The Third Generation Partnership Project (3GPP) introduced Cellular Vehicle-to-Everything (C-V2X) for vehicular communications. In the standard, C-V2X Mode 4 is defined for the distributed resource selection. Subsequently, in 3GPP Release 16, NR-V2X is introduced with Mode 1 and Mode 2 for vehicular communications. Likewise C-V2X Mode 4, NR-V2X Mode 2 is used for decentralized resource scheduling. The vehicles select the resources based on their local observations by utilizing the Semi-persistent Scheduling (SPS). Since, the vehicles select the resources based on the local observation, sensing nature of SPS is challenged by the hidden node problem that lead to resource conflict. To resolve the contention, 3GPP also introduced the Physical Sidelink Feedback Channel (PSFCH) to assist the distributive resource scheduling based on the receiver feedback. However, this incurred a signaling overhead. In this work, federated learning is exploited for distributive training via offline method and distributive multi-agent-based resource scheduling is performed following the principles of NR-V2X Mode 2. Distributed training favors the model accuracy by accommodating the varying affect of the environment due to the high mobile dynamics. Simulation is conducted by integrating SUMO in conjunction with 3GPP NR-V2X standard. Performance results demonstrate a substantial improvement compared to other deep learning methods, where centralized training and random resource selection procedures are employed. This research marks a significant stride towards efficient and conflict-resilient resource allocation in vehicular communications.

Cellular vehicle-to-everything (C-V2X) is the first standard introduced by the 3rd generation partnership project (3GPP) in its Rel. 14 to enable V2X communication. This standard is endorsed by the industries, scientists, and researchers. In C-V2X, mode 4 is designed to enable direct V2V communication via the PC-5 interface. In mode 4 vehicles select the resources which are over two-dimensional frequency and time domain for their packet transmission. These resources are scheduled based on a semi-persistent scheduling algorithm introduced by 3GPP to avoid resource contention. However, when the density of vehicles is high resolving resource contention is a major concern. To this end, 3GPP introduced NR-V2X which is the evolution of C-V2X in its Release 16. NR-V2X mode 2 is introduced as an alternative to C-V2X mode 4 for distributed resource scheduling. NR-numerologies and semi-persistent scheduling with re-evaluation mechanism is introduced by 3GPP in NR-V2X play a vital role in resolving resource contention. This paper presents first the analytical models for NR-V2X mode 2 considering the impact of NR-numerologies and re-evaluation mechanism on resource selection. The analytical models are designed considering the four transmission errors that could occur in NR-V2X mode 2. Moreover, the model validation is performed by comparing the proposed analytical models with the NR-V2X simulator implemented in ns-3. The model validation shows that the proposed analytical models are accurate.

Content retrieval in content-centric vehicular networks faces challenges that include high latency, especially when content is stored far from the requesting vehicle. On-path caching feature in the conventional vehicular named data Networks (VNDN) enables content storage that can reduce latency. However, due to the constantly changing dynamic ad hoc nature of the vehicular network, the availability of stored content for the requester vehicle cannot be guaranteed. In addition, without knowing which content will be requested, where it will be requested and when it will be requested, the content caching functionality of VNDN is underutilized. To address this issue, this manuscript proposes a content prefetching scheme for the Content-centric Internet of Vehicles (CIoV) by introducing the content Value of Popularity ( $VoP$ ) matrix. Considering vehicles requesting content of similar interests, we evaluate $VoP$ through three value update functions that follow the power law of the time elapsed since the last content requested. By multiple parameters of consumer vehicle similarity, an on-road proactive content retriever vehicle is selected. The simulation results showed that the proposed proactive on-path content prefetching mechanism significantly reduces the content delivery delay while increasing the success delivery ratio by 48% and extends the spread of content within the network by 53%.