Approaching the Capacity of Wireless Networks through Distributed Interference Alignment

TLDR

Interference alignment can achieve near‑Shannon capacity at high SNR, yet its effectiveness over limited dimensions and the requirement for global channel knowledge remain unresolved. The authors aim to demonstrate iterative algorithms that exploit network reciprocity to achieve interference alignment using only local channel knowledge. They propose iterative schemes that leverage the reciprocity of wireless links to adjust transmit and receive beamformers based solely on local channel measurements. Numerical results show the feasibility of interference alignment under these local‑knowledge iterative schemes, offering insights beyond current theory.

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.