Zak-OTFS ISAC with Bistatic Sensing via Semi-Blind Atomic Norm Denoising Scheme

TL;DR

This paper introduces a semi-blind atomic norm denoising scheme for Zak-OTFS ISAC that enhances bistatic sensing accuracy and robust communication in high-mobility doubly dispersive environments.

Contribution

It develops a novel joint channel estimation and data detection method using atomic norm denoising tailored for Zak-OTFS with fractional delay-Doppler shifts.

Findings

Achieves super-resolution sensing accuracy.

Approaches perfect channel state information performance.

Provides a convergent efficient iterative algorithm.

Abstract

Integrated sensing and communication (ISAC) through Zak-transform-based orthogonal time frequency space (Zak-OTFS) modulation is a promising solution for high-mobility scenarios. Realizing accurate bistatic sensing and robust communication necessitates precise channel estimation; however, this remains a formidable challenge in doubly dispersive environments, where fractional delay-Doppler shifts induce severe channel spreading. This paper proposes a semi-blind atomic norm denoising scheme for Zak-OTFS ISAC with bistatic sensing. We first derive the discrete-time input-output (I/O) relationship of Zak-OTFS under fractional delay-Doppler shifts and rectangular windowing. Based on this I/O relation, we formulate the joint channel parameter estimation and data detection task as an atomic norm denoising problem, utilizing the negative square penalty method to handle the non-convex discrete constellation constraints. To solve this problem efficiently, we develop an accelerated iterative algorithm that integrates majorization-minimization, accelerated projected gradient, and inexact accelerated proximal gradient methods. We provide a rigorous convergence proof for the proposed algorithm. Simulation results demonstrate that the proposed scheme achieves super-resolution sensing accuracy and communication performance approaching the perfect channel state information lower bound.