NEAR FIELD BEAM FORMING IN TERAHERTZ WIDEBAND MASSIVE MIMO SYSTEMS USING CODEBOOK LEARNING

Abstract

Terahertz (THz) communication technology holds immense potential for enabling ultra-high data rates in wireless communication networks. However, the highly directional nature of THz waves poses significant challenges for achieving precise beam focusing, especially in near-field scenarios. In this study, we propose a novel approach leveraging deep learning techniques for near-field beam focusing in Terahertz Wideband Massive Multiple Input, Multiple Output (MIMO) systems. Our methodology's main novelty is the combination of codebook learning with deep neural networks. We use a convolutional neural network (CNN) architecture to learn complex spatial properties of THz channels and optimise beamforming weights. During the training phase, the codebook, which represents a discrete collection of beamforming vectors, is adaptively modified, allowing for dynamic response to changing channel circumstances. Extensive tests are being carried out to confirm the efficacy of our strategy, employing cutting-edge THz transceivers and MIMO technology. The proposed deep learning model is trained and evaluated using real-world channel data. Comparative comparisons with traditional beamforming approaches demonstrate our methodology's better performance, revealing significant advances in spectral efficiency and signal quality. This paper highlights the transformational influence of codebook learning linked with deep neural networks in boosting the performance of near-field beam focusing in Terahertz Wideband Massive MIMO systems. It not only provides a major achievement in the field of THz communications. Key Words: THz Communication, deep neural networks, MIMO, beamforming, deep learning.