Deep Reinforcement Learning for Dynamic Sensing and Communications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911175334166528 |
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| author | Zakeri, Abolfazl Nguyen, Nhan Thanh Alkhateeb, Ahmed Juntti, Markku |
| author_facet | Zakeri, Abolfazl Nguyen, Nhan Thanh Alkhateeb, Ahmed Juntti, Markku |
| contents | Environmental sensing can significantly enhance mmWave communications by assisting beam training, yet its benefits must be balanced against the associated sensing costs. To this end, we propose a unified machine learning framework that dynamically determines when to sense and leverages sensory data for beam prediction. Specifically, we formulate a joint sensing and beamforming problem that maximizes the average signal-to-noise ratio under an average sensing budget. Lyapunov optimization is employed to enforce the sensing constraint, while a deep Q-Network determines the sensing slots. A pretrained deep neural network then maps the sensing data to optimal beams in the codebook. Simulations based on the real-world DeepSense dataset demonstrate that the proposed approach substantially reduces sensing overhead while maintaining satisfactory communications performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Reinforcement Learning for Dynamic Sensing and Communications Zakeri, Abolfazl Nguyen, Nhan Thanh Alkhateeb, Ahmed Juntti, Markku Signal Processing Environmental sensing can significantly enhance mmWave communications by assisting beam training, yet its benefits must be balanced against the associated sensing costs. To this end, we propose a unified machine learning framework that dynamically determines when to sense and leverages sensory data for beam prediction. Specifically, we formulate a joint sensing and beamforming problem that maximizes the average signal-to-noise ratio under an average sensing budget. Lyapunov optimization is employed to enforce the sensing constraint, while a deep Q-Network determines the sensing slots. A pretrained deep neural network then maps the sensing data to optimal beams in the codebook. Simulations based on the real-world DeepSense dataset demonstrate that the proposed approach substantially reduces sensing overhead while maintaining satisfactory communications performance. |
| title | Deep Reinforcement Learning for Dynamic Sensing and Communications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2509.19130 |