A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks

TLDR

Recent results establish the optimality of interference alignment to approach the Shannon capacity of interference networks at high SNR, yet the extent of alignment over finite signalling dimensions and the requirement of global channel knowledge remain unknown. The study aims to provide iterative algorithms that use network reciprocity to achieve interference alignment with only local channel knowledge at each node. The algorithms iteratively adjust transmit and receive beamformers based on reciprocal channel measurements to align interference. Numerical results demonstrate the feasibility of interference alignment under local knowledge constraints and offer insights not yet available theoretically.

Abstract

Recent results establish the optimality of interference alignment to approach the Shannon capacity of interference networks at high SNR. However, the extent to which interference can be aligned over a finite number of signalling dimensions remains unknown. Another important concern for interference alignment schemes is the requirement of global channel knowledge. In this work, we provide examples of iterative algorithms that utilize the reciprocity of wireless networks to achieve interference alignment with only local channel knowledge at each node. These algorithms also provide numerical insights into the feasibility of interference alignment that are not yet available in theory.