The proposed framework achieves 20-30% reduced latency, a 15-35% reduction in energy consumption, and an 18-28% throughput enhancement compared to existing methods, and ensures a wide improvement in reliability and adaptability in 5G V2X communication networks.
Abstract
The 5G- enabled Vehicle -to-Everything (V2X) is a reliable communication technology that allows vehicles to communicate with other vehicles, networks, and infrastructure to enhance road safety and traffic proficiency. The challenges in 5G-V2X networks are high mobility, dynamic network topology, and strict quality-of-service (QoS) requirements. Especially in latency-sensitive applications such as collision avoidance and real-time traffic management, are degraded by frequent link failures, excessive routing overhead, and inefficient resource utilization. Recently, the machine learning-based routing algorithms integrated with fuzzy logic and metaheuristic optimization have achieved multi-objective performance with limited adaptability and slow convergence. To overcome these issues, a novel Multi-Objective Harris Hawks Optimization (MO-HHO) integrated with a Bayesian optimized Mobility-Aware Transformer Network (BMAT) is proposed to design an enhanced intelligent framework for 5G V2X Communication.MO-HHO optimizes cluster formation and routing paths with reduced latency and energy usage, and it re-clusters by improving throughput and stability using mobility-aware updates. The Bayesian optimized self-attention-based MAT transformer is used for typical long-range spatiotemporal dependencies for finding optimal cluster heads, routing stability, and probability of congestion with the Tree-structured Parzen Estimator (TPE) to produce better convergence. The proposed framework is assessed using realistic 5G V2X mobility scenarios under urban and highway conditions, and the obtained results achieve 20-30% reduced latency, a 15-35% reduction in energy consumption, and an 18-28% throughput enhancement compared to existing methods. Finally, the proposed framework ensures a wide improvement in reliability and adaptability in 5G V2X communication networks.
Vehicular Ad-Hoc Networks are an emerging paradigm within Intelligent Transportation Systems (ITS), enabling communication between vehicles (V2V) and/or vehicles and infrastructure (V2I). These networks aim to enhance road safety and improve the driving experience. However, due to the high mobility of vehicles and frequent changes in their geographical positions, ensuring reliable data delivery remains a significant challenge. Clustering has emerged as a promising technique to improve scalability, reduce overhead, and enhance routing stability in VANETs. This paper first introduces a new classification framework for clustering-based routing protocols according to their operational and decision parameters. It then proposes the Q-learning Weighted ClusterBased Routing Protocol (Q-WeCBR), which combines clustering with reinforcement learning. Q-WeCBR improves cluster head (CH) selection through a weighted selection function and a maintenance phase that ensures cluster stability. In addition, the integration of Q-learning enables the protocol to adapt intelligently to topology changes by selecting the most reliable routes.Simulation results obtained with OMNeT++ and SUMO demonstrate that Q-WeCBR outperforms CBR, DSDV, and GPSR in terms of packet delivery ratio and throughput, confirming the effectiveness of clustering combined with learning-based routing for dynamic vehicular networks.
Ahlam Boussadia· International journal of inf...· 0 citations
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.
William Hughes, Marta Silva· International Journal of Dat...· 0 citations
The rapid advancement of wireless communication has led to the emergence of the fifth-generation (5G) network, which aims to provide ultra-reliable low-latency communication (URLLC) while ensuring high data transmission rates. Optimizing data transmission in 5G networks is critical for supporting real-time applications such as autonomous vehicles, telemedicine, industrial automation, and smart cities. This paper explores various techniques and strategies to enhance data transmission efficiency, minimize latency, and improve reliability in 5G networks. We analyze the key performance indicators (KPIs) that influence data transmission, including bandwidth utilization, network slicing, and multiple access techniques. Furthermore, we discuss the role of edge computing, artificial intelligence (AI)-driven network management, and adaptive modulation techniques in optimizing data transmission. The paper also highlights the impact of interference management, energy efficiency considerations, and security protocols on 5G network performance. We conduct a comprehensive literature survey to examine existing optimization techniques and propose an improved methodology leveraging AI-driven resource allocation and dynamic spectrum sharing. Through simulation and analytical results, we demonstrate the effectiveness of the proposed approach in reducing end-to-end latency and improving network reliability. The findings contribute to the ongoing efforts in optimizing 5G networks and lay the foundation for future research in beyond-5G (B5G) and sixth-generation (6G) communication systems.
Kenji Sato· International Journal of Mod...· 0 citations
Findings validate the efficacy of incorporating swarm intelligence into the 5G architectures as a viable and self-optimizing solution for the promotion of connectivity and signal power performance in the next-generation high-density wireless networks.
H. Lasisi, H. B. Omodeni, B. Aderinkola et al.· 0 citations
Vehicle-to-Everything (V2X) communication is an essential building block of intelligent transportation systems, supporting high-data-rate vehicle-to-infrastructure (V2I) services, and ultra-reliable low-latency vehicle-to-vehicle (V2V) communication. However, in dense urban environments, V2X services can be significantly degraded by the presence of severe blockage, fast channel variations, and high levels of interference. Intelligent Reflecting Surfaces (IRSs) can be employed to reconfigure wireless propagation environments to improve V2X communication. However, the joint optimization of transmit power, spectrum reuse, and IRS reflection coefficients is a mixed-integer non-linear problem, which is further complicated by the fast vehicular mobility and time-varying interference in V2X networks. To tackle this challenging problem, this work proposes a scalable and deployable decentralized hierarchical multi-agent deep reinforcement learning (DH-MDRL) framework. The key design principle is the separation of control timescales, whereby each V2V link functions as an autonomous agent that responds to local observations at a fast timescale and determines its transmit power and spectrum reuse decisions, while the IRS controller at the base station (BS), using global network observations, updates the IRS reflection coefficients at a slower timescale. This hierarchical architecture reduces coordination signaling associated with centralized resource allocation while enabling distributed resource allocation. The IRS-assisted V2X network is modeled as a Markov decision process, where the reward design is tailored to optimize the V2I sum data rate while guaranteeing the latency and reliability constraints associated with safety-critical V2V communication. Simulation results show that the proposed DH-MDRL framework outperforms conventional schemes without IRSs and achieves an excellent trade-off between V2V link constraints’ satisfaction probability and V2I link sum data rates compared to centralized resource allocation approaches.