Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems
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Abstract (arXiv)
Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies across the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Feb 2021.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
7 papers.
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimizationcited by 56
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Surveycited by 6
- 2021Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamformingcited by 5
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMOcited by 4
- 2022Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networkscited by 2
- 2022Federated Multi-Task Learning for THz Wideband Channel and DoA Estimationcited by 0
- 2026Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMOcited by 0
- 2026Recurrent Transformer-Based Near- and Far-Field THz Wideband Channel Estimation for UM-MIMO12 May 2026
- 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey3 Aug 2025
- 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization10 Aug 2023
- 2022Deep Learning-Based Rate-Splitting Multiple Access for Reconfigurable Intelligent Surface-Aided Tera-Hertz Massive MIMO18 Sep 2022
- 2022Federated Multi-Task Learning for THz Wideband Channel and DoA Estimation13 Jul 2022
- 2022Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks10 May 2022
- 2021Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming27 Feb 2021