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 n 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. 2 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. 3

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

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. 5

Background Selective diversity at T x  Feedback from R x to T x 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 6

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

SIC Spatial multiplexing enables multi-user MIMO with SIC 8 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 intended  Weak: signal strength weaker than the intended Path loss and multipath  h tr follows Rayleigh distribution, α is the path exponent, P the transmission power 9

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 10

Dealing with Strong Interferers 11 dBm Strongest interferer P 1 P 1 /(N+P 2 +P 3 +….+P k ) > γ Second strongest interferer P 2 … Intended signal ((k-1) strongest) P k- 1 k stronger interferer (weak) P k P 2 /(N+P 3 +P 4 +….+P k ) > γ P k-1 /(N+P k ) > 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:

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. 12 uv xy a(x,y) b(u,v) P xy P uv P xv P uy

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

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

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 15

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 17 Transmitter Receiver Overhearing Nodes

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

Simulation Set Up OPNET simulations Traffic load: 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 20 OptimalC-MUSICD-MUSIC

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 21

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

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 23

THANK YOU ! QUESTIONS? 24