Connected vehicles offer substantial potential for improving traffic safety and enhancing comfort services. However, maintaining consistent connections in dynamic vehicular environments remains a persistent challenge, especially due to the need for seamless handovers (HO) between cellular towers as vehicles travel at high speeds. The limited communication range often leads to frequent HOs and connection drops, which can degrade the reliability of services and resources. The virtual cell (VC) paradigm can help mitigate the challenges of the limited communication range in 5G networks for high-mobility, ultra-dense scenarios. To address these challenges, we propose a mobility-oriented approach using a multi-output regression model named MSVR to manage VCs. Our proposed approach ensures stable HO decision-making by dynamically managing VCs based on predictive mobility, considering network attributes and real-time data: speed, signal strength, and network quality. Realistic simulations and extensive result analyses have been conducted to demonstrate the effectiveness of the proposed MSVR approach over existing works in terms of throughput, frame loss ratio, number of HO, and size of VC.

Technological advancements in cellular networks have enabled to surpass many challenges in telecommunications, but some features remain restricted, such as throughput, packet loss, and latency. User equipment (UE), including mobile, smart devices, vehicles, IoT devices, and smart city infrastructure, requires seamless connectivity to share data and resources effectively. Multiple radio access technology (multi-RAT) scenarios offer a solution to the limitations of individual RATs by combining their strengths. Determining the optimal RAT for traffic steering (TS) in multi-RAT scenarios is challenging due to factors such as high mobility, ultra-dense networks, overall dynamic network conditions, and the unique needs of individual users. In this context, we propose a bi-level approach, called BIL-TS, which includes (i) centrally determining the optimality of RATs and (ii) locally making TS decisions. BIL-TS utilizes the Actor-Critic SARSA Reinforcement Learning (ACS-RL) in level (i) to evaluate the optimality of RATs by considering the entire network, and level (ii) leverages Linear Regression (LR) to make decisions of TS to the optimal RAT based on specific requirements of each UE. Simulation results show that our proposed BILTS approach significantly enhances efficiency in TS, resulting in higher throughput, reduced packet loss, and lower latency.

5G technology offers high bandwidth, stability, and reliability among connected vehicles, which is necessary for increasing data sharing in intelligent transportation. While providing these benefits with its small cellular range and densification, it also presents a challenge in frequent handovers (HOs). This issue can result in unnecessary HO, HO failures, and ping-pong effects, negatively impacting service delivery and compromising safety data sharing. A learning-oriented proactive HO decision-making strategy can ensure connection stability by making HO decisions based on real-time scenarios. This paper presents a high mobility and ultra-dense network-aware proactive HO decision-making (PAHD) approach, efficiently ensuring stable connectivity by predicting future HO. PAHD consists of two parts (i) Gaussian Process Regression for mobility prediction and (ii) Bidirectional Long Short-Term Memory for the prediction of network traffic density. Realistic simulated analyses have shown that PAHD significantly improves efficiency in HO decision-making.

Ensuring connection stability is crucial for both vehicular safety and user experience. With the increasing amount of data sharing among connected vehicles, there is a need for more bandwidth, stability, and reliability. While 5G technology can offer these benefits with its small cellular range and densification, it also presents a challenge in frequent handovers (HOs). This issue can result in unnecessary HO, HO failures, and ping-pong effects, negatively impacting service delivery and compromising safety data sharing. To this end, we present High- mobility and Ultra-density Aware Handover decision-making (HMUD-H) approach using the SARSA Reinforcement Learning algorithm for connection management, which efficiently makes HO decisions to ensure stable connectivity. The HMUD-H algorithm is adaptable and can handle dynamic, highly mobile, and ultra-dense vehicular networks. Realistic simulated analyses have demonstrated that our algorithm significantly reduces the number of HOs, average cumulative HO time, HO failures, and ping-pong effects, thus improving overall connection stability.

5G and vehicular networks have enabled Intelligent Transportation Systems (ITS) with better safety and infotainment services where connected vehicles are critical components for data sharing. However, a stable connection is mandatory to transmit data successfully across the network. The 5G technology enhances bandwidth, stability, and reliability but suffers from low communication ranges, which results in frequent and unnecessary handovers and connection drops. In this paper, we introduce a user-centric approach, Factor-distinct SARSA Reinforcement Learning (FD-SRL), which combines a time series data-oriented model LSTM and adaptive method SARSA Reinforcement Learning for Virtual Cell (VC) and handover (HO) management. Our proposed approach maintains stable connections by reducing the number of HOs, given the fast-paced changes due to mobility, network load, and communication conditions. Realistic simulations demonstrated that FD-SRL reduced the number of HOs and the average cumulative HO time, showing potential improvements in connection stability for 5G-based ITS.

Connected vehicles enable massive data sharing and support intelligent transportation services. Consequently, a stable connection is compulsory to transmit across the network successfully, where 5G technology introduces more bandwidth, stability, and reliability. However, 5G communication is susceptible to frequent handovers and connection drops. A user-centric perspective helps cope with the smaller communication range in ultra-dense 5G networks. We thus introduce a Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) algorithm for user-centric to efficiently handle virtual cell (VC) management and reduce the number of handovers (HO). The adaptability of the algorithm copes with high vehicular mobility and dynamic traffic and communication, deciding on in-rage cellular towers and VC size. Realistic simulated analyses showed CO-SRL reduced the number of handovers and the cumulative handover time.