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This paper addresses two major challenges in terahertz (THz) channelestimation: the beam-split phenomenon, i.e., beam misalignment because offrequency-independent analog beamformers, and computational complexity becauseof the usage of ultra-massive number of antennas to compensate propagationlosses. Data-driven techniques are known to mitigate the complexity of thisproblem but usually require the transmission of the datasets from the users toa central server entailing huge communication overhead. In this work, weintroduce a federated multi-task learning (FMTL), wherein the users transmitonly the model parameters instead of the whole dataset, for THz channel anduser direction-of-arrival (DoA) estimation to improve thecommunications-efficiency. We first propose a novel beamspace support alignmenttechnique for channel estimation with beam-split correction. Then, the channeland DoA information are used as labels to train an FMTL model. By exploitingthe sparsity of the THz channel, the proposed approach is implemented withfewer pilot signals than the traditional techniques. Compared to the previousworks, our FMTL approach provides higher channel estimation accuracy as well asapproximately 25 (32) times lower model (channel) training overhead,respectively.

Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation