Intelligent traffic load optimization and channel allocation in next-generation wireless networks using neural networks

Mohammed Aboud Kadhim (), Sadeer Rasheed Ahmed () and Ahmed Rifaat Hamad ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 5, 740-756

Abstract: We explore neural network-based optimization techniques for resource allocation and management in dense wireless networks in the research. The growing demand for efficient communication in contemporary wireless systems makes the isolation of traffic load and channel distribution essential for guaranteeing the best possible development of the network. The new approach is based on artificial neural networks and proactively assigns the available frequency-related channels to users according to their traffic load. The neural network is trained on the data to predict the optimal channel allocation strategy based on a dataset representing users’ traffic regarded as demand. Performances are measured in terms of network efficiency, channel utilization, percentage of collisions, and energy consumption. The findings reveal a marked enhancement in the network's performance parameters, including optimization of bandwidth usage, minimization of collision occurrences, and an overall increase in energy efficiency. This work demonstrates neural networks' capabilities to tackle next-generation wireless network challenges, pointing to a direction of smarter, more effective, and efficient communication systems.

Keywords: Channel allocation; Collision reduction; Dense wireless networks; Energy consumption; Frequency channel management.Intelligent communication systems; Machine learning; Network efficiency; Neural networks; Next-generation wireless systems; Resource allocation; Traffic load optimization. (search for similar items in EconPapers)
Date: 2025
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