发表机构
VNU University of Engineering and Technology (VNU-UET)（越南国家大学工程技术大学（VNU-UET））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多租户ISAC网络在线感知会话整合问题，提出CT-PPO算法，通过公共轨迹奖励信用等设计，提升回报、降低感知资源成本，在多负载场景下表现优于对比算法。
AI 中文摘要
集成感知与通信（ISAC）网络可通过共享感知会话服务兼容请求，但整合过程需耦合准入控制、资源复用、配置文件选择、感知服务水平协议（SLAs）、通信服务质量（QoS）及未来承诺。我们将该问题建模为约束马尔可夫决策过程，并提出公共轨迹分解式约束近端策略优化（CT-PPO）算法。训练期间，随机策略副本共享同一原始工作负载轨迹；留一法折扣蒙特卡洛回报对比为适用的演员因子提供奖励信用，而约束信用则保持因子/前缀特异性。在5个训练种子及匹配工作负载下，CT-PPO实现最高平均宏观回报，较匹配的联合信用PPO（JC-PPO）超出0.934（95%置信区间[0.702, 1.164]），较感知SLA感知贪心算法高出1.847；与JC-PPO相比，其感知资源成本降低6.277，每创建会话的接受请求数提升0.0806。四向消融实验显示，仅分解式替代函数无明显宏观回报增益，添加公共轨迹奖励信用则产生主导性改进。无需重训练时，CT-PPO在低、标称及高到达负载下均保持回报优势，在聚类到达场景下增益最强。部署采用公共观测与硬掩码；CT-PPO的额外参数仅存在于训练侧，其演员模型规模与JC-PPO匹配，仅演员部分的CPU延迟无明显变化。
英文摘要
Integrated sensing and communication (ISAC) networks can serve compatible requests through shared sensing sessions, but consolidation couples admission, reuse, profile selection, sensing service-level agreements (SLAs), communication quality of service (QoS), and future commitments. We formulate this problem as a constrained Markov decision process and propose Common-Trace Factorized Constrained Proximal Policy Optimization (CT-PPO). During training, stochastic policy replicas share the same primitive workload trace; leave-one-out discounted Monte Carlo return contrasts provide reward credit to applicable actor factors, while constraint credit remains factor/prefix-specific. Across five training seeds and matched workloads, CT-PPO achieves the highest mean macro return, exceeding matched Joint-Credit PPO (JC-PPO) by 0.934 (95% confidence interval [0.702, 1.164]) and SLA-Aware Greedy by 1.847; versus JC-PPO, it reduces sensing-resource cost by 6.277 and raises accepted requests per created session by 0.0806. A four-way ablation shows that the factorized surrogate alone yields no detectable macro-return gain, whereas adding common-trace reward credit produces the dominant improvement. Without retraining, CT-PPO retains a return advantage at low, nominal, and high arrival loads, with the strongest gain under clustered arrivals. Deployment uses public observations and hard masks; CT-PPO's extra parameters are training-side, its actor footprint matches JC-PPO, and actor-only CPU latency is effectively unchanged.
Comments13 pages, 4 figures, 6 tables
