Vehicle-to-everything (V2X) communications have emerged as an indispensable component of the Intelligent Transportation Systems (ITS), fostering innovations that enhance road safety and traffic efficiency.Within this transformative landscape, vehicle platooning has been identified by the Cellular-V2X (C-V2X) standards as an advanced V2X use case with the potential to reshape the future of ITS. However, the stringent requirements of such applications necessitate an optimized spectrum efficiency. Consequently, Radio Resource Allocation1 (RRA) has emerged as a critical and central component in addressing this challenge. This paper provides a comprehensive examination of RRA tailored specifically to the context of vehicle platooning. It presents an overview of fundamental concepts, including V2X communications, vehicle platooning, and RRA standards, which is followed by an in-depth review of the state-of-the-art, categorized based on the underlying communication technologies, RRA modes, and coverage conditions. By analyzing the existing literature, this paper identifies several key research challenges and potential opportunities, offering insightful guidance for shaping future research in this field.

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs.

As the global data traffic has significantly increased in the recent year, the ultra-dense deployment of cellular networks (UDN) is being proposed as one of the key technologies in the fifth-generation mobile communications system (5G) to provide a much higher density of radio resource. The densification of small base stations could introduce much higher inter-cell interference and lead user to meet the edge of coverage more frequently. As the current handover scheme was originally proposed for macro BS, it could cause serious handover issues in UDN i.e. ping-pong handover, handover failures and frequent handover. In order to address these handover challenges and provide a high quality of service (QoS) to the user in UDN. This paper proposed a novel handover scheme, which integrates both advantages of fuzzy logic and multiple attributes decision algorithms (MADM) to ensure handover process be triggered at the right time and connection be switched to the optimal neighbouring BS. To further enhance the performance of the proposed scheme, this paper also adopts the subtractive clustering technique by using historical data to define the optimal membership functions within the fuzzy system. Performance results show that the proposed handover scheme outperforms traditional approaches and can significantly minimise the number of handovers and the ping-pong handover while maintaining QoS at a relatively high level.

VANET routing aims to interconnect vehicular nodes via wireless links to transmit network packets. The goal of VANET routing is to reduce the communication cost, decrease the latency and increase the interoperability of the network. In this paper, a cluster-based routing protocol for VANETs called CNN is proposed. It takes advantage of the Hamming distance technique to partition a vehicular network into information-centric clusters based on the mobility of vehicular nodes. However, the proposed approach uses a named data network technique to forward network transmissions according to a hybrid communication model of Dedicated Short Range Communication and Mobile Agent. The former focuses on reactive intra-cluster link establishment, while the latter proactively forwards inter-cluster transmissions. A simulation measures the performance of the proposed approach and compares the results with two well-known VANET routing protocols: AODV and A- STAR. According to the simulation results, CNN outperforms the benchmarks in average end-to-end delay, path length, data delivery ratio, and total transmitted traffic, especially when the network is dense and the nodes are highly mobile.

Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means clustering algorithm is an effective algorithm for multi-clusters that can be used in VANETs. The problems with the K-Means algorithm concern the selection of a suitable number of clusters, the creation of a highly reliable cluster, and achieving high similarity within a cluster. To address these problems, a novel method combining a covering rough set and a K-Means clustering algorithm (RK-Means) was proposed in this paper. Firstly, RK-Means creates multi-groups of vehicles using a covering rough set based on effective parameters. Secondly, the K-value-calculating algorithm computes the optimal number of clusters. Finally, the classical K-Means algorithm is applied to create the vehicle clusters for each covering rough set group. The datasets used in this work were imported from Simulation of Urban Mobility (SUMO), representing two highway scenarios, high-density and low-density. Four evaluation indexes, namely, the root mean square error (RMSE), silhouette coefficient (SC), Davies–Bouldin (DB) index, and Dunn index (DI), were used directly to test and evaluate the results of the clustering. The evaluation process was implemented on RK-Means, K-Means++, and OK-Means models. The result of the compression showed that RK-Means had high cluster similarity, greater reliability, and error reductions of 32.5% and 24.2% compared with OK-Means and K-Means++, respectively.