AI-Driven Reconfigurable Intelligent Surface-Assisted Massive MIMO-OFDM for Terahertz 6G Networks

Paper ID: EIJTEM_2025_12_1_25-30

Author's Name: Senthil Kumar M, Sapthagiri Vienala

Volume: 12

Issue: 1

Year: 2025

Page No: 25-30

Abstract:

The sixth generation (6G) of wireless communication aims to achieve unprecedented data rates, ultra-low latency, and massive connectivity while ensuring sustainable energy efficiency. Among various enabling technologies, Massive Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) have emerged as the backbone of spectral-efficient systems. However, their scalability in high-frequency terahertz (THz) bands introduces severe propagation losses and power inefficiencies. To address this challenge, this paper proposes an AI-driven Reconfigurable Intelligent Surface (RIS)-assisted Massive MIMO-OFDM framework for energy-efficient 6G communication networks. The proposed model integrates deep reinforcement learning (DRL) with adaptive hybrid beamforming to dynamically optimize RIS phase shifts and MIMO precoding under varying channel conditions. By modeling the joint optimization of energy efficiency (EE) and spectral efficiency (SE) as a multi-objective learning problem, the system achieves an intelligent trade-off between power consumption and throughput. Simulation results demonstrate that the proposed AI-RIS-MIMO-OFDM framework achieves up to 32% higher energy efficiency and 27% improved spectral efficiency compared to conventional MIMO-OFDM systems without RIS. The study establishes a foundation for sustainable, adaptive, and intelligent physical layer design in future 6G networks.

Keywords: 6G Communication, Massive MIMO, OFDM, Reconfigurable Intelligent Surface (RIS), Deep Reinforcement Learning (DRL), Energy Efficiency, Hybrid Beamforming, Terahertz Communication, Channel Optimization, AI-Enabled Wireless Systems.

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