Dynamic Spectrum Coexistence of NR-V2X and Wi-Fi 6E Using Deep Reinforcement Learning

Abstract

The deployment of 5G NR-based Cellular-V2X, i.e., the NR-V2X standard, is a promising solution to meet the increasing demand for vehicular data transmission in the low-frequency spectrum. The high throughput requirement of NR-V2X users can be overcome by extending it to utilize the sub-6GHz unlicensed spectrum, coexisting with Wi-Fi 6E, thus increasing the overall spectrum availability. Most existing works on coexistence rely on rule-based approaches or classical machine learning algorithms. These approaches may fall short in real-time environments where adaptive decision-making is required. In this context, we introduce a novel Deep Reinforcement learning (DRL) based framework for 5G NR-V2X (mode-1 and mode-2) and Wi-Fi 6E coexistence. We propose an algorithm to dynamically adjust the transmission time of the 5G NR-V2X (for mode-1) or Wi-Fi 6E (for mode-2), based on the Wi-Fi and V2X traffic, to maximize the overall throughput of both systems. The proposed algorithm is implemented through extensive simulations using the Network Simulator-3 (ns-3), integrated with a custom Deep Reinforcement Learning (DRL) framework developed using OpenAIGym. This closed-loop integration enables realistic, dynamic interaction between the learning agent and high-fidelity network environments, representing a novel simulation setup for studying NR-V2X and Wi-Fi coexistence. The results show that when employing DRL on NR-V2X and Wi-Fi coexistence, the average data rates for Vehicular User Equipments (VUEs) and Wi-Fi User Equipments (WUEs) improve by $\sim$24% and 23%, respectively, as compared to the static method; and even higher improvement when compared to the existing RL-based LTE-V2X and Wi-Fi coexistence approach. Additionally, we analyzed the impact of NR-V2X coexistence on the Wi-Fi subsystem under mode-1 and mode-2 communications. Our findings indicate that mode-1 communication demands more spectrum resources than mode-2, leading to a performance compromise for Wi-Fi.