Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications

• URL: http://arxiv.org/abs/2602.15640v1
• Date: Tue, 17 Feb 2026 15:07:41 GMT
• Title: Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications
• Abstract summary: This paper introduces a time-constrained human-in-the-loop reinforcement learning framework that embeds human feedback, semantic utility, and latency control.<n>We formulate semantic adaptation driven by human feedback as a constrained Markov decision process.<n>We show that TC-HITL-RL consistently meets per-user timing constraints, outperforms baseline schedulers in reward, and stabilizes resource consumption.
• Score: 2.9668257052955176
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Semantic communication promises task-aligned transmission but must reconcile semantic fidelity with stringent latency guarantees in immersive and safety-critical services. This paper introduces a time-constrained human-in-the-loop reinforcement learning (TC-HITL-RL) framework that embeds human feedback, semantic utility, and latency control within a semantic-aware Open radio access network (RAN) architecture. We formulate semantic adaptation driven by human feedback as a constrained Markov decision process (CMDP) whose state captures semantic quality, human preferences, queue slack, and channel dynamics, and solve it via a primal--dual proximal policy optimization algorithm with action shielding and latency-aware reward shaping. The resulting policy preserves PPO-level semantic rewards while tightening the variability of both air-interface and near-real-time RAN intelligent controller processing budgets. Simulations over point-to-multipoint links with heterogeneous deadlines show that TC-HITL-RL consistently meets per-user timing constraints, outperforms baseline schedulers in reward, and stabilizes resource consumption, providing a practical blueprint for latency-aware semantic adaptation.

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