Robust Iterative Interference Alignment for Cellular Networks with Limited Feedback

TL;DR

This paper introduces a robust iterative interference alignment method for cellular networks with limited feedback, providing realistic performance evaluation, new scaling laws, and feedback strategies that improve spectral efficiency in practical scenarios.

Contribution

It proposes a novel interference alignment algorithm and feedback strategies that account for realistic feedback limitations, advancing beyond idealized assumptions in CoMP systems.

Findings

New performance metric better captures throughput degradation

Derived relevant scaling laws for limited feedback

Proposed feedback strategies achieve the scaling laws

Abstract

In theory coordinated multi-point transmission (CoMP) promises vast gains in spectral efficiency. But industrial field trials show rather disappointing throughput gains, whereby the major limiting factor is proper sharing of channel state information. Many recent papers consider this so-called limited feedback problem in the context of CoMP. Usually taking the assumptions: 1) infinite SNR regime, 2) no user selection and 3) ideal link adaptation; rendering the analysis too optimistic. In this paper we make a step forward towards a more realistic assessment of the limited feedback problem by introducing an improved metric for the performance evaluation which better captures the throughput degradation. We find the relevant scaling laws (lower and upper bounds) and how that they are different from existing ones. Moreover, we provide a robust iterative interference alignment algorithm and corresponding feedback strategies achieving the obtained scaling laws. The main idea is that instead of sending the complete channel matrix each user fixes a receive filter and feeds back a quantized version of the effective channel. Finally we underline our findings with simulations for the proposed system.