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

arXiv: https://arxiv.org/abs/2207.06017 · HTML: https://mlsanity.com/paper/2207.06017/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 13 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Wideband Channel Estimation with A Generative Adversarial Network](https://mlsanity.com/paper/2012.12063/) (2020)
- [An Overview of Signal Processing Techniques for Terahertz Communications](https://mlsanity.com/paper/2005.13176/) (2020)
- [Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems](https://mlsanity.com/paper/2102.06772/) (2021)
- [Federated Learning for Physical Layer Design](https://mlsanity.com/paper/2102.11777/) (2021)
- [Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming](https://mlsanity.com/paper/2103.00328/) (2021)
- [Deep Learning-based Channel Estimation for Wideband Hybrid MmWave Massive MIMO](https://mlsanity.com/paper/2205.05202/) (2022)
- [Wideband Channel Estimation for THz Massive MIMO](https://mlsanity.com/paper/2108.08173/) (2021)

## Abstract (arXiv)

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 beam-split 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.