Res6Edge: An Edge-AI Enabled Resource Sharing Scheme for C-V2X Communications towards 6G

The paper proposes a sixth-generation (6G)-enabled cellular vehicle-to-anything (C-V2X)-based scheme, Res6Edge, that supports high-data ingestion rate through artificial intelligence (AI) models at edge nodes, or Edge-AI. Through Edge-AI in 6G supported C-V2X, we address the research gaps of earlier schemes based on fifth-generation (5G) resource orchestration. 6G improves decision analytics and real-time resource sharing among C-V2X ecosystems. The scheme operates in three phases. In the first phase, a layered network model is proposed for V2X communication based on 6G-aggregator and core units. Then, based on the proposed stack, in the second phase, 6G resource allocation is proposed through macro base station (MBS) units. MBS ensures channel gain and reduces energy loss dissipation. Finally, in the third phase, an intelligent edge-AI scheme is formulated based on deep-reinforcement learning (DRL) to support responsive edge-cache and improved learning. The proposed scheme is compared to 5G baseline services in terms of parameters like- throughput, latency, and DRL scheme is compared to random allocation approaches. Through simulations, Res6Edge obtains a V2X user throughput of 43.24 Mbps, compared to 0.7 Mbps for 4 x 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> connected ACV sensors. The reduced latency is ≈ 13.84 times of 5G. DRL learning algorithm achieves a satisfaction probability of 0.5 for 500 vehicles, compared to 0.35 using conventional schemes. The obtained results indicate the viability of the proposed scheme.