Distributed precoding for MISO interference channels with channel mean feedback: Algorithms and analysis

This work focuses on the design and analysis of distributed stochastic precoding algorithms for multiple-input single-output (MISO) interference channels, where each transmitter is provided with mean information of its intended channel and that of interfering channels. Unlike in cases where exact channel gains are known as in most existing works, here generalrank precoding is required for optimality instead of the rank-one beamforming. An efficient algorithm for the distributed implementation of the Nash equilibrium precoding is first proposed. A sufficient condition for this algorithm to converge to the unique equilibrium is derived for the two-user case based on stochastic ordering, and is valid for a wide range of system parameters. To improve the sum-rate performance under medium to strong interference, a pricing-based algorithm is also provided and its convergence analyzed. The two algorithms are compared in terms of sum-rate and system overhead.