Topology Control for Effective Interference Cancellation in Multi-User MIMO Networks

E. Gelal, K. Pelechrinis, T.S. Kim, I. Broustis Srikanth V. Krishnamurthy, B. Rao

IEEE INFOCOM 2010

Problem Motivation & Contributions

• MIMO communications are becoming prevalent Multiple antenna elements robust links

• 802.11n utilizes MIMO PHY CSMA/CA no exploitation of MIMO capabilities At most one transmission each time instance

• How can we realize multi-user MIMO communications?• Precoding techniques can be used

Accurate channel estimation, feedback from receiver.

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Successive Interference Cancellation

Problem Motivation & Contributions

• We design MUSIC (Multi-User MIMO with Successive Interference Cancellation) Uses SIC for enabling Multi-user MIMO communications

• Centralized and distributed approaches• Evaluation on a variety of settings

Our approach scales and the decoding error probability is bounded

MUSIC outperforms DoF approaches.

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Roadmap

• Problem motivation & Contributions• Background• SIC

Problem formulation• Our approach• Evaluations• Conclusions

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Background

• Multi-user MIMO Precoding techniques

Tx sends pilot signals Rx receives pilot signals channel coefficients estimation Rx feedbacks channel coefficients to Tx Tx assigns weights at the antennas

Successive Interference Cancellation (SIC) Receiver iteratively extracts high interfering signals SINR requirement should be satisfied for every interferer.

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Background

• Selective diversity at Tx Feedback from Rx to Tx for the best transmission element One element used for subsequent transmission Feedback is required less often than with precoding

• Degrees of Freedom = k #antenna elements = k k simultaneous transmissions are possible

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Roadmap

• Problem motivation & Contributions• Background• SIC

Problem formulation• Our approach• Evaluations• Conclusions

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SIC

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Node 1

Node 2

Node 3

Node 4

SIC

SIC tries to remove first the stronger interferers and then decode the weaker intended signal.

Models

• Selection diversity and SIC• Two kinds of interferers

Strong: signal strength higher than the intendedWeak: signal strength weaker than the intended

• Path loss and multipath

htr follows Rayleigh distribution, α is the path exponent, P the transmission power

9€

Ptr = P | htr |2

dtrα

Dealing with Weak Interferers

• Maximum weak interference tolerated on link (u,v):

• We want to assure that:

• Assuming all interferers at the same distance as of the strongest one Aggregate weak interference follows Erlang distribution with parameters n: number of intreferers σ: variance of the Rayleigh distributed variable h

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€

Puvγ

−N

€

Pr{ Pzv > Puvγ

−N} < δz≠u∑

Dealing with Strong Interferers

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dBmStrongest interferer P1 P1/(N+P2+P3+….+Pk) > γ

Second strongest interferer P2

…Intended signal ((k-1) strongest) Pk-1

k stronger interferer (weak) Pk

P2/(N+P3+P4+….+Pk) > γ

Pk-1/(N+Pk) > γ SUCCESFUL DECODING !!

Compact rule: Iteratively for correct decoding on link (y,z), there must be at most one interferer u, with the following interfering power:

€

Puz ≥ Pyz(1+ γ )r,r ∈ {1,2,3,...,k −1}

Problem Formulation

• Interference Graph, Directed, edge and vertex weighted V’ : set of links, with weight the mean value of the received

signal strength E’ : set of directed edges among the links/vertices, with

weight the mean value of interference among the links connected.

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€

G'= (V ',E ',wV ',wE ' )

u v

x y

a(x,y) b(u,v)

Pxy

Puv

Pxv

Puy

Problem Formulation

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Time Slot 1 V1

’ linksTime Slot 2 V2

’ linksTime Slot m Vm

’ links

€

∪

€

∪

NP - Hard

Roadmap

• Problem motivation & Contributions• Background• SIC

Problem formulation• Our approach• Evaluations• Conclusions

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C-MUSIC

• The centralized algorithm is iterative.• Global knowledge of the topology• Main steps

Priority to links not scheduled Include links that do not require SIC for

decodingAdd links that can be decoded with SIC Try to pack more links among those already

scheduled15

C-MUSIC

• Two interfering links cannot belong to the same sub-topology if: The weak interferer causes more interference

than the weak interference budget The strong interference cannot be removed The two links have the same transmitter

(selection diversity) A node is the transmitter for one of the links

and a receiver for the other.16

D-MUSIC

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Transmitter Receiver

Overhearing Nodes

Roadmap

• Problem motivation & Contributions• Background• SIC

Problem formulation• Our approach• Evaluations• Conclusions

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Simulation Set Up

• OPNET simulations• Traffic load: 10-30 pkt/sec, 1500 bytes packets• Path loss (α=4) and Rayleigh fading• Simulations with different

Node density, SINR requirement, number of antenna elements

• Metrics of interest: Number of time slots, average decoding success probability,

throughput• Comparison with:

Optimal (small topologies), DoF based topology control 19

Evaluation results

• MUSIC is efficient in terms of number of time slots formed

• Density does not significantly decrease the probability of successful decoding

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Optimal C-MUSIC D-MUSIC7.83 9.18 9.64

Evaluation results

• DoF based link activation cannot effectively exploit the benefits of multi-user MIMO DoF-based link activation leads to more decoding errors

MUSIC provides better throughput as compared with DoF

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Roadmap

• Problem motivation & Contributions• Background• SIC

Problem formulation• Our approach: C-MUSIC• Evaluations• Conclusions

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Conclusions

• Identify the conditions for SIC to allow multi-packet reception in multi-user MIMO settings.

• Design a framework for exploiting SIC• Demonstrate through simulations the

applicability of our approach

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