Simulation Model for Radio Resource Scheduling Algorithms at MAC Layer of Mobile Networks

Effective radio resource scheduling at the Medium Access Control (MAC) layer is a critically important task for ensuring quality of service in mobile networks. The use of machine learning and artificial intelligence for MAC-layer scheduling is becoming a promising direction. Existing general-purpose simulators (MATLAB, ns-3, OMNeT++) are insufficiently optimized for in-depth research ofl resource scheduling algorithms and have limitations in their integration.

The purpose of this article is to develop a specialized simulation model for LTE (Long Term Evolution) network resource scheduling at the MAC layer for investigating both classical and intelligent scheduling algorithms.

The core of the proposed solution lies in creating a modular simulation model that incorporates different user mobility models, radio propagation models, traffic generation models, and classical scheduling algorithms (Round Robin, Proportional Fair, Best CQI). The model specializes in detailed simulation of MAC-layer processes. The system is implemented in Python with modular architecture enabling integration of machine learning and artificial intelligence-based algorithms. The source code is hosted in an open GitHub repository.

Experiments were conducted for an infinite buffer simulation scenario with three users from different mobility classes in an urban environment. Three classical scheduling algorithms were tested with evaluation of throughput, Jain's fairness index, and spectral efficiency.

The scientific novelty of the solution lies in creating a specialized simulation model optimized for investigating MAC-layer scheduling algorithms with the capability to integrate machine learning methods and providing flexibility in configuring various simulation scenarios.

The theoretical significance consists in expanding the toolkit for studying mobile network resource scheduling algorithms and establishing a foundation for developing intelligent schedulers.

The practical significance is providing researchers with a specialized tool for developing, testing, and comparing scheduling algorithms, as well as the ability to adapt the model for 5G/6G networks and integrate quality-of-service-aware schedulers.

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