2026· Journal of Communications Software and Systems· 0 citations· 29 references
Abstract
—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.
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.
Joshna M, R. K.· Journal of Artificial Intell...· 0 citations
Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.
H. Lasisi, H. B. Omodeni, B. Aderinkola et al.· 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
The results show that integrating coverage validation, RIS resource allocation, and wakeup delay mitigation enables RASS to balance energy efficiency while ensuring service is available, providing an efficient framework for improving energy efficiency in ultra-dense 5G and beyond networks while maintaining a reliable user experience.
M. Aal-nouman, Sarmad M. Hadi, Hamzah M. Marhoon· International Journal of Sen...· 0 citations
The exponential growth in mobile data traffic has driven a corresponding increase in Radio Access Network (RAN) energy consumption, making energy efficiency a critical priority for mobile operators. This paper presents a large-scale empirical evaluation of multi-generation (3G-5G) power-saving features deployed in a live commercial network. Using a controlled sample of 45 sites equipped with remote power meters, we quantify the individual and cumulative energy savings of four commercially available features. Through baseline measurements, sequential activation, and controlled rollback experiments, we isolate the contribution of each feature and assess its impact on key performance indicators and user experience. The combined activation offsets the power increase from network modernization, achieving a net saving of 15.78% in regional lower-load areas without measurable degradation in accessibility, retainability, or throughput. The micro-DTX feature emerges as the dominant contributor, delivering load-dependent savings of 7-12.5%. Comparative analysis between lower-load regional and higher-load urban environments reveals a strong dependency of achievable savings on traffic load and activation window configuration. These findings provide a replicable, data-driven framework for operators implementing energy-efficient RAN strategies while maintaining quality of service, and highlight critical trade-offs between aggressive power reduction and user experience.
A. Ayvazyan, Ashot Khudaverdyan, L. Husikyan et al.· Educational Data Mining· 0 citations
With the rapid proliferation of high-bandwidth and low-latency services such as virtual reality (VR), holographic communications, and large-scale Internet of Things (IoT), the complexity of network resource management has increased significantly. Network resource optimization plays a crucial role in improving throughput, reducing latency, and enhancing energy efficiency by enabling efficient utilization of limited wireless and wired resources. Conventional approaches, including rule-based methods, mathematical optimization, and reinforcement learning-based techniques, can achieve satisfactory performance in specific environments. However, they suffer from limitations such as poor generalization to dynamic environments, high modeling complexity, and difficulties in real-time decision-making. To overcome these limitations, recent studies have begun to explore network resource optimization based on Large Language Models (LLMs). This paper presents a comprehensive survey of LLM-based network resource optimization techniques. Existing studies are classified according to the role of LLMs, and the characteristics and limitations of each approach are analyzed.
Junyoung Park, Woongsoo Na· International Conference on...· 0 citations
