Quantized Deep Learning Channel Model and Estimation for RIS-gMIMO Communication

• Published in: IEEE open journal of the Communications Society Vol. 5; pp. 6932 - 6958
• Main Authors: Ghosh, Joydev, Vargas-Rosales, Cesar, Nhan Vo, Van, So-In, Chakchai
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 6G mobile communication; Accuracy; Channel estimation; Channel modeling; Channel models; Deep learning; Line of sight; Multilayer perceptrons; multiuser gigantic multiple-input multiple-output (MU-gMIMO); Optimization; quantized deep learning (qDL); Radiation; Radio frequency; reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Training; Wireless communication
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2024.3487847

Reconfigurable intelligent surfaces (RISs) and multiuser gigantic multiple-input multiple-output (MU-gMIMO) systems are key technologies for enabling sixth-generation (6G) networks. Their numerous advantages include minimal path losses, high energy efficiency (EE), high spectrum efficiency (SE), high data rates, and compatibility with line-of-sight (LoS) and non-LoS (NLoS) paths. However, RIS-gMIMO faces numerous challenges, including pilot overhead during beam training due to a combined radiation field, high training overhead due to the cascaded channels between transceivers, inaccurate channel state information (CSI) due to the rapidly changing RIS-user equipment (UE) channel, and low-accuracy channel estimation caused by semipassive RISs. With semipassive RIS-gMIMO communications, we present a novel quantized deep learning (qDL) channel model. This proposed channel model is constructed via a radio frequency (RF) chain matrix, a combined radiation field, and a truncated activation output. To enhance the feature extraction performance and reduce the loss of the model, a novel qDL-based channel estimation scheme is also proposed to concurrently utilize denoising multilayer perceptron (DnMLP) units to satisfy the imposed sparsity constraint. The qDL scheme outperforms the previously developed benchmark schemes in terms of accuracy and performance according to the normalized mean squared error (NMSE) of the simulation results.