Sense Once, Serve Many: Common-Trace Factorized Constrained PPO for Online Sensing-Session Consolidation in Multi-Tenant ISAC Networks
Read the original on arXiv Machine Learning →The paper introduces Common-Trace Factorized Constrained Proximal Policy Optimization (CT‑PPO) for optimizing online sensing‑session consolidation in multi‑tenant integrated sensing and communication (ISAC) networks. By formulating the problem as a constrained Markov decision process, CT‑PPO shares workload traces across stochastic policy replicas and uses leave‑one‑out discounted Monte Carlo returns to credit applicable actor factors, achieving higher macro returns and lower sensing‑resource costs than baseline methods. Ablation studies show that the common‑trace reward credit is the key contributor to performance gains, and the algorithm retains advantages across varying arrival loads without increasing deployment latency.
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