Aug 2026· Electronics· Vol 15, pp. 3444· 0 citations· 33 references
TL;DR
A heterogeneous communication network cooperation framework that integrates decentralized multi-hop vehicle-to-vehicle (V2V) relaying with conventional V2I communication to extend signal phase and timing (SPaT) dissemination beyond direct RSU coverage and maintains advisory continuity through distributed relay dissemination is proposed.
Abstract
Urban intelligent transportation systems increasingly rely on vehicle-to-infrastructure (V2I) communication for green-light optimal speed advisory (GLOSA) services. However, conventional GLOSA systems are vulnerable to roadside unit (RSU) coverage gaps and communication instability in mixed-traffic environments. In this paper, we propose a heterogeneous communication network cooperation framework that integrates decentralized multi-hop vehicle-to-vehicle (V2V) relaying with conventional V2I communication to extend signal phase and timing (SPaT) dissemination beyond direct RSU coverage. A lightweight gradient-based speed synchronization mechanism supports real-time trajectory adaptation with low computational overhead. The framework is evaluated through microscopic SUMO simulations with explicit communication impairment modeling across varied traffic densities and connected autonomous vehicle (CAV) penetration levels (10–70%). The results demonstrate reductions in travel time reductions of up to 22%, stop frequency of up to 95%, and CO2 emission exceeding 18% relative to V2I-only GLOSA under 70% CAV penetration. At the lower bound of 10% CAV penetration, the framework still achieves measurable improvements of approximately 4–6% in travel time and 15–20% in stop frequency, confirming practical benefit even under minimal connected-vehicle adoption. The proposed framework maintains advisory continuity through distributed relay dissemination, offering a scalable and communication-resilient enhancement to intelligent transportation coordination in heterogeneous environments.
—Efficient traffic signal control is essential for reducing congestion, emissions and travel delays in modern urban environments. Traditional Vehicle-to-Infrastructure (V2I) systems are limited by infrastructure coverage, while Vehicle-to-Vehicle (V2V) communication alone lacks global signal-state awareness. This study proposes a hybrid V2V-V2I communication model that enhances Green Light Optimal Speed Advisory (GLOSA) performance by allowing vehicles to relay Signal Phase and Timing (SPaT) information in low-infrastructure or obstructed environments. A mathematical formulation describing vehicle motion, inter-vehicle message propagation and signal-state transitions is developed, complemented by a detailed algorithmic description of the hybrid control logic. The model is implemented using SUMO with the Krauss microscopic car-following model to simulate real-world driving behavior. Results demonstrate that the hybrid model reduces stop frequency, travel time, fuel consumption, and CO₂ emissions compared to standalone V2I and V2V schemes. The added V2V relaying mechanism improves prediction accuracy, particularly in scenarios with limited roadside units. The findings highlight the feasibility, scalability and ecological benefits of integrating multi-source communication in intelligent traffic systems, offering a promising solution for future connected urban mobility.
Abdullah Alsaleh· Journal of Advances in Infor...· 0 citations
Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) density, frequent non-line-of-sight conditions, and highly dynamic vehicular topologies often prevent many Connected and Automated Vehicles (CAVs) from maintaining stable single-hop connectivity. Although multi-hop relay-assisted communication can extend infrastructure coverage, selecting relay links in real time under practical flow, capacity, and connectivity constraints remains challenging. Mixed-Integer Linear Programming (MILP) yields optimal multi-hop relay decisions, but its computational complexity scales sharply with network density, limiting real-time applicability. To address this, we propose a Learning-to-Optimise (L2O) framework based on Graph Neural Networks (GNNs) for real-time NR-V2X relay selection. Vehicular communication states are modeled as attributed graphs, where CAVs and RSUs are nodes and candidate radio links are enriched with propagation-aware features. An offline MILP oracle provides optimal supervision, while an edge-aware Graph Isomorphism Network (GINE) approximates oracle decisions with near-constant inference latency. Experiments on large-scale urban datasets generated by an integrated SUMO-GEMV2 simulation pipeline show that the proposed approach achieves connectivity comparable to that of the MILP oracle while reducing execution time by orders of magnitude. The framework enables cost-effective enhancement of urban V2X connectivity by leveraging existing vehicular assets and supporting scalable, real-time NR-V2X operation in smart city environments.
G. Amati, F. Mangiatordi, Simone Angelini et al.· International Conference on...· 0 citations
Simulation results demonstrate that the proposed adaptive scheme demonstrates notable improvements over classical loss-based and delay-based baselines in reducing queuing delays at UAV relay nodes, enhances the transmission efficiency of multi-hop terminals, and effectively maintains end-to-end goodput stability in high-latency environments.
L. Zong, Yun Cheng, Yi Yao· Italian National Conference...· 0 citations
This paper investigates the use of unmanned aerial vehicles (UAVs) as flying base stations (BSs) to enhance fifth generation (5G) vehicular communications on highways, where traffic congestion and fluctuating user demand can challenge the capacity of terrestrial infrastructure. While UAV-assisted vehicular networking has attracted significant attention, many existing studies rely on simplified mobility, propagation, or communication models that limit the assessment of practical deployment performance. To address these limitations, we develop a realistic UAV-assisted vehicular networking framework that integrates microscopic traffic simulation through Simulation of Urban MObility (SUMO), network control via Traffic Control Interface (TraCI), and standard-compliant 5G communication modeling using MATLAB R2025b 5G Toolbox. The framework incorporates a 3rd Generation Partnership Project (3GPP) rural macro cell (RMa) highway scenario, detailed clustered delay line (CDL)-based channel characterization, and cross-layer communication procedures. Within this framework, we propose a low-complexity trajectory optimization strategy that adapts the UAV position in real time to maximize the average received signal to noise ratio (SNR) while respecting practical motion constraints. Simulation results demonstrate that adaptive UAV positioning enhances communication performance, achieving mean SNR gains of up to 2.04 dB, throughput improvement of up to 11.2%, and block error rate (BLER) reductions of up to 27.3%. These findings highlight the potential of UAV-assisted communications to enhance user-perceived quality of service (QoS) for bandwidth-demanding vehicular applications under realistic 5G highway operating conditions.
Ignacio Vidal, Sandy Bolufé, K. Toledo· Italian National Conference...· 0 citations
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.
Swadhin Singh, Swatantra Kumar, Mr. Rahul Kumar· International Journal of Adv...· 0 citations
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.