Bargaining and Multi-User Detection in MIMO Interference Networks

We investigate the use of multi-user detection to improve performance in MIMO interference networks. Unfortunately, while multi-user detection often allows higher data rates, it greatly complicates the problem: in addition to choosing a transmit covariance for each transmitter, we must decide which signals each receiver will detect and which data rates make such detection feasible. We discuss methods to optimize the data rates in two ways: maximizing the sum throughput of the network, and choosing rates based on the Kalai-Smorodinsky bargaining solution from cooperative game theory. Simulation results suggest that, while sum-rate maximization yields higher average throughput, the Kalai-Smorodinsky solution provides a superior solution in terms of fairness. The simulations also suggest that multi-user detection significantly improves network performance.