AI 中文总结
针对有源IRS辅助系统，提出基于速率轮廓学习的真实干扰对齐算法，其性能优于传统加权最小均方误差算法且执行时间更短。
AI 中文摘要
有源智能反射面（IRS）可提供额外的空间自由度，使干扰对齐（IA）能够以低成本实现。本文提出一种适用于有源IRS辅助系统的真实IA方案，该方案假设直连链路被阻塞，仅要求IRS获取瞬时信道系数。为在满足单个最小速率要求和传输功率约束的条件下最大化可达和速率，提出一种基于速率轮廓学习的算法，该算法利用离线训练的可达速率轮廓将原问题解耦为多个可行性子问题，再通过广义特征值分解求解。仿真结果表明，所提算法在性能上优于传统的加权最小均方误差算法，同时所需的程序执行时间显著更少。
英文摘要
With additional spatial degrees of freedom provided by the active intelligent reflecting surface (IRS), interference alignment (IA) can be achieved at low cost. In this letter, we propose a real IA scheme for an active IRS-aided system. The proposed scheme only requires the IRS to know the instantaneous channel coefficients under the assumption of blocked direct links. To maximize the achievable sum rate subject to individual minimum rate requirements and transmission power constraints, we propose a rate-profile learning-based algorithm. The algorithm uses offline-trained achievable rate profiles to decouple the original problem into multiple feasibility subproblems, which are then solved by generalized eigenvalue decomposition. Simulation results demonstrate that our proposed algorithm outperforms the conventional weighted minimum mean square error algorithm, while requiring significantly less program execution time.
