Beamspace Channel Estimation in Terahertz Communications: A Model-Driven Unsupervised Learning Approach

06/30/2020
by   Hengtao He, et al.
0

Terahertz (THz)-band communications have been one of the promising technologies for future wireless networks that integrate a wide range of data-demanding applications. To compensate for the large channel attenuation in THz band and avoid high hardware cost, a lens-based beamspace massive multiple-input multiple-output (MIMO) system is considered. However, the beam squint effect appeared in wideband THz systems, making channel estimation very challenging, especially when the receiver is equipped with a limited number of radio-frequency (RF) chains. Furthermore, the real channel data cannot be obtained before the THz system is used in a new environment, which makes it impossible to train a deep learning (DL)-based channel estimator using real data set beforehand. To solve the problem, we propose a model-driven unsupervised learning network, named learned denoising-based generalized expectation consistent (LDGEC) signal recovery network. By utilizing the Steins unbiased risk estimator loss, the LDGEC network can be trained only with limited measurements corresponding to the pilot symbols, instead of the real channel data. Even if designed for unsupervised learning, the LDGEC network can be supervisingly trained with the real channel via the denoiser-by-denoiser way. The numerical results demonstrate that the LDGEC-based channel estimator significantly outperforms state-of-the-art compressive sensing-based algorithms when the receiver is equipped with a small number of RF chains and low-resolution ADCs.

READ FULL TEXT

page 7

page 14

research
02/05/2018

Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems

Channel estimation is very challenging when the receiver is equipped wit...
research
10/28/2019

Knowledge-Aided Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a ...
research
02/07/2022

Deep Learning based Channel Estimation for Massive MIMO with Hybrid Transceivers

Accurate and efficient estimation of the high dimensional channels is on...
research
01/16/2018

Pilot Contamination Mitigation with Reduced RF Chains

Massive multiple-input multiple-output (MIMO) communication is a promisi...
research
05/04/2020

DeepRx: Fully Convolutional Deep Learning Receiver

Deep learning has solved many problems that are out of reach of heuristi...
research
12/31/2021

Efficient Channel Estimation for RIS-Aided MIMO Communications with Unitary Approximate Message Passing

Reconfigurable intelligent surface (RIS) is very promising for wireless ...
research
04/21/2023

An Orchestration Framework for Open System Models of Reconfigurable Intelligent Surfaces

To obviate the control of reflective intelligent surfaces (RISs) and the...

Please sign up or login with your details

Forgot password? Click here to reset