Performance of Fuzzy Inference System for Adaptive Resource Allocation in C-V2X Networks

Abstract

Mode 4 of 3GPP Cellular Vehicle-to-Everything (C-V2X) uses a new Sensing-Based Semi-Persistent Scheduling (SB-SPS) algorithm to manage its radio resources. SB-SPS applies a probabilistic approach to provide the resource allocation in the system. The resource keep probability (Prk) variable plays an essential role in the resource allocation mechanism. Most of the previous works used a fixed Prk value. However, the Packet Delivery Ratio (PDR) can be improved by adapting the optimal Prk value. Hence, we propose a Fuzzy Inference System (FIS) with two inputs, distance, and Channel State Information (CSI) to determine the suitable Prk. The simulation results show that the proposed FIS method outperforms the other methods for sparse and congested road scenarios, with total numbers of vehicles at 200 and 400, respectively.

Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles

“…Ref. [22] utilized the fuzzy interference system theory and designed the corresponding matching statement table to dynamically determine the resource keep probability in mode 4 to improve the packet delivery ratio performance. In [23][24][25], deep reinforcement learning was introduced into the resource allocation in mode 3 to improve the latency performance, and the corresponding action space, the state space, and the reward function were designed for different communication scenarios.…”