Federated Learning for THz Channel Estimation

This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon, i.e., beam misalignment because of frequency-independent analog beamformers, and computational complexity because of the usage of ultra-massive number of antennas to compensate propagation losses. Data-driven techniques are known to mitigate the complexity of this problem but usually require the transmission of the datasets from the users to a central server entailing huge communication overhead. In this work, we employ federated learning (FL), wherein the users transmit only the model parameters instead of the whole dataset, for THz channel estimation to improve the communications-efficiency. In order to accurately estimate the channel despite beam-split, we propose a beamspace support alignment (BSA) technique. By exploiting the sparsity of the THz channel, the proposed approach is implemented with fewer pilot signals than the traditional techniques. Compared to the previous works, our FL-BSA approach provides higher channel estimation accuracy as well as approximately 68 (32) times lower model (channel) training overhead, respectively.