AI 中文总结
本文针对通信受限的两跳云雷达网络，提出带缓存接入协议与混合整数优化的资源分配方法，可在通信预算内优化传感器子集与时频资源，最小化目标参数的整体CRLB以实现高保真雷达传感。
AI 中文摘要
分布式雷达传感利用空间分集解决遮挡问题并提升估计精度，但要实现这些增益需将高维雷达数据传输至融合中心（FC），这对无线网络提出了极高要求，尤其在工厂这类密集、动态且易受干扰的环境中，弹性和延迟至关重要。本文研究容量受限的两跳云雷达网络中的资源分配问题，提出一种带缓存的接入协议：传感器先执行本地频谱加窗以降低数据速率，再通过中间边缘服务器（ES）将测量值传输至FC。资源分配被建模为混合整数优化问题，目标是在回传和前传容量约束下最小化目标参数的整体克拉美-罗下界（CRLB），基于Big-M公式和逐次凸逼近（SCA）开发迭代求解算法，该框架能高效识别信息最丰富的传感器子集并优化时频资源分配，确保在严格通信预算内实现高保真传感。
英文摘要
Distributed radar sensing exploits spatial diversity to resolve occlusions and improve estimation accuracy. Realizing these gains, however, relies on the transmission of high-dimensional radar data to a Fusion Center (FC). This imposes significant demands on the wireless network, especially in dense, dynamic, and interference-prone environments like factories, where resilience and latency are critical. This paper studies the resource allocation problem in a capacity-constrained two-hop cloud radar network. We propose a buffered access protocol where sensors perform local spectral windowing to reduce data rates before transmitting measurements to the FC via intermediate Edge Servers (ESs). The resource allocation is formulated as a mixed-integer optimization problem aimed at minimizing the aggregate Cramer-Rao Lower Bound (CRLB) of the target parameters subject to fronthaul and backhaul capacity constraints. We develop an iterative solution algorithm based on Big-M formulation and Successive Convex Approximation (SCA). The proposed framework efficiently identifies the most informative sensor subsets and optimizes time-frequency resource assignments, ensuring high-fidelity sensing within stringent communication budgets.
