Aug 2026· Journal of Artificial Intelligence· 0 citations· 17 references
TL;DR
It is concluded 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.
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.
—To reduce power consumption and extend network lifespan, academic and industrial groups have focused on energy-efficiency approaches for Next Generation Networks (NGNs). Fifth-generation (5G) networks offer a large number of services at high data rates, low latency, and massive connectivity. Increasing volumes of heterogeneous traffic from billions of devices, ranging from smartphones to intelligent transport systems, significantly challenge network resource utilization, particularly power consumption. This study targets energy-efficient resource allocation in sliced 5G systems, ensuring service-level guarantees for heterogeneous applications through intelligent optimization. This work proposes a novel hybrid optimization framework for energy-aware resource provisioning in 5G sliced networks using Hybrid Grey Wolf–Tasmanian Devil Optimization (HGWTDO) with a Linear Pattern Search (LPS) refinement technique. While HGWTDO combines the global search ability of Grey Wolf Optimization (GWO) and the exploitation abilities of the Tasmanian Devil Optimizer, the addition of LPS provides accurate local convergence. LPS has been integrated into the proposed solution to enhance optimization results. The solution is augmented with a Classification Tree-based classification that assigns users to their corresponding slices for Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and massive Machine-Type Communication (mMTC) based on quality of service (QoS) requirements. The suggested system provides improved power efficiency under QoS constraints and is an intelligent, scalable solution for energy-aware 5G network slicing compared with existing techniques.
P. Raddy, Sudhanva A M, Arathi R. Shankar· Journal of Communications So...· 0 citations
A structured review of optimization models in cloud and data center environments using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guided methodology covering literature from 2016 to 2025 reveals that the adaptive methods can improve throughput, reduce latency, and enhance energy efficiency under specific datasets, simulation settings, traffic models, and network configurations.
S. Alanazi· Journal of Advances in Infor...· 0 citations
This paper presents a comprehensive framework for artificial intelligence (AI)-enabled autonomous network slicing optimization in 6G systems and investigates the application of advanced machine learning paradigms specifically deep reinforcement learning, federated learning, and generative AI to orchestrate dynamic resource provisioning, cross-slice isolation, and proactive SLA (Service Level Agreement) enforcement.
N. P J, Jeeva Jothi· International Journal of Com...· 0 citations
Cognitive radio networks (CRNs) has significant potential for optimizing spectrum usage, but they still face issues, such as high energy consumption, increased transmission delay, and inefficient routing due to unpredictable network performance and spectrum variance. Relatively new developments, including deep reinforcement learning (DRL), allow for simultaneous routing and resource management, but still need substantial number of trial and error interactions with the environment, which can consume energy and lead to a significant convergence time. Maintaining optimal connectivity with both primary users (PUs) and secondary users (SUs) in large scale CRNs following a homogenous Poisson process while optimally utilizing spectrum access remains a challenge. Towards advancing these problems, we propose an energy-aware, cross-layer routing design based on an apprenticeship learning framework. Our multi-stage dynamic adjustment rating (DAR) mechanism allows for effective tuning of transmit power to decrease action space (following a multi-level transition) and reduce energy consumption. The Whale Swarm Optimization Algorithm (WsOA) allows us to provide predictions of link connectivity and path probability to ensure a more reliable routing selection is presented. To ensure faster convergence and a minimal memory footprint, we incorporate Deep Q-learning with a rapidly converging local search method based on permutation-equivariant neural networks for unseen environments in the given network scenarios. The simulation results show that the suggested approach outperforms conventional algorithms like CRQ-routing, PM-DQN, and other DQL methods in terms of throughput (100-70) pt/s, routing delay (5-1) ms, packet delivery ratio (100-70) % and Percentage packet loss (5-1) %.
M. Lakshmi, Arram. Mahesh Babu· Journal of Circuits, Systems...· 0 citations
A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.
M. Saeed, Rashid A Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 0 citations
A QoE-aware framework for Multi-Access Edge Computing-enabled Open Radio Access Network (O-RAN) architectures, combining a graph attention network (GAT) encoder, distributed multi-agent DRL, and privacy-preserving FL, while transitioning control from Quality of Service (QoS) to QoE metrics is proposed.
Manoj Prasad Kunasegran, Wai Leong Pang, S. K. Phang· IEEE Access· 0 citations
