5G NR V2X has been designed to support advanced connected and automated driving V2X services. These services are  characterized by variable traffic patterns that can generate packet  collisions in decentralized systems where vehicles autonomously  select their radio resources like 5G NR V2X mode 2. 5G NR V2X  introduces a re-evaluation mechanism at the MAC layer to detect and avoid possible packet collisions before a vehicle transmits in  selected resources. Most of the studies conducted to date on 5G NR  V2X do not consider the re-evaluation mechanism despite being a  mandatory MAC feature. This paper advances the state of the art  with an in-depth analysis and evaluation of the operation and performance of re-evaluation in 5G NR V2X mode 2 under different traffic patterns and mode 2 configurations. The study shows that re-evaluation is effective in avoiding collisions with  periodic traffic but its effectiveness decreases with aperiodic  traffic and of variable size. The study also shows that re-evaluation  is effective in avoiding collisions generated by the retransmission  of packets. However, its overall impact on the performance of 5G  NR V2X mode 2 is small, while it can have a relevant  implementation cost due to the frequent re-evaluation checks and resource reselections. This raises questions on the current design of the re-evaluation mechanism that is a mandatory feature in 5G NR V2X mode 2.

Due to the rapid and widespread growth of the Internet-of-Things (IoT) paradigm, present days witness an exponential increase in the number of connected devices. In this regard, the orthogonal transmission techniques featured by conventional 4G and 5G systems can only support a limited number of simultaneously active users, due to their low spectral efficiency and poorly flexible resource allocation. To overcome such limitations, the 6G framework will include novel Next Generation Multiple Access (NGMA) solutions that will efficiently and flexibly connect a significantly larger number of devices over the same portion of spectrum. Under the NGMA umbrella, the Power-Domain Non-Orthogonal Multiple Access (PD-NOMA) technology is able to accommodate multiple users on the same frequencies by carefully assigning different power levels to the active users and employing Successive Interference Cancellation (SIC) receivers. In this work, we put forth a novel analytical approach to evaluate the performance that PD-NOMA achieves on the uplink of a single cell when a dynamic-ordered SIC receiver is considered. With respect to other existing works, the fundamental limits on the system performance are assessed analytically for an arbitrary number $n$ of simultaneously transmitting users, and both the case of Rayleigh and lognormal-shadowed Rayleigh fading are examined. The closed-form expressions presented in this work, whose correctness and excellent accuracy are validated through Monte Carlo simulations, disclose the impact of lognormal shadowing and an increasingly larger number of active users on the PD-NOMA performance.

This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them. Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.

The Third Generation Partnership Project (3GPP) recently released the 5G New Radio (NR) Vehicle-to-Everything (V2X) standard. NR-V2X is expected to support a large set of enhanced V2X applications, characterized by stringent requirements in terms of latency, reliability and data rate. The use of simulations plays a crucial role in the preliminary assessment of NR-V2X achievable performance, providing valuable insights on the strengths and limits of the new solution. This work introduces MoReV2X, an open-source ns-3 module for the simulation of NR-V2X communications. The development of MoReV2X adheres to 3GPP specifications, includes various traffic models, features an accurate implementation of the NR propagation channel between vehicles and allows to determine a thorough set of Key Performance Indicators (KPIs). A first set of exemplary results is reported to testify the proper functioning of the module. Moreover, the results reveal the impact that different subcarrier spacings have on system performance, when periodic and aperiodic traffic sources are considered.