Joint Parametric Sensing and Bilinear Interference Recovery in Low-Altitude ISAC Networks
TL;DR
Simulation results show that IICS significantly outperforms conventional 2D-FFT processing and state-of-the-art iterative benchmarks, substantially extending the reliable sensing regime toward much lower signal-to-interference-plus-noise (SINR) ratio regimes, and reveals an inherent sensing-to-communication tradeoff.
Abstract
Networked integrated sensing and communication (ISAC) is a promising paradigm for future wireless systems, especially for low-altitude UAV sensing and tracking. However, its sensing performance can be severely degraded by crosslink interference from neighboring communication base stations, whose power is typically much stronger than that of the target echo due to one-way propagation and the absence of round-trip path loss. In this paper, we propose an iterative interference cancellation and sensing (IICS) algorithm for joint parametric sensing and bilinear interference recovery. Built upon a damped alternating refinement framework, the proposed method exploits the parametric structure of sensing echoes and the bilinear coupling in the cross-link interference component to progressively decouple the sensing and communication variables. Specifically, IICS alternates among parametric sensing refinement, interference-symbol demodulation, and interference-channel estimation through damped iterative updates. Simulation results show that IICS significantly outperforms conventional 2D-FFT processing and state-of-the-art iterative benchmarks, substantially extending the reliable sensing regime toward much lower signal-to-interference-plus-noise (SINR) ratio regimes. The results further reveal an inherent sensing-to-communication tradeoff, while confirming that IICS maintains superior interference recovery reliability even in echo-dominated scenarios.