is not crucial at

MTs

Coarse statistical channel information, such as a covariance matrix, is sufficient in the up-link massive MIMO.

Without accurate CSIT, interference is inevitable. IDMA is an efficient interference cancelation technique, and hence a natural way to realize NOMA.


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Statistical channel information

Statistical channel information refers to partial knowledge of the channel.

A typical case of statistical channel information is a correlation matrix

containing the power distribution of on different eigen-directions. It does not

contain phase information.

A correlation matrix can be obtained by taking cross-correlation of the received

signals on different antennas. It changes slowly and can be estimated with

much lower cost (compared with full CSIT). The overheads related to CSIT

(such computational cost and pilot usage) can be greatly reduced in this way.

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Non-orthogonal mode transmission

Statistical channel information, only partial CSIT is available. The system is characterized by the following properties.

Partial CSIT is very useful. It still can provide close to optimal performance.

The channel cannot be fully orthogonalized. There is interference among different users. This leads to the non-orthogonal mode transmission.

Interference cancelation techniques are required to suppress in this case.

initial interference

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Generalized NOMA

Non-orthogonal scheme are necessary to achieve the ultimate multi-user capacity. The advantage of NOMA becomes noticeable in the high rate regime.

0

1

2

3

4

5

6

7

8

9

-1 1 3 5 7 9 11 13 15 17 19 21

K=1

K=2

K=4 K=8

K=∞

4×4 MIMO

single cell

equal rate for all users

K is the number of

users

sum power (dB)

sum rate

S Tomida and K Higuchi “Non-orthogonal access with SIC in cellular downlink for user fairness enhancement” ISPACS 2011.

Peng Wang, Jun Xiao, and Li Ping, "Comparison of orthogonal and non-orthogonal approaches to future wireless cellular systems," IEEE Vehicular Technology Magazine, Sept. 2006.

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Mutual information analysis


NBS/NMT = 4

FCSIT = full CSIT

SWF = statistical water filing

NP = no precoding (no-CSIT)

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CSIT for the uplink

For the uplink, beamforming is individually done, so approximate CSIT is

sufficient to ensure good performance. Decoding can be jointly and

interference can be suppressed.

BS

correlation

channel

MT 1

correlation

MT K

correlation

MT k

correlation

joint detection

individual beamforming

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Joint detection via IDMA

IDMA is a low-cost technique that facilitates joint detection.

With IDMA, the signals are separated by user-specific interleavers. Channel

estimation, MUD and decoding can be performed jointly and iteratively in an

IDMA receiver.

decoder

MUD

channel

estimator

1

dy

1 1,py p

1d

1d

1h

Li Ping, Lihai Liu, Keying Wu, and W. K. Leung, "Interleave Division Multiple-Access," IEEE

Trans. Wireless Commun., vol. 5, no. 4, pp. 938-947, Apr. 2006.

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IDMA performance

With a large number of users, conventional MRC detection performs poorly.

Iterative IDMA detection is a low-cost, high performance option.

64 BS antennas

16 users

total rate = 16

IDMA

MRC

-20 -19 -18 -17 -16 -15 -14 -13 -12

10-4

10-3

10-2

10-1

100

Eb/N0(dB)

BER

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Contents

Introduction




Conclusions

36

Overview

OFDMA is suboptimal for MIMO. Orthogonal SDMA can do better but is still

sub-optimal. In general, any orthogonal scheme is suboptimal for MIMO.

Theoretically, NOMA can achieve MIMO capacity but, practically, interference

can still be a problem.

IDMA is a low-cost interference cancelation technique, and hence a natural

way to realize SDMA and NOMA.

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Balanced and unbalanced MIMO

Ideally, we want a true MIMO system, with large numbers of antennas at both

ends. Such a setting can provide a huge rate gain.

In practice, however, we can only mount a limited number of antennas on a

handset. Such a setting can achieve large power gain but not rate gain.

rate

N

power

N

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OFDMA

In conventional OFDMA, only one user is allowed to transmit on each subcarrier

at a given time in a cell. OFDMA with massive MIMO achieves good power gain.

However, the related rate gain is less impressive.

Can we do better?

rate

N

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Space-division multiple access (SDMA)

In massive MIMO, more users can be supported using the SDMA via ZF.

sum-rate ~ min(NBS, K × NMT) ∙ log(1+SNR).

However, ZF requires accurate CSIT.

rate

N

K=1

K=2

K=3

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IDMA and NOMA

IDMA does not require accurate CSIT in the upper link. It can acquire and refine channel information iteratively.

SDMA-IDMA thus provides a robust implement technique NOMA.

S Tomida and K Higuchi “Non-orthogonal access with SIC in cellular downlink for user fairness enhancement” ISPACS 2011.

Peng Wang, Jun Xiao, and Li Ping, "Comparison of orthogonal and non-orthogonal approaches to future wireless cellular systems," IEEE Vehicular Technology Magazine, Sept. 2006.

Y Chen, J Schaepperle and T Wild, “Comparing IDMA and NOMA with superimposed pilots based channel estimation in uplink” PIMRC 2015.

initial interference

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Multi-user (non-orthogonal) gain

We can view IDMA-SDMA as a NOMA or a non-ideal SDMA. With iterative

processing, it can potentially provide the similar gain as ideal SDMA.

sum-rate ~ min(NBS, K × NMT) ∙ log(1+SNR).

.

rate

N

K=1

K=2

K=∞

Peng Wang, and Li Ping, "On maximum eigenmode beamforming and multi-user gain," IEEE Trans.

Inform. Theory, vol. 57, no. 7, pp. 4170-4186, Jul. 2011.

initial interference among users

42

Simulation Results: 4 Users per Cell

conventional pilot-based

SVD blind estimation

data-aided: 4th iteration

-10 -8 -6 -4 -2 0 2 410

-4

10-3

10-2

10-1

100

BE

R

SNR (dB)

Settings: 1=1, i=0.2 for i1. Jp=1. 64-QAM. Other parameters are the

same as the previous figure. {i} are large scale fading factors.

43

Contents

Introduction




Conclusions

44

Conclusions

Down-link requires accurate CSIT.

Pilot contamination is a problem in this case, but it can be mitigated by a

data aided channel estimation technique.

Up-link requires only coarse statistical CSIT, provided that iterative

detection is used at the BS.

OFDMA is suboptimal for massive MIMO. Orthogonal SDMA can do

better but is still sub-optimal. Theoretically, NOMA can achieve MIMO

capacity but, practically, interference can be a problem. IDMA is a low-cost

interference cancelation technique, and hence a natural way to realize

SDMA and NOMA.

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Pilot overhead

pilot data

An challenge how to design pilots for NOMA? In a massive MIMO system

with many users, it can be very costly to allocate orthogonal positions to all

users.

user 5

user 1

user 2

user 3

user 4

user 6

user 7

user 8

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Superimposed pilots

pilot and data

This problem can be resolved in SDMA-IDMA using superimposed pilots. This

technique is discussed in the paper below and we are current working on this

issue.

Chulong Liang, Junjie Ma and Li Ping, “Rate maximization for data-aided channel estimation in multi-user large antenna systems”, under preparation.

user 1

user 2

user 3

user 4

user 5

user 6

user 7

user 8

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