In this paper, we propose a novel transmissive reconfigurable intelligent surface (TRIS) transceiver-empowered distributed cooperative integrated sensing and communication (ISAC) network to enhance coverage as well as to enhance wireless environment understanding. Based on the network requirements, the users are categorized into cooperative users (CUEs) and destination users (DUEs), and the CUEs utilize their own resources to serve the DUEs. To realize cooperation, we implement rate-splitting multiple access (RSMA) at the base station (BS), where the common stream is decoded and reencoded at the CUEs and forwarded to the DUEs, while the private stream satisfies the CUEs' own communication requirements. We construct an optimization problem with maximum minimum radar mutual information (RMI) as the objective function to optimize the BS beamforming matrix, the CUE beamforming matrices, the common stream rate vectors, and the user scheduling vectors. Due to the coupling of the optimization variables and non-convex operation, the proposed problem is a non-convex optimization problem that cannot be solved directly. To address the above challenges, we adopt a consensus alternating direction method of multipliers (ADMM) framework to decouple the optimization variables and solve it. Specifically, the problem is decoupled into multiple subproblems and solved by iterative optimization independently until overall convergence is achieved. Finally, numerical results validate the superiority of the proposed scheme in terms of improving communication sum-rate and RMI, and greatly reduce the algorithm complexity.

翻译：本文提出了一种新型透射式可重构智能表面收发器赋能的分布式协作集成感知与通信网络，旨在提升网络覆盖范围并增强对无线环境的理解。根据网络需求，用户被分为协作用户与目标用户，其中协作用户利用自身资源为目标用户提供服务。为实现协作，我们在基站端采用速率分割多址接入技术，其中公共流在协作用户处被解码并重新编码后转发至目标用户，而私有流则满足协作用户自身的通信需求。我们构建了一个以最大化最小雷达互信息为目标函数的优化问题，以优化基站波束成形矩阵、协作用户波束成形矩阵、公共流速率向量以及用户调度向量。由于优化变量间的耦合性及非凸运算，该问题属于无法直接求解的非凸优化问题。为应对上述挑战，我们采用一致性交替方向乘子法框架对优化变量进行解耦求解。具体而言，原问题被分解为多个子问题，通过独立迭代优化直至整体收敛。最终，数值结果验证了所提方案在提升通信总速率与雷达互信息方面的优越性，并显著降低了算法复杂度。