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

1. 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

2. 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. Successive Interference Cancellation

3. 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.

4. Roadmap • Problem motivation & Contributions • Background • SIC • Problem formulation • Our approach • Evaluations • Conclusions

5. 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.

6. 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

7. Roadmap • Problem motivation & Contributions • Background • SIC • Problem formulation • Our approach • Evaluations • Conclusions

8. Node 4 Node 2 Node 1 Node 3 SIC • Spatial multiplexing enables multi-user MIMO with SIC SIC tries to remove first the stronger interferers and then decode the weaker intended signal. SIC

9. Models • Selection diversity and SIC • Two kinds of interferers • Strong: signal strength higher than the intended • Weak: signal strength weaker than the intended • Path loss and multipath • htr follows Rayleigh distribution, α is the path exponent, P the transmission power

10. 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

11. Dealing with Strong Interferers dBm Strongest interferer P1 P1/(N+P2+P3+….+Pk) > γ P2/(N+P3+P4+….+Pk) > γ Second strongest interferer P2 … Intended signal ((k-1) strongest) Pk-1 Pk-1/(N+Pk) > γ SUCCESFUL DECODING !! k stronger interferer (weak) Pk Compact rule: Iteratively for correct decoding on link (y,z), there must be at most one interferer u, with the following interfering power:

12. 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. b(u,v) a(x,y) Pxv u v x y Pxy Puy Puv

13. Problem Formulation ... • V1’ V2’ … Vk’ = V’ • TDMA scheme • In every time slot: ALOHA – like access with probability of failure at most δ. • Objective: minimize m Time Slot 1 V1’ links Time Slot 2 V2’ links Time Slot m Vm’ links NP - Hard

14. Roadmap • Problem motivation & Contributions • Background • SIC • Problem formulation • Our approach • Evaluations • Conclusions

15. 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 decoding • Add links that can be decoded with SIC • Try to pack more links among those already scheduled

16. 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.

17. D-MUSIC Transmitter Receiver Overhearing Nodes

18. Roadmap • Problem motivation & Contributions • Background • SIC • Problem formulation • Our approach • Evaluations • Conclusions

19. 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

20. Evaluation results • MUSIC is efficient in terms of number of time slots formed • Density does not significantly decrease the probability of successful decoding

21. 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

22. Roadmap • Problem motivation & Contributions • Background • SIC • Problem formulation • Our approach: C-MUSIC • Evaluations • Conclusions

23. 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

24. THANK YOU ! QUESTIONS?