AI FOR GREEN 6G: A REVIEW OF ENERGY-AWARE ROUTING TECHNIQUES

Abstract

The impending advent of Sixth-Generation (6G) wireless networks promises unprecedented performance, including tera-bit-per-second data rates and ultra-low latency. However, the energy consumption required to support such massive connectivity and computational demands poses a significant threat to global sustainability goals. Consequently, the concept of "Green 6G" has emerged, aiming to minimize the carbon footprint of network operations. This paper provides a comprehensive review of energy-aware routing techniques that leverage Artificial Intelligence (AI) to optimize energy efficiency in 6G networks. We analyze key AI paradigms, including Deep Reinforcement Learning (DRL), Federated Learning (FL), and Graph Neural Networks (GNNs), and their applications in intelligent routing decisions. The review highlights how these AI-driven techniques can dynamically manage network resources, predict traffic loads, and select energy-optimal paths, thereby reducing overall power consumption without compromising Quality of Service (QoS). The challenges of computational overhead, data privacy, and integration with novel 6G architectures like terahertz communication and network slicing are also discussed. This survey concludes that AI is not merely an enabler but a cornerstone for realizing sustainable and intelligent 6G networks, paving the way for an eco-friendly digital future.