In this paper, an Over-the-Air Computation (AirComp) scheme for fast data aggregation is considered. Multisource data are simultaneously transmitted by single-antenna mobile devices to a single-antenna fusion center (FC) through a wireless multiple-access channel. The optimal power levels at the devices and a postprocessing scaling function at the FC are jointly derived such that mean square error of the computation is minimized. Different than the existing approaches that rely on perfect channel state information (CSI) at the FC and assume that the devices’ optimal power levels can be selected from an infinite solution set, in the present paper, it is assumed that only quantized CSI is available at the FC and that the aforementioned optimal power levels lie in a finite discrete set of solutions. To derive the optimal power levels and FC’s scaling factor, a difficult nonconvex constrained optimization problem is formulated. An efficient and robust solution to quantization errors is developed via the deep reinforcement learning framework. Numerical results verify the good performance of the proposed approach while it exhibits a significant reduction in the required feedback.

中文翻译：

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具有量化 CSI 和离散功率控制级别的无线计算

在本文中，考虑了一种用于快速数据聚合的空中计算（AirComp）方案。多源数据由单天线移动设备通过无线多址信道同时传输至单天线融合中心（FC）。设备的最佳功率水平和 FC 的后处理缩放函数是共同导出的，以便最小化计算的均方误差。与依赖 FC 处的完美信道状态信息 (CSI) 并假设可以从无限解集中选择设备的最佳功率水平的现有方法不同，在本文中，假设只有量化的 CSI 可用在 FC 处，并且上述最佳功率水平位于一组有限离散解中。为了导出最佳功率水平和 FC 的比例因子，制定了一个困难的非凸约束优化问题。通过深度强化学习框架开发了一种有效且稳健的量化误差解决方案。数值结果验证了所提出的方法的良好性能，同时它显示出所需反馈的显着减少。