Federated Channel-Beam Mapping: from sub-6GHz to mmWave

Accurate beamforming is a critical challenge for mmWave communications. Because of the large training overhead of beam training at high frequencies, it becomes relevant to exploit the available knowledge at sub-6GHz to predict the mmWave beamforming vectors using deep learning tools. In addition, fully centralized learning (CL) approaches require training over all the users data, rising major issues in terms of signaling and computational cost. To address these issues, we propose a federated learning (FL) scheme in a wireless network composed of multiple communicating links (access points – users) to predict directly the downlink mmWave beamforming vectors from the uplink sub-6GHz channels. The access points train their local deep neural networks using local data and only share their model parameters to obtain an average global one, which improves the quality of their prediction in terms of data rate. Our experiments demonstrate the potential and robustness of our proposed scheme especially under difficult conditions, performing close to the fully centralized one. When the training data is scarce, the relative gain of our scheme can reach up to 50% compared to a fully distributed one. Remarkably, our scheme can even outperform the fully centralized one when the quality of the training data is poor, enjoying a relative gain of up to 14%.