Jun 2026· Educational Data Mining· pp. 1-4· 0 citations· 10 references
Abstract
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.
We introduce an open live multi-technology cellular network dataset derived from real, operational base stations of a commercial mobile operator in Slovakia. It provides a rare look inside live 2G, 4G and 5G Radio Access Networks (RANs) through Performance Management (PM) counters collected at 15-minute intervals and mapped to cell-level granularity. Each baseband processes multiple radio configurations, enabling multi-band and multi-generation correlation studies across real network deployments. Unlike any previously released resource, this dataset exposes real energy consumption values alongside radio and user-level performance indicators such as utilisation, Channel Quality Indicator (CQI), number of Radio Resource Control (RRC) connected and active users, uplink and downlink data volume and Multiple-Input Multiple-Output (MIMO) rank for all network cells where applicable. It supports research in energy-aware RAN optimisation, data-driven network management and cross-layer performance modelling. The dataset has been validated for internal consistency and is released under the Creative Commons Attribution (CC BY) license to foster reproducible and open telecommunications research.
Peter Lehoczký, Matúš Turcsány, L. Krajčovičová et al.· Scientific Data· 0 citations
This review systematically traces how mobile network technology has evolved from second-generation (2G) through fifth-generation (5G) systems, looks at how each generation has been applied in smart grid settings, and sets out what each generation could and could not do.
Musaab Abdelmageed Abdelraheem Abdalla· International Journal of Sci...· 0 citations
—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
As network traffic grows, reducing energy costs and $\mathbf{C O}_{2}$ emissions is essential for sustainable telecom operations. Energy efficiency (EE) is therefore a key design objective in 5G Radio Access Networks (RAN), where computing resources in the Central Unit (CU) and Distributed Unit (DU) must be managed while meeting Quality of Service (QoS) requirements. This work focuses on the CU/DU computing platform, a major contributor to RAN power consumption due to continuous baseband processing. We present a measurement-based framework that dynamically adjusts CPU core count and clock frequency to minimize the energy consumption while meeting the data rate requirements of User Equipment (UE). The proposed algorithm selects the most energy-efficient CPU configuration based on experimentally derived power-performance profiles from a practical 5G RAN testbed. Experimental evaluation on two RAN platforms shows power savings of up to 16% and 35%, respectively, compared to a baseline configuration with all CPU cores active at the maximum base clock frequency. The results demonstrate the effectiveness of the proposed framework for energy-efficient operation in practical 5G RAN deployments.
Ramagiddaiah Eediga, Preethi G V, Anand Svr et al.· International Conference on...· 0 citations
Bridging the rural digital divide remains a persistent global challenge despite the widespread deployment of 4G and 5G cellular networks. This paper presents a comprehensive survey and quantitative analysis of network architectures for enabling future sixth-generation (6G) services in rural and underserved regions. Drawing on a structured review of peer-reviewed studies, 3GPP and ITU-R standards, O-RAN Alliance specifications, and documented field deployments, we classify and evaluate four architectural categories: terrestrial networks (TNs), non-terrestrial networks (NTNs), hybrid TN–NTN systems, and Open Radio Access Network (O-RAN)-based deployments. Each architecture is assessed against International Mobile Telecommunications for 2030 (IMT-2030) performance targets for throughput, end-to-end round-trip latency, energy efficiency (expressed as energy consumed per bit), coverage, and reliability. Unlike prior surveys that address individual components in isolation, such as satellite backhaul, microwave transport, or O-RAN frameworks, this paper provides a unified, deployment-driven, and quantitatively grounded treatment of all four architectural classes under rural 6G constraints. The survey analyzes key enabling technologies including Integrated Access and Backhaul (IAB), High-Altitude Platform Stations (HAPSs), Low-Earth Orbit (LEO) satellite systems, Reconfigurable Intelligent Surfaces (RIS), and AI-native orchestration frameworks. Analytical models are presented for RIS-assisted link enhancement and AI-native RAN Intelligent Controller (RIC) control utility. A quantitative, normalized key performance indicator (KPI) comparison across cost efficiency, spectral utilization, power consumption, and deployment scalability is grounded in a transparent scoring methodology, extended with a multi-criteria (AHP) architecture ranking, a weight-sensitivity analysis, and a parametric techno-economic cost-per-user comparison, and supported by an extensive review of technical, industrial, and policy literature. Beyond current deployments, this work identifies emerging technologies, including direct-to-device non-terrestrial access, programmable radio environments, and intelligent RAN control, that are expected to play a central role in extending sustainable rural 6G connectivity. A practical deployment decision framework and scenario-driven architectural guidance are provided for researchers, network operators, and policymakers pursuing inclusive, future-ready rural connectivity.
Souradeep Deb, Nishith D. Tripathi, Jeffrey H. Reed· IEEE Open Journal of the Com...· 0 citations
The fifth generation (5G) of mobile communication is expected to deliver ultra-low latency, massive connectivity, and multi-gigabit throughput, but these benefits are hard to achieve with computation being centralized in a remote cloud data center. To overcome this limitation, Multi-access Edge Computing (MEC) moves the computation, storage and application logic to the edge of the network near the location where the data is generated. In this paper, the performance of an Edge-Computing-assisted 5G network is analyzed using the MATLAB 5G Toolbox in combination with the MATLAB 5G System Level and Link Level simulation framework. The layered architecture is proposed and mathematically modeled with regard to queuing delay, task-offloading decisions and energy consumption, considering 5G New Radio (NR) access network, Multi-access Edge Computing (MEC) layer and centralized cloud layer. The simulation is designed as a discrete-event system-level simulation with realistic 5G NR numerology, a large number of user equipment (UEs) and varying edge-server capacities to measure latencies, throughput, packet delivery ratio (PDR), response time, energy consumption, bandwidth utilization and reliability. Edge-assisted offloading shows an average end-to-end delay of 46-58% lower than a traditional cloud-only architecture and a maximum of 61% delay reduction in response time compared to the conventional cloud-only architecture, while maintaining high packet delivery ratio and reliability under heavy user density, while consuming a small amount of extra energy at the edge servers. These results validate MEC as a key enabling complementary technology for 6G networks with its supporting use cases for Ultra-Reliable Low-Latency Communication (URLLC); and the paper also reveals open research challenges in adaptive resource allocation, security and AI-based orchestration for future edge architectures in 6G.
Jayant Pratap, Amandeep, Dharmender Kumar, Suraj S· International Journal of Adv...· 0 citations