论文标题
优化IRS辅助C-RAN系统中的空中计算
Optimizing Over-the-Air Computation in IRS-Aided C-RAN Systems
论文作者
论文摘要
无线计算（AIRCOMP）是一种有效的解决方案，可以在无线通道上进行联合学习。 AirComp假设可以通过发射器端相补偿来控制来自不同设备的无线通道，以确保相干的空中组合。智能反射表面（IRS）可以提供控制信道传播条件的替代方法或其他方法。这项工作研究了在大型云无线电访问网络（C-RAN）中为AIRCOMP系统部署IRS的优势。在此系统中，Worker设备通过分布式访问点（APS）将本地更新的模型上传到参数服务器（PS），这些访问点（APS）在有限容量的Fronthaul链接上与PS通信。共同优化IRS的反射阶段和PS处的线性检测器的问题的问题是，目的是最大程度地减少PS估计的参数的平均平方误差（MSE）。数值结果验证了在C-RAN系统中使用优化阶段部署IRS的优势。
Over-the-air computation (AirComp) is an efficient solution to enable federated learning on wireless channels. AirComp assumes that the wireless channels from different devices can be controlled, e.g., via transmitter-side phase compensation, in order to ensure coherent on-air combining. Intelligent reflecting surfaces (IRSs) can provide an alternative, or additional, means of controlling channel propagation conditions. This work studies the advantages of deploying IRSs for AirComp systems in a large-scale cloud radio access network (C-RAN). In this system, worker devices upload locally updated models to a parameter server (PS) through distributed access points (APs) that communicate with the PS on finite-capacity fronthaul links. The problem of jointly optimizing the IRSs' reflecting phases and a linear detector at the PS is tackled with the goal of minimizing the mean squared error (MSE) of a parameter estimated at the PS. Numerical results validate the advantages of deploying IRSs with optimized phases for AirComp in C-RAN systems.
