Deep Learning Model-Based Channel Estimation for THz Band Massive MIMO with RF Impairments

TL;DR

This paper proposes a deep learning-based channel estimation method for THz band massive MIMO systems that accounts for RF impairments, using a BiLSTM-GRU neural network to improve robustness and performance.

Contribution

It introduces a novel RNN-based neural network architecture specifically designed to handle RF impairments in large-scale THz M-MIMO channel estimation.

Findings

Outperforms benchmark methods at various SNR levels

Effectively models RF impairments as sequential noise

Enhances robustness of channel estimation in practical scenarios

Abstract

THz band enabled large scale massive MIMO (M-MIMO) is considered as a key enabler for the 6G technology, given its enormous bandwidth and for its low latency connectivity. In the large-scale M-MIMO configuration, enlarged array aperture and small wavelengths of THz results in an amalgamation of both far field and near field paths, which makes tasks such as channel estimation for THz M-MIMO highly challenging. Moreover, at the THz transceiver, radio frequency (RF) impairments such as phase noise (PN) of the analog devices also leads to degradation in channel estimation performance. Classical estimators as well as traditional deep learning (DL) based algorithms struggle to maintain their robustness when performing for large scale antenna arrays i.e., M-MIMO, and when RF impairments are considered for practical usage. To effectively address this issue, it is crucial to utilize a neural network (NN) that has the ability to study the behaviors of the channel and RF impairment correlations, such as a recurrent neural network (RNN). The RF impairments act as sequential noise data which is subsequently incorporated with the channel data, leading to choose a specific type of RNN known as bidirectional long short-term memory (BiLSTM) which is followed by gated recurrent units (GRU) to process the sequential data. Simulation results demonstrate that our proposed model outperforms other benchmark approaches at various signal-to-noise ratio (SNR) levels.