Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks
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| Format: | Preprint |
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2024
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| _version_ | 1866912179645579264 |
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| author | Hamadouche, Anis Sellathurai, Mathini |
| author_facet | Hamadouche, Anis Sellathurai, Mathini |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks Hamadouche, Anis Sellathurai, Mathini Signal Processing 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. |
| title | Efficient Dual-Blind Deconvolution for Joint Radar-Communication Systems Using ADMM: Enhancing Channel Estimation and Signal Recovery in 5G mmWave Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2409.19187 |