PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-wave and Terahertz Ultra-Massive MIMO Systems

TL;DR

This paper introduces PRINCE, a deep learning-enhanced channel estimation method for ultra-massive MIMO systems in mmWave and THz bands, achieving high accuracy with reduced complexity through pruning techniques.

Contribution

The paper proposes a novel PRINCE method combining AMP and deep CNN with pruning, improving channel estimation accuracy and efficiency in hybrid UM-MIMO systems.

Findings

Achieves NMSE of -10 dB at 10 dB SNR after pruning 80% of feature maps.

Balances estimation accuracy with low computational complexity.

Demonstrates effectiveness in mmWave and THz ultra-massive MIMO scenarios.

Abstract

Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications. To compensate for the high propagation loss with reduced hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a hybrid beamforming structure is a promising technology in the mmWave and THz bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO systems, which requires recovering the high-dimensional channels from severely few channel observations. In this paper, a Pruned Approximate Message Passing (AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is firstly proposed, which enhances the estimation accuracy of the AMP method by appending a DCNN network. Moreover, by truncating the insignificant feature maps in the convolutional layers of the DCNN network, a pruning method including training with regularization, pruning and refining procedures is developed to reduce the network scale. Simulation results show that the PRINCE achieves a good trade-off between the CE accuracy and significantly low complexity, with normalized-mean-square-error (NMSE) of $-10$ dB at signal-to-noise-ratio (SNR) as $10$ dB after eliminating $80\%$ feature maps.