This paper investigates a coordinated multi-cell integrated sensing, communication, and powering (ISCAP) system operating in the electromagnetic near field, where each base station (BS) employs an extremely large-scale antenna array (ELAA) to simultaneously support downlink communication, wireless power transfer (WPT), and environmental sensing. Three categories of communication users (CUs) with different interference cancellation capabilities are considered, and sensing is enabled through a distributed multiple-input multiple-output (MIMO) radar architecture. To address the resulting design challenges, a robust optimization framework is proposed by optimizing the beamforming strategy to maximize the worst-case detection probability over a prescribed sensing region, subject to per-user signal-to-interference-plus-noise ratio (SINR) constraints and energy harvesting requirements at energy receivers (ERs), while explicitly capturing the uncertainty in ER locations. By leveraging semidefinite relaxation (SDR), the original non-convex problem is reformulated as a convex semidefinite program with a provably tight relaxation. Furthermore, a low-complexity maximum ratio transmission (MRT)-based suboptimal scheme is developed, yielding a closed-form solution in the asymptotic regime as the number of antenna elements approaches infinity. Extensive numerical results reveal the fundamental trade-offs among sensing accuracy, communication reliability, and WPT efficiency.

翻译：本文研究了一种在电磁近场中运行的协同多小区集成感知、通信与供能系统，其中每个基站均采用超大规模天线阵列，以同时支持下行链路通信、无线能量传输与环境感知。系统考虑了具备不同干扰消除能力的三类通信用户，并通过分布式多输入多输出雷达架构实现感知功能。为应对由此产生的设计挑战，本文提出了一种鲁棒优化框架，通过优化波束成形策略，在满足各用户信干噪比约束与能量接收器能量收集需求的同时，最大化规定感知区域内的最坏情况检测概率，并显式地刻画了能量接收器位置的不确定性。借助半定松弛技术，原始非凸问题被重构为具有可证明紧致松弛特性的凸半定规划问题。此外，本文还提出了一种基于最大比传输的低复杂度次优方案，该方案在天线单元数量趋于无穷的渐近条件下可得到闭式解。大量数值结果揭示了感知精度、通信可靠性与无线能量传输效率之间的基本权衡关系。