Jul 2026· International Conference Computing Methodologies and Communication· pp. 667-673· 0 citations· 20 references
Abstract
With the advent of fifth-generation (5G) networks, the world has become wireless thanks to ultra-low latency, high bandwidth, and massive connectivity that have become an essential part of applications like autonomous vehicles, industrial IoT, smart cities, and real-time multimedia streaming. Conventional fixed or intuitive-based resource allocation schemes fail to adjust well to changing network states and user demands with varied needs leading to delay of service, congestion and the poor use of spectrum. In this paper, 5G NetOptima, which is an AI-based real-time resource allocation framework, is introduced and optimizes both bandwidth and latency at the same time. The suggested system uses machine learning models predicting the intelligent allocation decisions by analyzing network parameters such as user density, traffic type, channel quality, and the priority of services continuously. Dynamic priorities are given to latency sensitive and mission critical services, whereas bandwidth utilization is optimized over the entire bandwidth to improve the Quality of Service (QoS) and Quality of Experience (QoE). The vast simulations show that 5G NetOptima is more efficient than traditional methods of the allocation, as the 5G system facilitates the reduction of latency by the significant degree, enhanced the throughput, and enhanced the load balancing throughout the network. The most important novelty of the work is related to its combined AI-oriented structure which adjusts to all network parameters in real time, provides an efficient, scalable, and intelligent solution to 5G networks of the next generation.
The rapid deployment of fifth-generation (5G) mobile communication systems has significantly transformed wireless connectivity by supporting ultra-high data rates, ultra-low latency, massive machine-type communications, and heterogeneous Internet of Things (IoT) applications. However, the unprecedented growth in mobile traffic, dynamic user mobility, and diversified quality-of-service requirements have introduced substantial challenges in traffic prediction, congestion management, spectrum utilization, and network resource allocation. Conventional optimization techniques often fail to adapt to the highly dynamic and nonlinear characteristics of modern 5G environments. Deep learning has emerged as an effective paradigm for intelligent traffic optimization by learning complex spatial-temporal traffic patterns from large-scale network data and enabling proactive decision-making. Advanced architectures such as Long Short-Term Memory networks, Convolutional Neural Networks, Graph Neural Networks, Autoencoders, and Deep Reinforcement Learning provide enhanced capabilities for traffic forecasting, dynamic routing, load balancing, network slicing, edge intelligence, and energy-efficient resource management. This paper investigates the integration of deep learning algorithms into 5G traffic optimization frameworks, presents a comprehensive review of recent developments, proposes an intelligent AI-driven optimization architecture, evaluates major performance metrics, and discusses implementation challenges, scalability issues, and future research directions toward autonomous next-generation mobile networks.
Kamal N, Thirupathi Sundararajulu, A. R et al.· International journal of com...· 0 citations
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
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.
Mamoon M. Saeed, Rashid A. Saeed, M. A. Ahmed et al.· 2026 6th International Confe...· 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. J., Jeeva Jothi· International Journal of Com...· 0 citations
A constrained optimization model that supports different management goals through alternative objective functions (latency-aware or power-aware) while enforcing operational constraints, including node capacities, slice-specific latency bounds, and explicit limits on VNF migrations/relocations between scheduling periods is proposed.
R. Moreno-Vozmediano, E. Huedo, R. Montero et al.· Journal of Network and Syste...· 0 citations
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
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