Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks

TL;DR

This paper presents an ADMM-based framework for joint radar channel and transmit signal estimation in mmWave joint radar-communication systems, improving accuracy and robustness in 5G networks.

Contribution

It introduces a novel dual-blind deconvolution method using ADMM that effectively estimates radar channels and signals under practical hardware constraints.

Findings

Reliable radar channel estimation demonstrated through simulations

Enhanced communication robustness in joint radar-communication scenarios

Algorithm accommodates hardware limitations and various system configurations

Abstract

This paper introduces a novel framework for jointly estimating unknown radar channels and transmit signals in millimeter-wave (mmWave) Joint Radar-Communication (JRC) systems, a problem often referred to as dual-blind deconvolution. The proposed method employs the Alternating Direction Method of Multipliers (ADMM) to iteratively refine the radar channel G (or H) and the transmitted signal X under convex constraints, incorporating both smooth and non-smooth penalty terms via proximal operators. By enforcing a bounded perturbation model for the radar channel and a strict transmit power budget, the algorithm aligns well with practical hardware limits. Extensive simulations demonstrate that the proposed approach reliably addresses the dual-blind deconvolution challenge, resulting in effective radar channel estimation and robust communication performance. Notably, the framework's iterative structure readily accommodates hardware considerations and different system configurations, making it well-suited for emerging mmWave JRC scenarios. Its adaptability and computational efficiency highlight the potential for wider adoption in next-generation wireless networks, where radar detection and communications increasingly share bandwidth and hardware resources.