Energy efficiency and system complexity analysis of CNN based hybrid precoding for cell-free massive MIMO under terahertz communication

Abstract

The integration of terahertz (THz) communication with cell-free massive multiple-input multiple-output (CFMM) systems presents a promising strategy to enhance energy efficiency and reduce system complexity in future wireless networks. However, this integration faces significant challenges, such as dynamic and unpredictable channel behavior. Traditional channel estimation techniques are inadequate for handling these dynamic conditions. To address these issues, a convolutional neural network (CNN)-based hybrid precoding scheme is proposed for CFMM systems operating at THz frequencies. This method leverages CNN to predict optimal precoding weights, significantly improving the adaptability of hybrid precoding. The CNN-based model not only mitigates pilot contamination (PC) but also enhances channel estimation by capturing temporal and spatial dynamics. Simulation results indicate that the CNN-based approach achieves superior energy efficiency and lower system complexity compared to conventional techniques. At a signal-to-noise ratio (SNR) of 30 dB, it achieves 1.2 bits per joule and reduces system complexity to 1,400 FLOPs, demonstrating better scalability and resource optimization. These findings highlight the potential of CNN-based hybrid precoding to revolutionize THz communication in next-generation wireless networks by optimizing energy efficiency and system complexity.