Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based receiver designs rely on accurate knowledge of this underlying distribution, which, in practice, may be complex and intractable. In this work, we propose a neural network-based symbol detection technique for downlink fading channels, which is based on the maximum a-posteriori probability (MAP) detector. To enable training on a diverse ensemble of fading realizations, we propose a federated training scheme, in which multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics, while inducing a substantially reduced communication overhead in its training procedure compared to centralized training.
翻译:无线通信往往会通过渠道消失。提出了各种统计模型,以捕捉消逝的内在随机性,传统的基于模型的接收器设计依靠对这一基本分布的准确了解,而这种分布实际上可能复杂而棘手。在这项工作中,我们提议对下链接淡化渠道采用基于神经网络的标志检测技术,该技术以最大速率(MAP)探测器为基础。为了能够就各种逐渐消退的发现组合进行培训,我们提议了一个联合培训计划,让多个用户合作共同学习一个通用的数据驱动探测器,因此名为FedRec。结果接收器的表现显示,在不要求了解淡化统计数据的情况下,在不同渠道条件下接近MAP的性能,与此同时,与集中培训相比,其培训程序中的通信间接费用大大减少。