Vehicle-to-Everything communications (V2X) are becoming increasingly popular as a solution for safer roads and better traffic management. One of the essential protocols in V2X is the Dedicated Short Range Communication (DSRC) protocol suite. DSRC includes the IEEE 802.11p protocol that operates at the medium access control (MAC) and physical (PHY) layers. Upon collision, the IEEE 802.11p MAC layer applies a carrier sense multiple access/collision avoidance (CSMA/CA) mechanism that randomly selects a backoff time to re-check the channel activity and then retransmit. However, the random selection of the backoff time may lead to further packet collisions that decrease the utilization of the communication channel, which suffers from a limited bandwidth in the first place. This paper proposes a fuzzy model based on rational decision-making, which we call F-802.11p, to improve the IEEE 802.11p protocol backoff time selection by limiting the IEEE 802.11p beacon messages to better use of the available bandwidth. A simulation study presents the evaluation of our work compared to IEEE 802.11p. We deployed the simulation software in two scenarios: the Veins Framework map and the map of New Administrative Cairo in Egypt. We base our comparison on slots backoff, times into backoff, PHY busy time, MAC busy time, total lost packets, and generated/received beacon messages. Simulation results show that both protocols have comparable results in slots backoff, times into back off, and the generated beacon messages. At the same time, our F-802.11p significantly outperforms the IEEE 802.11p in PHY busy time, MAC busy time, total lost packets, and the received beacon messages in both scenarios.

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.