Beamforming Design for Intelligent Reflecting Surface-Enhanced Symbiotic Radio Systems

TL;DR

This paper proposes a beamforming design for IRS-assisted symbiotic radio systems that maximizes primary system sum-rate while ensuring secondary user decoding performance, using non-coherent detection and convex approximation techniques.

Contribution

It introduces a practical beamforming optimization considering non-coherent detection at the secondary user and develops a suboptimal algorithm with improved sum-rate performance.

Findings

Significant sum-rate improvement over benchmarks.

Effective secondary user decoding performance guarantee.

Validated through extensive simulations.

Abstract

This paper investigates multiuser multi-input single-output downlink symbiotic radio communication systems assisted by an intelligent reflecting surface (IRS). Different from existing methods ideally assuming the secondary user (SU) can jointly decode information symbols from both the access point (AP) and the IRS via multiuser detection, we consider a more practical SU that only non-coherent detection is available. To characterize the non-coherent decoding performance, a practical upper bound of the average symbol error rate (SER) is derived. Subsequently, we jointly optimize the beamformer at the AP and the phase shifts at the IRS to maximize the average sum-rate of the primary system taking into account the maximum tolerable SER constraint for the SU. To circumvent the couplings of variables, we exploit the Schur complement that facilitates the design of a suboptimal beamforming algorithm based on successive convex approximation. Our simulation results show that compared with various benchmark algorithms, the proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decoding performance of the secondary system.