AI 中文总结
研究基于透射式可重构智能表面收发器的集成感知与通信中发射波束成形设计，结合分数规划与主元最小化框架及乘子交替方向法，提出两种算法，仿真表明算法收敛有效，低复杂度且性能接近基准。
AI 中文摘要
集成感知与通信（ISAC）是未来无线网络的关键技术，需要高效硬件架构来共同支持通信和感知。本文利用透射式可重构智能表面（TRIS）收发器实现ISAC系统。在给定系统模型下，研究TRIS收发器的发射波束成形设计，以在满足预定义感知波束方向图增益/通信速率阈值及TRIS收发器单位功率约束的条件下最大化和速率/波束方向图增益。由于目标函数和约束是非凸的，这两个优化问题极具挑战性。为解决这些难题，结合分数规划（FP）方法和主元最小化（MM）框架开发基于二阶锥规划（SOCP）的解决方案。因单位功率约束引入大量约束，增加了优化问题求解复杂度。通过拆分耦合约束并应用乘子交替方向法（ADMM）框架，分别针对和速率及波束方向图增益最大化问题提出两种基于解析的算法来高效更新波束成形器配置。仿真结果证明了所提算法的收敛性和有效性，表明低复杂度算法在大幅降低计算复杂度的情况下实现了接近基于SOCP基准的性能。
英文摘要
Integrated sensing and communication (ISAC) is a key technology for future wireless networks, calling for hardware-efficient architectures to jointly support communication and sensing. In this paper, a transmissive reconfigurable intelligent surface (TRIS) transceiver is leveraged to enable an ISAC system. Under the considered system model, we investigate transmit beamforming design for the TRIS transceiver to maximize the sum-rate/beampattern gain, subject to the predefined sensing beampattern gain/communication rate thresholds and the per-unit power constraints of the TRIS transceiver. Since the objective functions and constraints are non-convex, the above two optimization problems are highly challenging. To resolve the difficult optimization problems, we combine the fractional programming (FP) method and the majorization-minimization (MM) framework to develop second-order cone programming (SOCP)-based solutions. Since the per-element power constraints introduce a large number of constraints, this increases the complexity of solving the optimization problems. By splitting the coupling constraints and applying the alternating direction method of multipliers (ADMM) framework, we propose two analytic-based algorithms for efficiently updating the beamformer configurations in the sum-rate and beampattern gain maximization problems, respectively. Simulation results demonstrate the convergence and effectiveness of the proposed algorithms, and show that the low-complexity algorithms achieve performance close to the SOCP-based benchmarks with substantially reduced computational complexity.
