On the overhead of interference alignment: training, feedback, and cooperation

Abstract

Interference alignment (IA) is a cooperative transmission strategy that, under some conditions,/nachieves the interference channel’s maximum number of degrees of freedom. Realizing IA gains,/nhowever, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In/nthis paper, we study the performance of IA in multiple-input multiple-output systems where channel/nknowledge is acquired through training and analog feedback.We design the training and feedback system/nto maximize IA’s effective sum-rate: a non-asymptotic performance metric that accounts for estimation/nerror, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with/noverhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback/nchannel quality. A main insight from our analysis is that, by properly designing the CSI acquisition/nprocess, IA can provide good sum-rate performance in a very wide range of fading scenarios. Another/nobservation from our work is that such overhead-aware analysis can help solve a number of practical/nnetwork design problems. To demonstrate the concept of overhead-aware network design, we consider/nthe example problem of finding the optimal number of cooperative IA users based on signal power and/nmobility.