Integrated Sensing and Communications for Low-Altitude Economy with Deterministic Sensing and Gaussian Information Signals

TL;DR

This paper proposes an integrated sensing and communication framework for UAV surveillance, optimizing waveform design and detection strategies to enhance target detection while maintaining communication quality.

Contribution

It introduces a joint detection and beamforming approach for UAV ISAC systems, leveraging both deterministic and stochastic signals with advanced optimization techniques.

Findings

The NP-based detector outperforms conventional schemes.

Optimized beamforming maximizes detection probability under power and SINR constraints.

A fundamental trade-off exists between communication rate and detection performance.

Abstract

Reliable surveillance and communication for unmanned aerial vehicles (UAVs) are crucial for enabling and sustaining the accelerated growth of the low-altitude economy. Integrated sensing and communications (ISAC) offers a cost-effective and scalable framework for target sensing by leveraging existing wireless communication systems. This paper investigates a bistatic downlink ISAC architecture tailored to UAV operations, in which a BS communicates with a legitimate UAV and detects a potential unauthorized intruder in the surveillance region. We assume that the BS transmits superimposed ISAC waveforms comprising both Gaussian-information-bearing and deterministic sensing components. First, we develop a Neyman-Pearson (NP)-based optimal detector that jointly exploits both deterministic sensing and stochastic signal components. Subsequently, we optimize the transmit beamforming design at the BS to maximize the minimum detection probability over the entire surveillance region, subject to a minimum signal-to-interference-plus-noise ratio (SINR) requirement at the authorized UAV and a total transmit power budget at the BS. The resulting design problem is highly non-convex, which is efficiently addressed via semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques. Simulation results demonstrate the superiority of the proposed NP-based detector, which fully leverages the synergy between both types of signals, over conventional benchmark schemes that treat information-bearing signals merely as interference. Furthermore, the results reveal a fundamental sensing-communication trade-off, where increasing the communication-rate threshold directs more transmit power to Gaussian-information-bearing signals, thereby reducing the power allocated to deterministic components and consequently weakening detection performance.