Robust ISAC Transceiver Beamforming Design under Low-Resolution AD/DA Converters

TL;DR

This paper proposes a robust beamforming design for integrated sensing and communication systems with low-resolution converters, improving detection performance by accounting for quantization noise and using advanced optimization techniques.

Contribution

It introduces a novel robust beamforming approach that handles low-resolution DACs/ADCs, employing SDR and MM algorithms for different scenarios, enhancing system robustness.

Findings

The proposed algorithm improves detection performance in low-resolution quantization scenarios.

Simulation results confirm the robustness and efficacy of the method.

The approach outperforms non-robust algorithms in practical settings.

Abstract

In this letter, we investigate the robust beamforming design for an integrated sensing and communication (ISAC) system featuring low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). Taking into account quantization noise, we aim at maximizing the radar signal-to-quantization-plus-noise ratio (SQNR) while guaranteeing the minimum required signal-to-quantization-plus-interference-plus-noise ratio (SQINR) for communication users. To address this nonconvex design problem, we first examine a scenario involving a point target and uniform-resolution DACs, where the globally optimal solution is obtained by applying the semidefinite relaxation (SDR) technique. For more general scenarios, including those with mixed-DACs and/or an extended target, we develop a low-complexity majorization-minimization (MM)-based algorithm to tackle the problem iteratively. Compared to the non-robust algorithm, the proposed algorithm demonstrates improved detection performance under practical quantization. Simulation results confirm the robustness and efficacy of our proposed algorithm in low-resolution quantization scenarios.