Implementation Insights of Robust Dynamic Spectrum Sharing for Heterogeneous Services in Non-Standalone 5G

Abstract

Dynamic spectrum sharing (DSS) is a highly efficient approach for deploying new radio (NR) on lower frequency bands currently utilized by long-term evolution (LTE), to enhance the coverage. This paper addresses several critical issues in DSS. Firstly, we investigate resource block (RB) allocation in a multi-cell environment, considering inter-cell interference (ICI) and frequency reuse (FR) to optimize the combined LTE and NR data rates in a DSS-enabled network. We then apply maximum throughput (MT) and enhanced proportional fairness (ePF) schedulers for RB allocation within our proposed simulation framework. Additionally, we explore the impact of satisfying users’ quality of service (QoS) on data rate and fairness across various sharing ratio values for LTE and NR guaranteed bit rate (GBR) users. Our results show that while MT achieves higher data rates, ePF ensures better fairness and QoS among users, albeit with a potential data rate reduction of 25-30%. Moreover, under high data rate GBR scenarios, the network can maintain an appropriate fairness index (FI) based on the sharing ratio while guaranteeing GBR users. The ePF scheduler tends to drop more users compared to MT, yet a balance among LTE/NR spectrum sharing ratio, fairness, GBR satisfaction, and overall data rate maximization can be achieved in DSS networks. We also evaluate DSS performance across various realistic propagation models, identifying an optimal sharing ratio that maximizes total data rates for LTE and NR in each environment, with the MT scheduler delivering the highest data rates in rural macro areas. Lastly, we address the issue of demand uncertainty to develop a robust DSS network. Our findings indicate that robust DSS outperforms unrobust DSS by 7-25% and unrobust static spectrum sharing (SSS) by 19-40%.