Detection in Bistatic ISAC with Deterministic Sensing and Gaussian Information Signals

TL;DR

This paper proposes a Neyman-Pearson detector for bistatic ISAC systems that jointly utilizes deterministic sensing and Gaussian communication signals, optimizing beamforming to enhance detection while maintaining communication quality.

Contribution

It introduces a novel detection method combining deterministic and Gaussian signals and develops an optimized beamforming approach for bistatic ISAC systems.

Findings

The proposed detector outperforms benchmark schemes.

Joint signal utilization improves detection performance.

Higher communication rate thresholds reduce detection effectiveness.

Abstract

Integrated sensing and communications (ISAC) is a disruptive technology enabling future sixth-generation (6G) networks. This paper investigates target detection in a bistatic ISAC system, in which the base station (BS) transmits superimposed ISAC signals comprising both Gaussian information-bearing and deterministic sensing components to simultaneously provide communication and sensing functionalities. First, we develop a Neyman-Pearson (NP)-based detector that effectively utilizes both the deterministic sensing and random communication signals. Closed-form analysis reveals that both signal components contribute to improving the overall detection performance. Subsequently, we optimize the BS transmit beamforming to maximize the detection probability, subject to a minimum signal-to-interference-plus-noise ratio (SINR) constraint for the communication user (CU) and a total transmit power budget at the BS. The resulting non-convex beamforming optimization problem is addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which leverages both types of signals, over benchmark schemes that treat information signals as interference. They also reveal that a higher communication-rate threshold directs more transmit power to Gaussian information-bearing signals, thereby diminishing deterministic-signal power and weakening detection performance.