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presentation on massive MIMO

MM

YS

Massive MIMO:

Fundamentals, Opportunities and Challenges

Erik G. Larsson

June 9, 2013

Div. of Communication SystemsDept. of Electrical Engineering (ISY)

Linkoping UniversityLinkoping, Sweden

www.commsys.isy.liu.se

With thanks to my team and collaborators:

Hien Q. Ngo (LiU, Sweden) Antonios Pitarokoilis (LiU) Hei Victor Cheng (LiU) Daniel Persson (LiU)

Fredrik Rusek (Lund, Sweden) Ove Edfors (Lund) Buon Kiong Lau (Lund) Fredrik Tufvesson (Lund)

Thomas L. Marzetta (Bell Labs/Alcatel-Lucent, USA)

Saif Mohammed (IIT/Delhi, India)

Christoph Studer (Rice Univ., USA)

1/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Massive MIMO

M=

x100

antennas!K

terminals

k=1

k=K

Massive multiuser MIMO (MISO): M K 1 (think 100 10 or 500 50) coherent, but simple, processing

Potential to dramatically improve rate & reliability

Potential to drastically scale down TX power

Not only theory, at least one known testbed (64 10)

2/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Large MIMO Deployment Scenarios

Reduce bulky items (coax) Each antenna unit simple (low accuracy) Resilience against individual failures (hotswapping) Potential economy of scale in manufacturing Grid-free powering

3/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Massive MIMO Operation

Not enough resources for pilots & CSI feedback, so operate in TDD.

On the uplink, acquire CSI from uplink pilots and/or blindly from data detect symbols M K linear processing (MRC, ZF, MMSE) nearly optimal

On the downlink, use CSI obtained on the uplink make necessary adjustments based on reciprocity calibration apply multiuser MIMO precoding simple precoders desirable (and very good!): MRT, ZF, MMSE, ...

MRC/MRT operation intracell interference will appear as noise 1 bps/Hz/terminal; K bps/Hz/terminal total distributed implementation

ZF/MMSE operation can cancel out intra cell interference computationally more demanding

4/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

MRT versus ZF precoding

5/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Massive MIMO Opportunities

Multiplexing gain K, Array power gain M (ideally)

Rely on the law of large numbers average out fast fading and thermal noise

Channel hardens h2/M constant no FD scheduling, full BW to all terminals, simple MAC almost no air-interface latency

In the 1 bps/Hz/t regime, many impairments drown in thermal noise

M K unused degrees of freedom (e.g. 500 50 = 450)

6/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Massive MIMO Challenges and Questions

Multiplexing gain can only materialize if channel responses are(nearly) orthogonal

Array gain relies on coherency getting CSI is the main thing

HW power consumption (RF) must scale fast enough with PHW power consumption (BB) must scale slow enough with M

Scaling down P thermal noise eventually limits performance.

P large enough, interference limits performance intracell interference intercell interference

7/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Limits Imposed by Propagation

8/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Favorable propagation in Point-to-Point MIMO

M K MIMO link H , M K

Favorable propagation (f.p.) if

HHH I 21 = . . . = 2K For H2 = constant, log

I + 1N0HHH max if f.p.

(maxmin

)2= extra power needed to use all eigenmodes

Hmk zero mean & i.i.d., and M K f.p.

9/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Favorable propagation in MU-SIMO

Favorable propagation if

1

MgHi gj 0 for i 6= j

gi2 depends on path loss and shadow fading10/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

I.i.d. channels give favorable propagation

tail no tail

~20 dB ~3 dB

11/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Do we have favorable propagation in practice?

Our partners at Lund Univ., Sweden have conducted uniquemeasurements [RPL2013,GERT2011,GTER2012].

Indoor 128-ant. (4x16 dual-pol.) array. 3 users indoor, 3 outdoor.

2.6 GHz CF, 50 MHz BW, 100 snapshots (10m).

Normalized to retain only small-scale fading.

12/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Lund measurements, example of results

tail no tail

~26 dB ~7dB

13/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Summary

Assumptions on f.p. have substantial support in measurements

I.i.d. model for small-scaled fading appears reasonable

More details and models in F. Tufvessons tutorial next

14/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Limits Imposed by Noise and Intracell Interferencewith Linear Processing

15/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Single-Cell (Noise-Limited) Uplink

M antennas K terminals Power (SNR) per terminal: P i.i.d. Rayleigh fading

Coherence interval: T symbols (e.g. 2 100kHz 1ms = 200) K mutually orthogonal pilots of length (K T )

pilots data

t T-t MMSE channel estimation

16/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Spectral-energy efficiency tradeoff

Sum-spectral efficiency bounds [NLM2013]:

R =

(1

T

)K log2

(1 + (M1)P

2

(K1)P 2+(+K)P+1), for MRC(

1 T

)K log2

(1 + (MK)P

2

(+K)P+1

), for ZF

Energy efficiency: ,R

P SISO system: K = 1,M = 1

maxP,

, s.t. R = const.

17/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

UL, T = 200, SISO reference, M = K = 1

0 10 20 30 40 50 60 70 80 9010

-1

100

101

102

103

104

K = 1, M = 1

20 dB

10 dB

0 dB

-10 dB

Rel

ativ

e En

ergy

-Ef

ficie

ncy

(b

its/J)

/(bits

/J)

Spectral-Efficiency (bits/s/Hz)18/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Spectral-energy efficiency tradeoff, cont.

Sum-spectral efficiency bounds [NLM2013]:

R =

(1

T

)K log2

(1 + (M1)P

2

(K1)P 2+(+K)P+1), for MRC(

1 T

)K log2

(1 + (MK)P

2

(+K)P+1

), for ZF

Energy efficiency: =R

P SISO system: K = 1,M = 1

maxP,

, s.t. R = const.

Single-user SIMO system: K = 1,M = 100

maxP,

, s.t. R = const.

19/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

UL, T = 200, SISO and SU-SIMO

0 10 20 30 40 50 60 70 80 9010

-1

100

101

102

103

104

K=1, M=1 (SISO)

20 dB

10 dB

0 dB

-10 dB

Rel

ativ

e En

ergy

-Ef

ficie

ncy

(b

its/J)

/(bits

/J)

Spectral-Efficiency (bits/s/Hz)

K=1, M=100 (SU-SIMO)

20/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

Spectral-energy efficiency tradeoff, cont. Sum-spectral efficiency bounds [NLM2013]:

R =

(1

T

)K log2

(1 + (M1)P

2

(K1)P 2+(+K)P+1), for MRC(

1 T

)K log2

(1 + (MK)P

2

(+K)P+1

), for ZF

Energy efficiency: =R

P SISO system: K = 1,M = 1

maxP,

, s.t. R = const.

Single-user SIMO system: K = 1,M = 100

maxP,

, s.t. R = const.

Multi-user SIMO system: M = 100, K 1 adapted

maxP,,K

, s.t. R = const.

21/44

Erik G. LarssonMassive MIMO: Fundamentals, Opportunities and Challenges

Communication Systems

Linkoping University

UL, T = 200, SISO, SU-SIMO & MU-SIMO

0 10 20 30 40 50 60 70 80 9010

-1

100

101

102

103