QoS-Driven Resource Allocation and Performance Optimization for Aggregated VLC–RF Vehicular Networks

Resource Allocation and Performance Optimization for IRS-Assisted Aggregated VLC–RF Vehicular Networks

With advances in emerging material technologies, intelligent reflecting surface (IRS)-assisted vehicular networks have been gaining growing interest. By adaptively shaping the wireless propagation environment, IRSs can improve vehicular network quality of service (QoS). However, most IRS-assisted vehicular network studies are limited to individual RF or VLC frameworks, while only a few investigate IRS-assisted aggregated VLC-RF vehicular networks that combine wide RF coverage with high VLC data rates. In this paper, aggregated VLC-RF vehicular networks are supported by both optical IRSs (OIRSs) and RF IRSs, and a resource allocation scheme is developed to improve the total achievable rate. First, we establish a system model for IRS-assisted aggregated VLC-RF vehicular networks, and then formulate a problem to maximize the total achievable rate. Furthermore, we decompose the maximization of the total achievable rate into five subproblems and solve them iteratively via an efficient alternating optimization scheme based on block coordinate descent (BCD). Moreover, simulation results validate the convergence and efficiency of our algorithm, while highlighting the effects of crucial parameters on system performance, providing valuable insights for resource allocation in IRS-assisted aggregated VLC–RF vehicular networks.

SDN-Assisted Low-Latency Resource Management for V2X Communication in Heterogeneous Cellular Networks

Vehicle-to-Everything (V2X) communication requires low-latency, reliable, and context-aware connectivity across vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian, and vehicle-to-network services. Heterogeneous cellular networks can support these services by combining 5G New Radio, cellular sidelink, roadside units, Wi-Fi or DSRC links, and multi-access edge computing; however, high mobility, variable traffic density, frequent handovers, congestion, and static resource-allocation policies can degrade end-to-end delay and packet delivery. This paper presents a software-defined networking assisted framework for latency-aware resource management in heterogeneous V2X environments. The proposed framework separates network control from forwarding, maintains a global view of vehicular network state, classifies V2X flows by service criticality, and dynamically selects routes and bandwidth allocations using delay, congestion, handover, and priority constraints. A mathematical formulation is developed for minimizing weighted end-to-end latency under link-capacity, minimum-bandwidth, packet-delivery, congestion, and controller-processing constraints. The paper also defines the Latency-Aware SDN Resource Allocation Algorithm for V2X Networks and specifies a reproducible simulation methodology using NS-3/SUMO or OMNeT++/Veins with multiple baselines, confidence intervals, and ablation analysis. Because the source report did not include raw simulation data, the revised results section includes clearly labelled hypothetical numerical plots and reporting values only to demonstrate result formatting and interpretation; these values are not validated empirical findings. The study provides a rigorous foundation for validating SDN-MEC orchestration in next-generation vehicular communication systems

Resource allocation scheme for VLC-RF networks under QoE constraints

Resource optimization algorithm for RF-aided NOMA VLC network joint blocklength control and power allocation

Simulation results demonstrate that the proposed resource optimization scheme for NOMA VLC/RF networks outperforms NOMA VLC and OMA VLC and OMA VLC in ensuring highly reliable data transmission.

A revenue-driven approach for enhanced task utilization in vehicular cloud computing

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher average data delivery. Specifically, versus RBSM, gains reach 77.6% in revenue, 228.0% in TCR, and 242.4% in TDD; against VVMM-U: 32.6%, 43.9%, and 47.9%; versus VVMM-LW: 153.7%, 74.5%, and 284.1%; and versus VVMM-MA: 25.7%, 30.2%, and 53.7%, respectively. These improvements stem from RDA-ITU’s core mechanisms: revenue-optimized candidate sorting, dynamic V2V relay pairing, and adaptive bandwidth allocation. Prioritizing high-revenue services and facilitating efficient cooperative offloading, RDA-ITU ensures strong performance in dense mobile environments, thus promoting revenue-aware vehicular edge computing.

Energy-Efficient Joint Optimization of VLC and Fog Computing Resources Allocation

A mixed-integer linear programming (MILP) model is developed that jointly optimizes access points (APs) selection, wavelength assignment and fog computing resources allocation to serve the requests of users and achieves lower average total power consumption.