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Downlink Multi-User MIMO for IEEE 802.16m Sivakishore Reddy Naga Sekhar Centre of Excellence in Wireless Technology 2013 Outline 1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions Sivakishore Reddy Naga

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Downlink Multi-User MIMO for IEEE 802.16m

Sivakishore Reddy Naga Sekhar

Centre of Excellence in Wireless Technology

2013

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 2 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 3 / 40

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MIMO Introduction

Figure: MIMO Systems

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 4 / 40

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SU-MIMO vs MU-MIMO

Single User MIMO : Promises reliability and channel capacity through diversity gain and rate maximization. Mainly acts as physical(PHY) layer performance booster. Multi User MIMO : Uses spatial degrees of freedom to schedule multiple users to simultaneously share the spatial channel. Optimum design strategy to increase the system capacity.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 5 / 40

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Requirements of MU-MIMO :

Requires Channel State Information.

Partial CSI. Codebook based precoding.

Requires more complex scheduling algorithms. Requires advanced transceiver methodologies.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 6 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 7 / 40

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System model

. . . User 2 data User1 data x x . . . x K

2 1

. . . . . . . . . 1 N 1 N 1 N PRECODER (W) 1 2 User 1 User 2 User K . . N PF SCHEDULER User N data x1 x2

^ ^ ^

xK t r r r u . . . . . Feedback: CQI, PMI

Figure: MU-MIMO System model

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 8 / 40

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Notation

Nt : No.of antennas at the BS. Nr : No.of antennas at the MS/user. Nu : No.of users contending for resource unit. K : No.of users served per resource unit. H : Nr × Nt desired channel matrix. G : Nr × Nt interfering co-channel matrix. W : Nt × K precoder. v : Nt × 1 precoding vector chosen from codebook. I : Number of strong interferers.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 9 / 40

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About Codebook based precoding

In 16m Codebook is defined for a given MIMO setup.

W =

  • v1 v2 . . . vK
  • Nt×K

The Nr × 1 received signal vector at the kth MS : yk = HkWx + nk , k ∈ {1, 2, . . . , K} =

  • P

K Hkvkxk +

  • P

K

K

  • i=k,i=1

Hkvixi + nk Desired signal IUI Noise x =

P K x1

  • P

K x2

. . .

  • P

K xK

T

K×1

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 10 / 40

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CL-MUMIMO PROBLEM STATEMENT

How should each user make his choice for the PMI? How should each user model his CQI? How should the BS use this feedback-info to schedule multiple users per data region?

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 11 / 40

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Choose PMI to minimize MSE

MUMIMO Equation : yk = HkWx +

I

  • i=1

GiW′

izi + nk

MSE for ith precoding vector is : MSEi = E(xs − ˆ xs)2 = σ2

xs − v∗ i H∗ k

  • HkWW∗H∗

k + Icov + σ2I

−1 Hkvi Icov =

I

  • i=1

GiW′

iW′∗ i G∗ i

If W is unitary, MSEi = σ2

xs − v∗ i H∗ k

  • HkH∗

k + Icov + σ2I

−1 Hkvi PMI = arg min

i

  • ∀subcarriers

MSEi

  • Sivakishore Reddy Naga Sekhar

(CEWiT) MU-MIMO for IEEE 802.16m 2013 12 / 40

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CQI modeling

Receiver uses an MMSE filter b, b∗

kyk

= b∗

kHkvkxk + K

  • i=k,i=1

b∗

kHkvixi + I

  • i=1

b∗

kGiW′ izi + b∗ knk

CQI = |b∗

kHkvk|2

K

i=k,i=1 |b∗ kHkvk|2 + b∗ k(Icov + σ2I)bk

where Icov = I

i=1 GiW′ iW′∗ i G∗ i = I i=1 GiG∗ i

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 13 / 40

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MUMIMO PF-Scheduler

Find all possible pairs of users who have reported orthogonal PMI. Find sum-PF-metric for each pair. Schedule pair with maximum sum-PF-metric.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 14 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 15 / 40

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Unitary vs Non-Unitary Precoders

The covariance term which influence CQI : Icov =

I

  • i=1

GiW′

iW′∗ i G∗ i

If the precoder W is not unitary, the interference level may change from one frame to another frame as the precoders used changes. So the CQI of 2x2 is stable compared to 4x2 as the precoders of 4x2 system are non-unitary.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 16 / 40

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CQI Stability

−10 −5 5 10 15 0.2 0.4 0.6 0.8 1 CQI(n−1)−PPSINR(n) in dB Histogram Comparision of Histograms 2x2 4x2

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 17 / 40

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2x2 vs 4x2

500 1000 1500 2000 2500 3000 3500 4000 4500 0.2 0.4 0.6 0.8 1 User Throughput (in kbps) CDF CDF of User Throughputs 2x2 Ideal Icov 4x2 Ideal Icov

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 18 / 40

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Practical Implications in Closed loop system

The mimo modes of interfering users can change in closed loop region. The calculation of CQI which involves Icov cannot be perfectly determined. The PMI decision term also involves Icov is also not perfect. In demodulation, if the mimo mode of CCI is different it is difficult to even estimate the interference covariance at the reciever. Thus degrading the performance further. The PMI and CQI fedback are IMPERFECT and reduces the capacity.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 19 / 40

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CL-MUMIMO

500 1000 1500 2000 2500 3000 3500 4000 4500 0.2 0.4 0.6 0.8 1 User Throughput (in kbps) CDF CDF of User Throughputs 2x2 Ideal Icov 2x2 Scaled Icov 4x2 Ideal Icov 4x2 Scaled Icov Practical 4x2 Practical 2x2

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 20 / 40

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CL-MU-MIMO vs OL-SU-MIMO

5 10 15 20 25 30 35 40 45 50 1.8 2 2.2 2.4 2.6 2.8 3 Sector Spectral Efficiency in bps/Hz #Active users per sector OL−SU−MIMO,2x2 CL−MU−MIMO,2x2

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 21 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 22 / 40

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Open Loop MU-MIMO System Model

. . . User 2 data User1 data x x . . . x K

2 1

. . . . . . . . . 1 N 1 N 1 N PRECODER (W) 1 2 User 1 User 2 User K . . N PF SCHEDULER User N data x1 x2

^ ^ ^

xK t r r r u . . . . . Feedback: CQI, PSI

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 23 / 40

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Open Loop Multi-User MIMO

Precoders are fixed a priori.

W =

  • v1 v2 . . . vK
  • Nt×K

Each user feedbacks preferred stream index (PSI) and CQI. PSI ∈ {1, 2, . . . , K}. PF scheduler will serve set of K users who feedback PSIs {1, 2, . . . , K}.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 24 / 40

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Open Loop Region

Inside Open Loop Region : All the base stations will use same MIMO mode. Creates stable interference environment. Estimation of CCI is easy and accurate. Covariance matrix is calculated using estimated precoded channels.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 25 / 40

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Received signal

The Nr × 1 received signal vector at the kth MS : yk = HkWx +

8

  • i=1

GikWx′

ik + nk ,

k ∈ {1, 2, . . . , K} =

  • P

K Hkvkxk +

  • P

K

K

  • i=k,i=1

Hkvixi +

8

  • i=1

GikWx′

ik + nk

Desired signal IUI CCI Noise x =

P K x1

  • P

K x2

. . .

  • P

K xK

T

K×1

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 26 / 40

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Covariance matrix and MMSE filter

The Covariance matrix of CCI (KCCI) : KCCI = P K

  • 8
  • i=1

GikWW∗G∗

ik

1 × Nr MMSE filter equation for kth user : bk = (Hkvk)∗

  • ˜

Hk ˜ H∗

k + K

P KCCI + KNo P INr −1 where ˜ Hk = HkW.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 27 / 40

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SINR Calculation

ˆ xk = bkyk =

  • P

K bkHkvkxk +

  • P

K

K

  • i=k,i=1

bkHkvixi +

8

  • i=1

bkGikWx′

ik + bknk

SINRk = P

K

  • |bkHkvk|2

IUIk + CCIk+bk2No IUIk = P K

  • K
  • i=k,i=1

|bkHkvi|2 CCIk = bkKCCIb∗

k,l

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 28 / 40

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CQI and PSI Computation

W =

  • v1

v2 . . . vK

  • Nt×K

SINRk,l = P

K

  • |bkHkvl|2

IUIl + CCIk+bk2No PSIk = arg max

l

SINRk,l CQIk = SINRk,P SIk

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 29 / 40

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Open Loop Single-User MIMO

Single user with single stream is scheduled. No orthogonal pairing problem. Better Cell edge user performance than MU-MIMO.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 30 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 31 / 40

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OL-MU-MIMO vs OL-SU-MIMO

5 10 15 20 25 30 35 40 45 50 1.8 2 2.2 2.4 2.6 2.8 3 Sector Spectral Efficiency in bps/Hz #Active users per sector OL−MU−MIMO,2x2,K=2 OL−SU−MIMO,2x2

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 32 / 40

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OL-MU-MIMO vs OL-SU-MIMO contd...

5 10 15 20 25 30 35 40 45 50 0.05 0.1 0.15 0.2 0.25 #Active users per sector Cell Edge user spectral efficiency in bps/Hz OL−SU−MIMO,2x2 OL−MU−MIMO,2X2,K=2 CL−MU−MIMO,2X2,K=2

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 33 / 40

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OL-MU-MIMO, CL-MU-MIMO and OL-SU-MIMO Comparison

500 1000 1500 2000 2500 3000 3500 4000 4500 0.2 0.4 0.6 0.8 1 Throughput in Kbps CDF OL−MUMIMO,actual covar CL−MUMIMO,scaled covar OL−SU−MIMO sector SE,cell edge SE CL: 2.3655,0.071 OL: 2.2754,0.062 SU: 2.3857,0.1150 1000frames,tc=3000, 2X2,2 users Doppler=7 Hz

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 34 / 40

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Sector Spectral Efficiency variation for OL-MU-MIMO :

Nu 4 × 4,K = 2 4 × 4,K = 3 4 × 4,K = 4 5 4.1905 3.2062 2.2163 10 4.4942 3.9944 3.0491 50 4.9806 5.2304 5.4579

Table: Sector Spectral Efficiency for 4 × 4 OL-MU-MIMO.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 35 / 40

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Convergence of OL-MU-MIMO and CL-MU-MIMO

When there are large number of active users per sector both OL-MU-MIMO and CL-MU-MIMO will perform similarly.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 36 / 40

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Outline

1 Introduction 2 Closed Loop MU-MIMO 3 Results 4 Open Loop MU-MIMO 5 Results 6 Conclusions

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 37 / 40

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Conclusions

MU-MIMO system achieves higher throughput when compare to single user system. MU-MIMO system has slightly more FER when compare to single user system. Optimum number of users (with single stream per user) that can be scheduled in a dataregion to achieve maximum throughput is equal to min (Nt, Nr). FER and throughput of the system improves as min (Nt, Nr) increases.

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 38 / 40

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THANK YOU

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 39 / 40

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ANY QUESTIONS ?

Sivakishore Reddy Naga Sekhar (CEWiT) MU-MIMO for IEEE 802.16m 2013 40 / 40