Dynamic extended sigmoid-based scheduling with virtualized RB allocation for maximizing frequency numerology efficiency in 5G/B5G NR networks

Abstract

New trends of extremely ultra-low delay, ultra-high reliability, long-distance high-moving, etc., are strongly required for achieving Emergency resecure, Active Safe Driving (ASD), IoV communications, LEO communications, etc. Thus, 3GPP 5 G/B5G specifies flexible numerology technology providing different frequency SCS modes with different slot-times for diverse types of flows requiring different 5QIs in terms of delay, data rate, jitter, loss probability, and reliability. Although based on the frequency numerology, 3GPP Release 17 specifies three types of RB allocations, i.e., Types 0, 1, and dynamic switching allocations for UL access, such static and consecutive RB allocations result in inefficient numerology and frequency spectrum efficiency. As a result, typical critical issues exhibit in B5G NR, including high mutual exclusion (MX) of numerology, RB state independency flow scheduling, and static RB allocations suffering from dynamic flowing traffic. Thus, this paper proposes the Extended Sigmoid-based Scheduling with a Virtualized RB Allocation (SSA) consisting of two phases. Phase 1 proposes an Extended Sigmoid-based Cost-based Flow Scheduling (SCFS) algorithm to dynamically differentiate flow priorities. Phase 2 proposes a dynamic Virtualized RB pre-Allocation (VRBA) mechanism to maximize radio RB utilization and minimize mutual exclusion in 5 G frequency numerology. Numerical results indicate that the proposed SSA outperforms the compared approaches in network delay, jitter, resource utilization, network reward, and net-profit. Consequently, this paper achieves several objectives and contributions: 1) analyzing the QoS requirements of different applications with limited frequency spectrum, 2) proposing vRB state-related dynamic flow scheduling algorithm to maximize network efficiency, 3) minimizing numerology MX for vRB allocation, and 4) proposing adaptive cost-reward-based scheduling.