On the Overhead of Interference Alignment: Training, Feedback, and Cooperation


Omar El Ayach, Angel Lozano, Robert W. Heath, Jr.


submitted to IEEE Transactions on Wireless Communications, April 2012


Interference alignment (IA) is a cooperative transmission strategy that, under some conditions, achieves the interference channel's maximum number of degrees of freedom. Realizing IA gains, however, is contingent upon providing transmitters with sufficiently accurate channel knowledge. In this paper we study the performance of IA in multiple-input multiple-output systems where channel knowledge is acquired through training and analog feedback. We design the training and feedback system to maximize IA's effective sum-rate: a non-asymptotic performance metric that accounts for estimation error, training and feedback overhead, and channel selectivity. We characterize effective sum-rate with overhead in relation to various parameters such as signal-to-noise ratio, Doppler spread, and feedback channel quality. We show that the overhead of IA can be optimized to ensure good performance in a wide range of fading scenarios. Finally, we show how this analysis can help solve network design problems such as finding the optimal number of cooperative IA users based on signal power and mobility.


A preprint of the paper can be found on ArXiv.