Robust Transceiver Design for Reciprocal M*N Interference Channel Based on Statistical Linearization Approximation

TL;DR

This paper proposes two robust transceiver algorithms for MIMO interference channels that improve throughput under imperfect CSI by maximizing expected SINR and minimizing SINR variance, using statistical linearization.

Contribution

It introduces novel algorithms leveraging statistical linearization to enhance MIMO interference channel performance with imperfect CSI.

Findings

Algorithms outperform non-robust designs in simulations

Variance minimization improves stability under CSI errors

Reciprocity-based optimization enhances sum rate performance

Abstract

This paper focuses on robust transceiver design for throughput enhancement on the interference channel (IC), under imperfect channel state information (CSI). In this paper, two algorithms are proposed to improve the throughput of the multi-input multi-output (MIMO) IC. Each transmitter and receiver has respectively M and N antennas and IC operates in a time division duplex mode. In the first proposed algorithm, each transceiver adjusts its filter to maximize the expected value of signal-to-interference-plus-noise ratio (SINR). On the other hand, the second algorithm tries to minimize the variances of the SINRs to hedge against the variability due to CSI error. Taylor expansion is exploited to approximate the effect of CSI imperfection on mean and variance. The proposed robust algorithms utilize the reciprocity of wireless networks to optimize the estimated statistical properties in two different working modes. Monte Carlo simulations are employed to investigate sum rate performance of the proposed algorithms and the advantage of incorporating variation minimization into the transceiver design.