Full-Duplex MIMO Systems with Hardware Limitations and Imperfect Channel Estimation

TL;DR

This paper proposes a novel MMSE-based joint digital precoder and combiner design for full-duplex MIMO systems that effectively cancels self-interference despite hardware limitations and imperfect channel knowledge.

Contribution

It introduces a new joint design method combining gradient projection and machine learning concepts to address non-convex SI cancellation optimization.

Findings

The proposed algorithm effectively cancels self-interference in full-duplex MIMO systems.

Simulation results demonstrate improved SI cancellation performance.

The method handles hardware impairments and imperfect CSI effectively.

Abstract

We consider a bidirectional in-band full-duplex (FD) multiple-input multiple-output (MIMO) system subject to imperfect channel state information (CSI), hardware distortion, and limited analog cancellation capability as well as the self-interference (SI) power requirement at the receiver analog domain so as to avoid the saturation of low noise amplifier (LNA). A novel minimum mean square error (MMSE)-based joint design of digital precoder and combiner for SI cancellation is offered, which combines the well-known gradient projection method and non-monotonicity considered in recent machine-learning literature in order to tackle the non-convexity of the optimization problem formulated in this article. Simulation results illustrate the effectiveness of the proposed SI cancellation algorithm.