AI 中文总结
针对多RIS辅助SISO系统中未知强干扰源的SINR最大化问题，引入基于组的相位参数化，开发ZO-AdaMM算法，有效降低搜索维度，显著加快收敛速度，在有限测量预算下实现卓越干扰抑制性能。
AI 中文摘要
本文研究了具有未知强干扰源的多可重构智能表面（RIS）辅助单输入单输出（SISO）系统中，基于测量的信号与干扰加噪声比（SINR）最大化问题。目标是优化反射系数以使接收机处的SINR最大化。由于干扰信道未知，该优化问题是一个目标函数无闭式解析表达式的黑箱优化问题。为解决具有离散变量约束的高维黑箱优化问题，引入基于组的相位参数化以显著降低搜索维度，并在此基础上开发了基于组的零阶自适应矩（ZO-AdaMM）算法。仿真结果表明，所提分组策略显著加快收敛速度，在有限测量预算下实现了卓越的干扰抑制性能，尤其在小预算情况下。
英文摘要
In this paper, we study measurement-driven signal-to-interference-plus-noise ratio (SINR) maximization for a multi-reconfigurable intelligent surface (RIS)-aided single-input single-output (SISO) system with unknown strong interference sources. Specifically, the objective is to optimize the reflection coefficients such that the SINR is maximized at the receiver. As the interference channels are unknown, such an optimization problem is a black-box optimization problem with an objective function whose closed-form analytical expression is unknown. To address the high-dimensional black-box optimization problem with discrete variable constraints, we introduce a group-based phase parameterization that significantly reduces the search dimension. Building on this model, we develop a group-based zeroth-order adaptive moment (ZO-AdaMM) algorithm. Simulation results show that the proposed grouping strategy markedly accelerates the convergence speed and achieves a superior interference suppression performance under limited measurement budgets, especially in the small-budget regime.
