AI 中文总结
本文针对通感一体化无蜂窝大规模MIMO系统，提出联合云、前传与无线资源分配的端到端优化框架，可大幅降低总功耗并保持良好检测性能。
AI 中文摘要
无蜂窝大规模MIMO（CF-mMIMO）与通感一体化（ISAC）结合是未来6G网络的有前景架构，可实现基于感知的新型应用，但感知功能的集成增加了无线、前传、云各域的功耗，传统发射功率优化方法未考虑该问题。本文针对带分布式多目标检测的绿色CF-mMIMO ISAC系统，提出跨层端到端（E2E）优化框架，设计分布式感知方法：接收接入点（RX-AP）计算本地检验统计量，通过加权组合策略转发至云进行聚合；推导全知情（FIS）和部分知情（PIS）场景下的最大后验比检验（MAPRT）检测器，覆盖RX-AP处不同侧信息水平；构建联合优化问题，在通信与感知约束下，通过协同优化发射功率分配、AP工作模式、通信用户与感知关联、RX-AP分配及云/前传资源，最小化网络总功耗；该混合整数非凸问题采用基于连续凸近似和惩罚松弛的两阶段迭代算法求解。数值结果表明，所提E2E框架相比基准方案大幅降低总功耗，相比仅优化发射功率的方案节省超50%，相比仅优化无线资源的方案节省约13%-15%，同时保持有竞争力的检测性能。
英文摘要
In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, UE and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex--concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0.95. The results also reveal a fundamental implementation trade-off: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Comments14 pages
