Next-Generation 6G Wireless Networks: Challenges and Opportunities in Terahertz (THz) Communication and AI-Driven Signal Processing

TLDR

This work presents a comprehensive survey of THz channel characteristics and analyzes state-of-the-art AI techniques—such as deep reinforcement learning for beam alignment and federated learning for distributed optimization—that enhance the efficiency and reliability of THz networks.

Abstract

The advent of 6G wireless networks promises to revolutionize communication by leveraging terahertz (THz)

frequencies (0.1–10 THz) and artificial intelligence (AI) to achieve terabit-per-second (Tbps) speeds, ultra-low latency, and

ubiquitous connectivity. However, THz communication faces critical challenges, including severe propagation losses, molecular

absorption, and hardware limitations, which demand innovative solutions. This paper explores the synergistic role of AI-driven

signal processing in overcoming these barriers, focusing on adaptive beamforming, channel estimation, and resource allocation.

We present a comprehensive survey of THz channel characteristics and analyze state-of-the-art AI techniques—such as deep

reinforcement learning (DRL) for beam alignment and federated learning for distributed optimization—that enhance the

efficiency and reliability of THz networks. Furthermore, we identify open research challenges, including energy-efficient AI

deployment, security vulnerabilities, and standardization gaps. By bridging theoretical models with practical implementations,

this work provides a roadmap for realizing 6G’s potential, emphasizing the need for interdisciplinary collaboration across

wireless engineering, AI, and materials science. Our findings underscore AI as a pivotal enabler for scalable and intelligent

THz-based 6G networks, while highlighting future directions for industry and academia.