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

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.