Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915647640829952 |
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| author | Zafari, Mehdi Liu, Rang Swindlehurst, A. Lee |
| author_facet | Zafari, Mehdi Liu, Rang Swindlehurst, A. Lee |
| contents | Integrated Sensing and Communication (ISAC) is emerging as a key enabler for 6G wireless networks, allowing the joint use of spectrum and infrastructure for both communication and sensing. While prior ISAC solutions have addressed resource optimization, including power allocation, beamforming, and waveform design, they often rely on centralized architectures with full network knowledge, limiting their scalability in distributed systems. In this paper, we propose two coordinated decentralized optimization algorithms for beamforming and power allocation tailored to cell-free ISAC networks. The first algorithm employs locally designed fixed beamformers at access points (APs), combined with a centralized power allocation scheme computed at a central server (CS). The second algorithm jointly optimizes beamforming and power control through a fully decentralized consensus ADMM framework. Both approaches rely on local information at APs and limited coordination with the CS. Simulation results obtained using our proposed Python-based simulation framework evaluate their fronthaul overhead and system-level performance, demonstrating their practicality for scalable ISAC deployment in decentralized, cell-free architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems Zafari, Mehdi Liu, Rang Swindlehurst, A. Lee Signal Processing Optimization and Control Integrated Sensing and Communication (ISAC) is emerging as a key enabler for 6G wireless networks, allowing the joint use of spectrum and infrastructure for both communication and sensing. While prior ISAC solutions have addressed resource optimization, including power allocation, beamforming, and waveform design, they often rely on centralized architectures with full network knowledge, limiting their scalability in distributed systems. In this paper, we propose two coordinated decentralized optimization algorithms for beamforming and power allocation tailored to cell-free ISAC networks. The first algorithm employs locally designed fixed beamformers at access points (APs), combined with a centralized power allocation scheme computed at a central server (CS). The second algorithm jointly optimizes beamforming and power control through a fully decentralized consensus ADMM framework. Both approaches rely on local information at APs and limited coordination with the CS. Simulation results obtained using our proposed Python-based simulation framework evaluate their fronthaul overhead and system-level performance, demonstrating their practicality for scalable ISAC deployment in decentralized, cell-free architectures. |
| title | Coordinated Decentralized Resource Optimization for Cell-Free ISAC Systems |
| topic | Signal Processing Optimization and Control |
| url | https://arxiv.org/abs/2508.01044 |