Adaptive Net-Profit-Based Scheduling with Minimizing Mutual Exclusion vRB Allocation in 5G-A NR Networking

Wei-Teng Chang¹, Ben-Jye Chang^2,*
1 Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan
2 Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan
* Corresponding Author: Ben-Jye Chang. Email: email , email
(This article belongs to the Special Issue: Computer Modeling for Future Communications and Networks)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.077118

Received 02 December 2025; Accepted 25 February 2026; Published online 30 March 2026

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Abstract

Some critical applications of emergency, Active Safe Driving (ASD), eV2X, and LEO communications require ultra-low delay and highly reliable transmission according to beyond 5G-Advanced (5G-A), 6G, and LEO specifications. Related studies proposed various scheduling algorithms in terms of single and multiple QoS requirements. However, these approaches tend to prioritize traditional QoS requirements while neglecting crucial considerations such as bearer costs and associated benefits. Moreover, most scheduling neglects the carrying cost according to the radio resource state and the bringing reward from different types of flows. Thus, this paper proposes a novel cost-based flow scheduling (eSCFS) framework that utilizes an extended sigmoid function to dynamically prioritize flows, taking into account all relevant key factors. The principal objective is to reduce latency while optimizing the utilization of radio RB and maximizing the net benefits of 5G-A NR networks. The eSCFS method has been validated through numerical simulations, which demonstrated superior key performance metrics, including network latency, resource utilization, and overall profitability. Consequently, several objectives are thus achieved: 1) analyzing the QoS requirements of various services within limited radio resources, 2) proposing a novel vRB state-dependent dynamic flow scheduling and adaptive virtual radio RB management to maximize network performance.