Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2511.02216v1
• Date: Tue, 04 Nov 2025 03:08:59 GMT
• Title: Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning
• Abstract summary: Next-generation wireless communication systems must support ultra-reliable low-latency communication (URLLC) service for mission-critical applications.<n>Meeting stringent URLLC requirements is challenging, especially for two-hop cooperative communication.<n>We develop an adaptive transmission design for a two-hop relaying communication system.
• Score: 2.665411186169543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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