Federated Learning for THz Channel Estimation

• URL: http://arxiv.org/abs/2207.06017v1
• Date: Wed, 13 Jul 2022 07:57:25 GMT
• Title: Federated Learning for THz Channel Estimation
• Authors: Ahmet M. Elbir and Wei Shi and Kumar Vijay Mishra and Symeon Chatzinotas
• Abstract summary: This paper addresses two major challenges in terahertz (THz) channel estimation: the beam-split phenomenon and computational complexity. 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. In this work, we employ federated learning (FL), wherein the users transmit only the model parameters instead of the whole dataset.
• Score: 44.058714794775995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 communications 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 technique without requiring additional hardware. Compared to the previous works, our method provides higher channel estimation accuracy as well as approximately $68$ times lower communications overhead.

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