Symbol-Level Precoding-Based Self-Interference Cancellation for ISAC Systems

TL;DR

This paper explores symbol-level precoding as a novel method for self-interference cancellation in integrated sensing and communication systems, enhancing target detection while maintaining user quality of service.

Contribution

It formulates a new nonconvex fractional programming problem for SIC using SLP and proposes an efficient penalty-based BCD algorithm for its solution.

Findings

Enhanced detection probability demonstrated through simulations

Proposed method effectively balances SIC and user QoS

Algorithm achieves efficient convergence with closed-form updates

Abstract

Consider an integrated sensing and communication (ISAC) system where a base station (BS) employs a full-duplex radio to simultaneously serve multiple users and detect a target. The detection performance of the BS may be compromised by self-interference (SI) leakage. This paper investigates the feasibility of SI cancellation (SIC) through the application of symbol-level precoding (SLP). We first derive the target detection probability in the presence of the SI. We then formulate an SLP-based SIC problem, which optimizes the target detection probability while satisfying the quality of service requirements of all users. The formulated problem is a nonconvex fractional programming (FP) problem with a large number of equality and inequality constraints. We propose a penalty-based block coordinate descent (BCD) algorithm for solving the formulated problem, which allows for efficient closed-form updates of each block of variables at each iteration. Finally, numerical simulation results are presented to showcase the enhanced detection performance of the proposed SIC approach.