Title: A beamspace channel estimation based on deep convolutional reconstruction networks

Authors: Teng Fei; Zhengyu Zhu; Jingyu Zhang; Lanxue Liu; Xinzong Yang

Addresses: School of Information Engineering, Tianjin University of Commerce, Tianjin 300400, China ' School of Information Engineering, Tianjin University of Commerce, Tianjin 300400, China ' China Mobile Group Xinjiang Company Limited, Beijing, 100000, China ' School of Information Engineering, Tianjin University of Commerce, Tianjin 300400, China ' School of Information Engineering, Tianjin University of Commerce, Tianjin 300400, China

Abstract: One major challenge in millimetre-wave massive multiple-input multiple-output (MIMO) systems is achieving precise channel estimation, which still faces low accuracy and reliance on prior channel information. This paper proposes a novel beamspace channel estimation algorithm using a deep convolutional reconstruction network called DeRePixNet without requiring prior channel information. The multi-scale fusion module (MSFM) is designed to form a rich feature mapping in this network. MSFM and residual block (RB) are organically combined to prevent gradient vanishing while the network depth increases, to identify efficient local sparse structures in a convolutional visual network and replicate it spatially. The inverse transformation process from measurement vectors to the original channel is solved directly using DeRePixNet in a data-driven manner. We conducted theoretical derivations and system simulations based on the Saleh-Valenzuela channel model. The proposed DeRePixNet demonstrates superior performance compared to most existing methods. Compared to the orthogonal matching pursuit, approximate message passing learned approximate message passing, and Gaussian mixture learned approximate message passing algorithms, DeRePixNet reduces the average normalised mean squared error by approximately 11.14 dB, 8.95 dB, 1.98 dB, and 1.19 dB, respectively.

Keywords: deep convolutional reconfiguration networks; millimetre wave; massive MIMO; channel estimation; multi-scale fusion module; MSFM.

DOI: 10.1504/IJSNET.2025.144553

International Journal of Sensor Networks, 2025 Vol.47 No.2, pp.88 - 97

Received: 08 Jul 2024
Accepted: 16 Jul 2024
Published online: 19 Feb 2025 *

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