Multiuser MIMO Advances towards Terabit Interactive SatComs

Symeon Chatzinotas

In collaboration with

Gan Zheng, Dimitrios Christopoulos, Bjorn Ottersten

SnT, University of Luxembourg

SatNEx Annual Lecture

ESA-ESTEC, 16 May 2012 1/53

Outline

• Motivation

• Preliminaries – Cooperation, MU-MIMO, Power constraints

• Ground segment – Complexity, Channel Acquisition

• Space Segment – Power, Bandwidth, Clustering, Pattern

• Performance evaluation – FW/RTN, Pattern Design

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Motivation

• Terabit communications

– Multimedia-based content

– Video Coding: HDTV, 3DTV…

– Smartphone cameras (8Mpx)

• Interactive communications

– Internet traffic

– Video streaming / Broadcast losing ground

• Uplink data

– User-generated content (images, videos)

– Cloud services, P2P, file sharing

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Multibeam Architecture

• Based on the cellular concept

– Satellite as data relay (bent-pipe)

– One or multiple GWs with interference-free feeder links

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Performance Metrics

• Spectral Efficiency (b/s/Hz)

• Energy Efficiency (b/s/Hz/W)

• Coverage (b/s/Hz/Km2)

• Handling interference is still the dominant problem!

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Toy Model

• Classic medium access problem

– Interference channel

B1

B2

U1

U2

Interference

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Orthogonalization

• Traditional methods impose resource splitting e.g. cellular concept, TDMA/FDMA/OFDMA, even CDMA

• Single-user links have reached their limits (coding, channel state estimation etc)

B1

B2

U1

U2

AVOID INTERFERENCE

7/53

Cooperation

• Cooperation enables joint signal processing over multiple dimensions

• Transforms into multiuser MIMO (MAC for RTN, BC for FW)

B1

B2

U1

U2

Cooperation

EXPLOIT INTERFERENCE

8/53

Why? RTN case

• Orthogonalization

– Half the resources to begin with

• Cooperation

– Combine two observations for decoding

– Interbeam signals are no longer interference

• Useful in decoding 9/53

Multiuser MIMO Techniques

Non-Iinear / Capacity

Linear / Less Complex

Linear / HSNR

RTN SIC LMMSE ZF

FWD DPC R-ZF ZF

Serial

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FWD Power Constraints

• Sum power

– Duality but unrealistic

• Individual power

– Complicated duality

• Linear

– Constraints over beam sets e.g. MPAs, FlexTWTAs

• Non-linear

– For non-linear regions of TWTA characteristics

11/53

Ground Segment

• Complexity

• Channel acquisition

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Complexity

• Implementation complexity

– Non-linear techniques: complex iterative processing

– Linear techniques: less complex linear processing

• Affordable since:

– Computational resources at GW

– MultiGW: 50-100 beams per GW

• Strong beam separation

– Only directly adjacent beams considered (3-7)

– Reduced dimensionality

13/53

Channel Acquisition

• Pilot-assisted

• Long round-trip delay

– GEO FW: 2*0.25 sec, RTN: 0.25 sec

– Outdated CSI

• FSS channels

– AWGN, Rain fading: Longer coherence time

• MSS

– Shadowing, Multipath: Shorter coherence time

• Worst case: MSS FW

– Recent results for dealing with delayed CSI 14/53

Space Segment

• Power / Bandwidth requirements

• Clustering techniques

• Multibeam pattern design

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Power

• Current satellites

– Not enough TWTAs for full frequency

Solution 1: More satellites Solution 2: Larger satellite

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Bandwidth

• Current satellites – Not enough feeder link bandwidth

• Solution 1: Q/V bands, optical

• Solution 2: Regulatory change – Move spectrum from user to feeder link

• Solution 3: OB-MUD / Large satellite

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Clustering & Pattern

• Clustering

– Chess-like OR neighborhood-based?

• Pattern

– More OR less beam overlap?

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Multibeam Joint Processing in the Forward Link

• Flexible Power Constraints

• Multiple Gateways

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Outline

• System Model

– Linear Beamforming with Generic Power Constraints

– Channel Model

• Problem Formulation

– Rate Balancing, Rate Matching, Sum-rate

• Performance

• Scalability

• MultiGW architectures

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Linear Beamforming

21/53

System Model

• K=7 beams

• One user per beam

• Worst-case user positions

• Average or instantaneous traffic demand

• Performance objective: – Rate Balancing

– Rate Matching

– Throughput Maximization

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Channel Model

• User Channel

• Rain Fading

• Beam Gain

23/53

Problem Formulation

• Linear BF

– User SINR

– User Rate

• Rate Balancing

– with hard ceiling

24/53

Other Objectives

• Throughput Maximization

• Rate matching: n=2

25/53

Generic Power Constraints

• Motivation: Flexible TWTAs, MPAs

• Sum power

– 1 constraint

• Per beam

– K constraints

• Power sharing

– Over N beams

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Parameters

27/53

Rate Balancing

Throughput (GBps)

Conventional 2.57

LBF Individual power

10.75

LFB sum-power 11.58

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Rate Matching – l2 norm

• Better rate matching for proposed scheme compared to (R-)ZF

• Closely follows DPC performance

Throughput (GBps)

Conventional 6.03

ZF 10.05

RZF 10.09

Proposed 10.28

DPC 10.73

29/53

Power Consumption

• Greedy conventional

• (R-)ZF consumes less power

• Proposed scheme power consuming

30/53

Scalability

• Clustered beams: 10,30, 50, 70,100

31/13

Throughput

• Conventional saturates

• Proposed scheme scales well

• ZF, R-ZF slightly degraded

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MultiGW Architecture

• Conventional

– 4-colour

• Clustered MBP

– Intercluster interference

• Partial CSI

– Signal to leakage and noise ratio

• Partial CSI+Data

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Throughput

• CSI sharing – No considerable

gain

• CSI+data – 15%

improvement

• Worst case – 7 beams

– Gain increases with cluster size

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Multibeam Joint Processing in the Return Link

• MultiGW

• Clustering techniques

35/53

Outline

• Scenario

• Clustering & MultiGW

• Channel Model & Capacity Analysis

• Performance

• Conclusions

36/53

Scenario

• Unfair comparison

4 times more HPAs at the

Satellite are necessary

• Beam Clustering:

Co-channel beams are

jointly decoded

37/53

Clustering & MultiGW

• Clustering – Chess-like OR neighborhood-based?

• Each color served by a different GW – No intercluster interference

• Power gain through neighborhood-based – True for terrestrial channel, what about satellite???

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Channel Model

• General Input-Output relationship for the ith beam will read as

• Channel Assumptions: MSS

1. Rician Fading h

2. Beam gain b (Multibeam Satellite)

3. Log-normal Fading ξ (User Mobility)

• Channel Assumptions: FSS 1. Rain Fading h

2. Beam gain b (Multibeam Satellite)

Hence: where

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• Critical Assumption: “Each user experiences the same channel towards all

antennas since the distance between satellite antennas is infinitesimal compared

to the user-satellite propagation path”

• Absence of scatterers near the satellite + long propagation time

Fully correlated received signals at the Satellite

No transmit correlation

• Due to rank deficiencies, channel matrix reduces to:

Channel Model (cont’d)

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• Ergodic sum-rate:

• Analytical bound:

• High SNR formulas

• It all depends on the eigenvalues of BB† !!!

Capacity Analysis

41/53

• Conventional System:

• MMSE Receiver

Capacity Analysis (cnt’d)

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Performance Results

43/53

• Only Full FR achieves

Terabit

• Very small gain, SNR

area [5 25]dB

• 2 fold capacity gain for

large SNR (>40dB)

• Clustering is indifferent

SIC Performance

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• Chess-like clustering

slightly better

• Conjecture: Power gain is

overbalanced from higher

condition number in BBH

• Satellite and terrestrial

channels have different

performance trends

MMSE Performance

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Conclusions

• Marginal gain in the current SNR operation region

• Full FR or higher SNRs needed

• No reason for different clustering in RTN

• What about FWD?

• Can power allocation and precoding make a difference?

• Condition number does not depend only on channel

• Not much hope according to SatNEx results…

46/53

Multibeam Joint Processing and Pattern Design

47/53

Multibeam Pattern

• Beam overlap

– 3dB based on worst-case C/I

• Multibeam processing

– Exploits interbeam signals

– Different design principle needed

• What is the optimal beam overlap?

48/53

• Multibeam antenna: ,

where:

Beam Gain

• 3dB coverage

• Total power is kept

constant!

49/53

FWD Link

• Less overlap is optimal for FWD MJD

• Power allocation for sum-rate – Favours beam-

centre users

• Fairness? – Rate balancing /

matching

50/53

RTN Link

• More overlap optimal for RTN MJD

• No power allocation – Beam-edge users

have to be served

• Wider beams – Larger channel

norm

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References

1. Zheng G., Chatzinotas S., Ottersten B., “Generic Optimization of Linear Precoding in

Multibeam Satellite Systems”, IEEE Transactions on Wireless Communications, vol.PP, no.99,

pp.1-13.

2. Christopoulos D., Chatzinotas S., Zheng G., Grotz, J., Ottersten B., “Linear and non-Linear

Techniques for Multibeam Joint Processing in Satellite Communications”, to appear,

EURASIP Journal on Wireless Communications and Networking.

3. S. Chatzinotas, G. Zheng, B. Ottersten, "Energy-Efficient MMSE Beamforming and Power

Optimization in Multibeam Satellite Systems", Asilomar 2011

4. G. Zheng, S. Chatzinotas, B. Ottersten, “Multi-Gateway Cooperation in Multibeam Satellite

Systems”, submitted to PIMRC 2012 http://arxiv.org/abs/1111.7094

5. Christopoulos D., Chatzinotas S., Ottersten B., “Multibeam Joint Decoding in Satellite

Systems: A fair Comparison over Conventional techniques”, IEEE Global Communications

Conference, Globecom 2012, submitted.

6. Chatzinotas S., Zheng G., Ottersten B., "Joint Precoding with Flexible Power Constraints in

Multibeam Satellite Systems", IEEE Global Communications Conference, Globecom 2011,

Houston, Texas, December 2011.

7. Christopoulos D., Chatzinotas S., Matthaiou M., Ottersten B., "On the Capacity of Multi-Beam

Joint Decoding over Composite Satellite Channels" (Invited Paper), 45th Asilomar

Conference on Signals, Systems and Computers, Asilomar 2011, Monterey, California,

November 2011.

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Thank you!

Questions & Discussion

Contact: Symeon.chatzinotas@uni.lu

http://www.uni.lu/snt/people/symeon_chatzinotas

Multiuser MIMO Advances towards Terabit Interactive SatComs

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