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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2604.23880 |
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| _version_ | 1866915960529616896 |
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| author | Wang, Zilong Zhang, Cheng Zhang, Zhilei Hu, Yaxuan Wang, Wen Huang, Yongming |
| author_facet | Wang, Zilong Zhang, Cheng Zhang, Zhilei Hu, Yaxuan Wang, Wen Huang, Yongming |
| contents | This paper presents a deep unfolding-supported coordinated multipoint beam pattern synthesis (DUCoMP-BPS) scheme to overcome the high complexity, poor adaptability, and limited scalability of traditional cell-free anti-jamming beamforming. In the proposed design, access points (APs) independently determine analog beamforming using local angle information, while the central processing unit (CPU) performs cooperative digital beamforming with only a single AP-CPU interaction, significantly reducing fronthaul overhead. To further improve efficiency, a deep unfolding strategy transforms the costly step size search in analog beamforming into a trainable parameter, where an offline-trained complex-valued neural network enables fast and adaptive online inference. Simulation results show that the complexity of DUCoMP-BPS scales linearly with the number of APs, reduces single-AP analog beamforming runtime by about 67% compared to conventional optimization, and achieves superior nulling performance over purely data-driven approaches. Hardware feasibility is validated on an Advanced RISC Machine-Field Programmable Gate Array (ARM-FPGA) heterogeneous platform, where algorithm-hardware co-verification and hardware-software decoupling enable efficient parallelism and low-latency execution. Finally, anechoic chamber measurements under practical hardware imperfections confirm robust beamforming performance, demonstrating the strong potential of DUCoMP-BPS for real-world deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_23880 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Coordinated Multipoint Anti-jamming Beam Pattern Synthesis: From AI Accelerated Algorithm to Hardware Implementation Wang, Zilong Zhang, Cheng Zhang, Zhilei Hu, Yaxuan Wang, Wen Huang, Yongming Signal Processing This paper presents a deep unfolding-supported coordinated multipoint beam pattern synthesis (DUCoMP-BPS) scheme to overcome the high complexity, poor adaptability, and limited scalability of traditional cell-free anti-jamming beamforming. In the proposed design, access points (APs) independently determine analog beamforming using local angle information, while the central processing unit (CPU) performs cooperative digital beamforming with only a single AP-CPU interaction, significantly reducing fronthaul overhead. To further improve efficiency, a deep unfolding strategy transforms the costly step size search in analog beamforming into a trainable parameter, where an offline-trained complex-valued neural network enables fast and adaptive online inference. Simulation results show that the complexity of DUCoMP-BPS scales linearly with the number of APs, reduces single-AP analog beamforming runtime by about 67% compared to conventional optimization, and achieves superior nulling performance over purely data-driven approaches. Hardware feasibility is validated on an Advanced RISC Machine-Field Programmable Gate Array (ARM-FPGA) heterogeneous platform, where algorithm-hardware co-verification and hardware-software decoupling enable efficient parallelism and low-latency execution. Finally, anechoic chamber measurements under practical hardware imperfections confirm robust beamforming performance, demonstrating the strong potential of DUCoMP-BPS for real-world deployment. |
| title | Coordinated Multipoint Anti-jamming Beam Pattern Synthesis: From AI Accelerated Algorithm to Hardware Implementation |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.23880 |