Federated Multi-Task Learning for THz Wideband Channel and DoA 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 introduce a federated
multi-task learning (FMTL), wherein the users transmit only the
model parameters instead of the whole dataset, for THz channel
and user direction-of-arrival (DoA) estimation to improve the
communications-efficiency. We first propose a novel beamspace
support alignment technique for channel estimation with beamsplit
correction. Then, the channel and DoA information are used
as labels to train an FMTL model. 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 FMTL approach provides higher channel
estimation accuracy as well as approximately 25 (32) times lower
model (channel) training overhead, respectively.