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

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), hig...