Deep Learning based Three-stage Solution for ISAC Beamforming Optimization
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911404509888512 |
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| author | Gao, Qian Zhong, Ruikang Liu, Yuanwei |
| author_facet | Gao, Qian Zhong, Ruikang Liu, Yuanwei |
| contents | In this paper, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a three-stage solution for ISAC beamforming. The three-stage beamforming optimization solution includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed three-stage solution outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20667 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Deep Learning based Three-stage Solution for ISAC Beamforming Optimization Gao, Qian Zhong, Ruikang Liu, Yuanwei Signal Processing In this paper, a general ISAC system where the base station (BS) communicates with multiple users and performs target detection is considered. Then, a sum communication rate maximization problem is formulated, subjected to the constraints of transmit power and the minimum sensing rates of users. To solve this problem, we develop a framework that leverages deep learning algorithms to provide a three-stage solution for ISAC beamforming. The three-stage beamforming optimization solution includes three modules: 1) an unsupervised learning based feature extraction algorithm is proposed to extract fixed-size latent features while keeping its essential information from the variable channel state information (CSI); 2) a reinforcement learning (RL) based beampattern optimization algorithm is proposed to search the desired beampattern according to the extracted features; 3) a supervised learning based beamforming reconstruction algorithm is proposed to reconstruct the beamforming vector from beampattern given by the RL agent. Simulation results demonstrate that the proposed three-stage solution outperforms the baseline RL algorithm by optimizing the intuitional beampattern rather than beamforming. |
| title | Deep Learning based Three-stage Solution for ISAC Beamforming Optimization |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.20667 |