Optimizing training and feedback for MIMO interference alignment

Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel´s maximum multiplexing gain. Realizing the gains of IA, however, is contingent upon efficiently providing the transmitters with the accurate channel knowledge needed for alignment. In this paper we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired by training and analog feedback. We derive throughput-maximizing training and feedback resource allocations accounting for estimation error, training and feedback overhead, and channel selectivity. We accurately characterize the effective sum rate with overhead in relation to various parameters such as signal-to-noise ratio and Doppler spread. We show that the overhead of IA can be optimized to ensure good performance in a wide range of fading scenarios.