Robust Transceiver Design for IRS-Assisted Cascaded MIMO Systems

TL;DR

This paper presents a novel robust transceiver design for IRS-assisted MIMO multi-hop systems that effectively cancels residual self-interference, adapts to system uncertainties, and optimizes throughput in worst-case RSI scenarios.

Contribution

It introduces the first IRS-based RSI cancellation method for MIMO full-duplex relay systems, with new optimization techniques and insights on antenna and IRS element allocation.

Findings

IRS can effectively cancel RSI if the number of elements exceeds interference channel size.

Proposed algorithms achieve near-optimal throughput under worst-case RSI.

Insights on antenna allocation improve relay system performance.

Abstract

{Robust transceiver design against unresolvable system uncertainties is of crucial importance for reliable communication. We consider a MIMO multi-hop system, where the source, the relay, and the destination are equipped with multiple antennas. Further, an intelligent reconfigurable surface (IRS) is established to cancel the RSI as much as possible. The considered decode-and-forward (DF) hybrid relay can operate in either half-duplex or full-duplex mode, and the mode changes adaptively depending on the RSI strength. We investigate a robust transceiver design problem, which maximizes the throughput rate corresponding to the worst-case RSI under a self-interference channel uncertainty bound constraint. To the best of our knowledge, this is the first work that uses the IRS for RSI cancellation in MIMO full-duplex DF relay systems. The yielded problem turns out to be a non-convex optimization problem, where the non-convex objective is optimized over the cone of semidefinite matrices. We propose a closed-from lower bound for the IRS worst case RSI cancellation. Eventually, we show an important result that, for the worst case scenario, IRS can be helpful only if the number of IRS elements are at least as large as the size of the interference channel. Moreover, a novel method based on majorization theory is proposed to find the best response of the transmitters and relay against worst case RSI. Furthermore, we propose a multi-level water-filling algorithm to obtain a locally optimal solution iteratively. Finally, we obtain insights on the optimal antenna allocation at the relay input-frontend and output-frontend, for relay reception and transmission, respectively.