Jul 2026· International journal of computer science and mobile computing· Vol 15, pp. 11-32· 0 citations
TL;DR
This paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues and combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation in case of assessing and normalizing available resources.
Abstract
The 5G wireless networks are fast with low latency and support a very large number of connected devices, making it possible to support advanced applications and services. Such environment demands efficient management of network resources that can be achieved by evaluation of traffic load, distribution of devices, and state of signals to ensure quality-of-service (QoS) and network performance. Conventional resource allocation and mobility management techniques tend to have challenges of flexibility, computational effectiveness, as well as optimal choice of devices in disparate network conditions. In order to address these issues, this paper introduces a multi-stage resource management system that is adaptive in 5G networks to address QoS and mobility efficiency issues. The framework combines the set of network parameters with Adaptive Multivariate Kernel Resource Estimation (AMKRE) in case of assessing and normalizing available resources. Resource-Aware Device Selection (ORADS) is used to select the appropriate devices and then it is followed by Robust Filter-Based Rank Evaluation (RFBRE) where devices are ranked on the basis of reliability and link quality. The algorithm that optimizes the resources distribution taking into consideration the space constraints is known as DistanceAware Adaptive Particle Swarm Optimization (DAAPSO), and the algorithm that provides the continuity of mobility is known as Hybrid Predictive Soft Handover Control (HPSHC). The multi-dimensional paradigm improves the resource utilization, quality-of-service (QoS) provisioning, and mobility management in 5G networks.
A comprehensive survey of AI-enabled mobility management strategies for 5G, Beyond 5G, and upcoming 6G networks, with particular attention to HO optimization and load balancing is presented.
H. Asif, Abdulraqeb Alhammadi, N. Tarhuni et al.· Future Internet· 0 citations
As mobile cellular communication technology advances and wireless mobile devices become increasingly popular, multimedia applications have become essential in modern society. Long‐term evolution (LTE), launched in 2009, has established itself as a leading cellular technology by providing high‐speed data and media transport. Its successor, LTE‐Advanced (LTE‐A), further enhances this capability with greater bandwidth and peak data rates, enabling effective multimedia transmission beyond traditional voice communication. To ensure consumer satisfaction, high quality of service (QoS) is critical. This paper presents an innovative resource allocation scheme tailored for various services in the downlink of LTE systems, emphasizing service differentiation as a vital factor for QoS, particularly for real‐time traffic. We provide a comprehensive overview of LTE architecture, its components, and their interconnections, followed by an in‐depth discussion of resource allocation, scheduling techniques, and QoS mechanisms in LTE. Furthermore, we review existing studies and algorithms, leading to the introduction of our proposed Two‐stage resource allocation with user satisfaction (TSRAUS) algorithm. Key findings demonstrate that the TSRAUS algorithm significantly improves resource utilization and user experience by effectively managing priorities between real‐time and nonpriority applications. Comprehensive statistical analysis, including confidence intervals and significance tests, rigorously validates the TSRAUS algorithm's superior performance across key QoS metrics. The conclusion summarizes these findings and highlights potential future enhancements, including the extension of the algorithm to multicell scenarios, user mobility considerations, and adaptive mechanisms to better address evolving network demands.
Samar Sindian, Eman Chehade, Hadeel Sleem· International Journal of Com...· 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
The deployment of Ultra-Dense Networks (UDNs) in 5G systems is to meet the growing demand for high data rates and massive connectivity. However, the dense deployment of small cells increases handover frequency, leading to challenges such as handover failures (HOF), unnecessary handovers, and the ping-pong effect, leading to degradeuser Quality of Service (QoS). This paper proposes a velocity-aware adaptive handover control approach for efficient mobility management in 5G ultra-dense networks. The proposed approach dynamically adjusts Handover Control Parameters (HCPs) called Time-to-Trigger (TTT) and Handover Margin (HOM) on the real-time velocity of User Equipment (UE) and signal conditions. The system is modeled as a two-tier heterogeneous network consisting of a macrocell overlaid with multiple small cells, and performance is evaluated using the Cost 231-Hata propagation model. The findings demonstrate that the proposed algorithm significantly reduces the total number of handovers, mitigates the ping-pong effect, and lowers handover failure rates compared to conventional static schemes. The results confirm that velocity-aware adaptive control enhances network reliability, reduces signaling overhead, and improves overall mobility performance in 5G ultra-dense environments.
Halah Hassen Aldumaini, Hanadi Esmeail Yahya, Oloof Ameen Mohmmed et al.· 2026 6th International Confe...· 0 citations
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
