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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.02216 |
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| _version_ | 1866911248028794880 |
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| author | Yu, Hyemin Yang, Hong-Chuan |
| author_facet | Yu, Hyemin Yang, Hong-Chuan |
| contents | Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications. Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication. In this paper, we develop an adaptive transmission design for a two-hop relaying communication system. Each hop transmission adaptively configures its transmission parameters separately, including numerology, mini-slot size, and modulation and coding scheme, for reliable packet transmission within a strict latency constraint. We formulate the hop-specific transceiver configuration as a Markov decision process (MDP) and propose a dual-agent reinforcement learning-based cooperative latency-aware transmission (DRL-CoLA) algorithm to learn latency-aware transmission policies in a distributed manner. Simulation results verify that the proposed algorithm achieves the near-optimal reliability while satisfying strict latency requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02216 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning Yu, Hyemin Yang, Hong-Chuan Information Theory Artificial Intelligence Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications. Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication. In this paper, we develop an adaptive transmission design for a two-hop relaying communication system. Each hop transmission adaptively configures its transmission parameters separately, including numerology, mini-slot size, and modulation and coding scheme, for reliable packet transmission within a strict latency constraint. We formulate the hop-specific transceiver configuration as a Markov decision process (MDP) and propose a dual-agent reinforcement learning-based cooperative latency-aware transmission (DRL-CoLA) algorithm to learn latency-aware transmission policies in a distributed manner. Simulation results verify that the proposed algorithm achieves the near-optimal reliability while satisfying strict latency requirements. |
| title | Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning |
| topic | Information Theory Artificial Intelligence |
| url | https://arxiv.org/abs/2511.02216 |