Jul 2026· International Journal of Sensors Wireless Communications and Control· Vol 16· 0 citations
TL;DR
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.
Abstract
The increasing density of 5G and beyond networks has greatly increased
energy demands, making energy efficiency an essential objective for next-generation systems.
While cell sleeping is a well-known technique used to reduce consumption, most existing schemes
fall short in maintaining service continuity and fail to address the long delays introduced by hardware
reactivation. These limitations can severely affect the Quality of Service (QoS), especially for
applications requiring low latency and high reliability.
This study introduces RIS-Assisted Safe Sleeping (RASS), a unified framework that enables
dense small cell deployments to save energy without sacrificing user experience. RASS integrates
three complementary mechanisms, namely a coverage-safety certificate that verifies robust
SINR before deactivation, RIS resource slicing that allocates reflection elements for both coverage
support and wake-up signalling, and an RIS-assisted paging channel that reduces the visible effect
of hardware wake-up delay.
Through systematic sensitivity analysis, we identify that RASS performs best with a slicing
factor of α = 0.7 and moderate coverage thresholds between -6 and -4 dB. Simulation results
demonstrate significant benefits over conventional schemes: the safe-sleep ratio rises substantially,
blocking probability falls by as much as 40%, and energy efficiency improves by more than 20%.
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.
Overall, RASS provides an efficient framework for improving energy efficiency in
ultra-dense 5G and beyond networks while maintaining a reliable user experience.
—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
This research Paper focuses on an Ultra-Dense Network (UDN) is a core enabling technology for 5G and 6G wireless systems, proposed to meet escalating capacity demands and support new high-rate, low-latency services. The fundamental principle is network densification, achieved by deploying a massive number of low-power Access Points (APs) and communication links per unit area, dramatically shortening the distance between transmitters and receivers to improve signal quality and spatial frequency reuse. Ultimately, the successful operation of UDNs relies heavily on advanced, AI-driven management systems to dynamically optimize resources, manage interference, and ensure seamless, high-performance connectivity in an inherently complex environment: Our key objectives are improving the Massive Capacity and Data Rates, Enhanced Coverage and Reliability, Ultra-Low Latency, Massive Connectivity Internet of Things (IoT) to maintaining the advanced resilient communication system all the time and every times.
P. Pradhan, Pramod D Gangejar· Journal of Ad-hoc Network an...· 0 citations
The development of sixth-generation (6G) mobile communications aims to deliver super-fast connections at terahertz frequencies, surpassing those of 5G. Nonetheless, these improvements come with challenges of data leakage and packet misinsertion, and they place greater pressure on secure and efficient routing. An Energy Optimized Network Route Cluster Bandwidth (EONRCB) is an Enhanced Service Data Transmission (ESDT) model proposed in this paper to circumvent these problems. The model contains several significant factors, such as Transmission Node Support Weight (TNSW), which evaluates network traffic and petite access control, which provides packet fragmentation and a secure route. Then, the Service Level Route Count Rollback Node Aggregator (SLR-CRNA), which validates active route node interaction. Further, the Sleeper Node Controller improves the consistency of node operations, whereas Slicing Neighbour Node Path Routing (SN2PR) introduces consistency in the source-to-destination connectivity within edge networks. Additional optimisation can be achieved through functionality such as Priority Cycle Tags (PCT) and Recursive Scheduling Time (RST), which provide mechanisms to improve overall RTPS throughput and node scheduling by minimising latency via a depth-first task alignment scheme. The suggested architecture achieves higher energy efficiency, reduced congestion, and a longer network lifespan. The performance of the proposed 6G framework will be validated against existing models to assess its effectiveness.
Sangeetha E, D. J· Journal of universal compute...· 0 citations
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
William Hughes, Marta Silva· International Journal of Dat...· 0 citations
This paper conducts a comprehensive literature survey to examine existing optimization techniques and proposes an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing that demonstrates the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability.
Kenji Sato· International Journal of Mod...· 0 citations
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yichen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 0 citations