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arXiv 2410.04567cs.ITeess.SPmath.IT
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面向配置大型RIS的多用户MIMO系统的速率约束下功率最小化研究

Power Minimization with Rate Constraints for Multi-User MIMO Systems with Large-Size RISs

Silvia Palmucci, Giulio Bartoli, Andrea Abrardo, Marco Moretti, Marco Di Renzo

AI总结:

本研究针对配置多块大型RIS的单小区多用户MIMO系统,以用户SINR为约束,通过迭代交替优化预编码与分块式RIS配置实现总发射功率最小化,在保证性能的同时降低了计算复杂度。

摘要:

本研究聚焦于配置多块大型可重构智能表面(reconfigurable intelligent surfaces, RISs)的单小区多用户多输入多输出(multiple-input multiple-output, MIMO)系统的优化问题。研究以用户信干噪比(signal-to-interference-plus-noise ratios, SINRs)为约束,通过优化预编码系数与RIS配置实现总发射功率最小化。该最小化问题被拆分为两个子问题,采用迭代交替优化(alternating optimization, AO)方法求解:第一个子问题旨在求解最优预编码器设计;第二个子问题通过将RIS划分为更小的瓦片(tile)来优化其配置,每个瓦片被配置为预定义配置的组合,从而实现RIS的高效优化,尤其适用于传统方法计算复杂度高到难以承受的场景。仿真结果表明,与基准方案相比,所提方法性能优异且复杂度较低。

英文摘要:

This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users' signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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