AI 中文总结
针对多基站协作ISAC网络，提出CRLB驱动的BF与PA框架，基于PEB、VEB优化，开发SDP基BF与两阶段PA算法，算法性能接近BF且执行时间短，适用于动态环境。
AI 中文摘要
本文提出一种面向协作集成感知与通信（ISAC）网络的克拉美-罗下界（CRLB）驱动的波束成形（BF）与功率分配（PA）框架，其中一组多天线基站（BS）联合服务多个用户，同时执行多静态目标估计。在设计中，研究如何利用位置误差界（PEB）和速度误差界（VEB）并将其融入BF与PA优化，以PEB和VEB作为指标直接表征感知精度。首先，提出一种基于半定规划（SDP）的BF，其中通过双舒尔补处理PEB和VEB约束，且BF协方差矩阵的秩-1约束通过半定松弛（SDR）进行松弛，利用卡罗需-库恩-塔克（KKT）条件证明该松弛的紧性。为解决复杂度问题，进一步开发两阶段PA算法，其中通信波束采用正则化迫零（RZF）方法形成，感知波束采用零空间投影（NSP）方法形成，通信与感知功率通过二阶锥规划（SOCP）依次求解。尽管该PA算法会牺牲自由度，但仿真中其性能与BF相比仅有微小差距，约25毫秒的执行时间证明其适用于动态环境。
英文摘要
This paper presents a Cramér-Rao lower bound (CRLB)-driven beamforming (BF) and power allocation (PA) framework for cooperative integrated sensing and communication (ISAC) networks, where a set of multi-antenna base stations (BSs) jointly serve multiple users and simultaneously perform multi-static target estimation. In our design, we investigate how the position error bound (PEB) and velocity error bound (VEB) can be exploited and incorporated into BF and PA optimization. The design leveraging PEB and VEB as metrics directly characterizes sensing accuracy. First, we propose a semidefinite programming (SDP)-based BF, where the PEB and VEB constraints are handled by double Schur complements, and the rank-1 constraints of the BF covariance matrices are relaxed by semidefinite relaxation (SDR), whose tightness is proved using the Karush-Kuhn-Tucker (KKT) conditions. Addressing the complexity, we further develop a two-stage PA algorithm, where the communication and sensing beams are formed by the regularized zero-forcing (RZF) and null-space projection (NSP) methods, respectively, and the communication and sensing PAs are solved sequentially by second-order cone programming (SOCP). Although the PA algorithm sacrifices the degrees of freedom, its performance shows a slight gap compared to BF in the simulation, while the execution time of around 25 ms demonstrates its applicability in dynamic environments.
