Wireless Channel Estimation using Federated Learning

Contemporary Machine Learning (ML) techniques can now be conveniently applied to the wireless channel estimation problem to reduce the estimation complexity and enhance the system's overall data rate. Traditionally, the wireless channel is estimated at the base station (BS), where BS receives the channel information from the users via the feedback channel. This poses a potential problem with the privacy and security of users' channel information as well as requires large bandwidth for the feedback channel in a system with a large number of users. To address, the dual issues of users' privacy and lessening the required bandwidth for the feedback channel, this paper proposes a Federation Learning (FL) based wireless channel estimation solution for a commonly used wireless communication system. In this technique, each user estimates its own channel and only shares the model parameters with the BS which then combines the information from all users to construct a joint wireless channel to be used for transmission for the next time slot. To evaluate the accuracy of proposed model, we used Stochastic Gradient Descent (SGD) to optimise the solution and compared the results with the results presented in [1]. The numerical results show that the proposed model renders more stable and accurate estimation results in conjunction with the use of SGD and FL.