Coordinated Communication and Computing Resource Management Using Traffic Steering and Resource Slicing in O-RAN-Based Vehicle-to-Network Communications
Aug 2026· Future Internet· Vol 18, pp. 452· 0 citations· 16 references
TL;DR
A coordinated communication and computing resource management framework for O-RAN-based V2N communications and demonstrates a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions.
Abstract
Beyond 5G and future 6G services require radio access networks to support heterogeneous applications with diverse latency, reliability, throughput, mobility, and computing requirements. These challenges are particularly pronounced in vehicle-to-network (V2N) communications because of high mobility, dynamic channel conditions, frequent handovers, and heterogeneous service requirements. Conventional traffic-steering methods primarily rely on radio-side indicators, while computing-resource availability and traffic-specific computation demands are often considered separately. To address this limitation, this paper proposes a coordinated communication and computing resource management framework for O-RAN-based V2N communications. The framework integrates a traffic-management rApp (TM-rApp) in the non-real-time RIC with a traffic-steering xApp (TS-xApp) in the near-real-time RIC to enable policy-based closed-loop control. Candidate cells are ranked using communication quality, computing-resource capability and availability, predicted throughput, mobility characteristics, and traffic-class priority. As a proof-of-concept supporting component, proactive throughput forecasting is evaluated using standalone LSTM and stacked ensemble (S-LSTM) models based on lagged radio, mobility, load, and throughput features. The S-LSTM provides an adaptive mechanism for combining base learners but does not achieve a statistically significant improvement over the standalone LSTM; moreover, the forecasting evaluation uses fixed, non-optimized hyperparameters and a single chronological train–test split without cross-validation. Accordingly, the prediction results are interpreted as preliminary evidence of forecasting feasibility rather than as a definitive predictive-performance contribution. The framework further incorporates O-RAN-compatible traffic-steering policies, a minimum dwell-time constraint, and priority-aware resource allocation. Evaluation using a real-world corridor based on Al Haramain Expressway Road in Jeddah and a synthetic straight-highway scenario shows that the proposed method improves SLA compliance over RSS and HHAARC, achieves the highest computing-resource satisfaction, and reduces handovers relative to RSS. The results demonstrate a balanced trade-off among SLA compliance, computing-resource satisfaction, delay, throughput, and mobility robustness, while also showing that load-aware steering can provide higher aggregate SLA compliance under specific traffic distributions.
The rapid advancements of next-generation vehicular networks require intelligent, low-latency, and efficient resource management to support heterogeneous services. In this work, we propose a Traffic-aware Dynamic Resource Allocation (TADRA) architecture for UAV-assisted vehicular O-RAN to address the challenges of dynamic traffic conditions, infrastructure failures, and stringent quality of service (QoS) requirements. Due to the dynamic mobility and flexible deployment characteristics, UAV Open Radio Units (O-RUs) in the TADRA architecture support the terrestrial infrastructure under overload or failure conditions, dynamically extending coverage, balancing traffic loads, and restoring service to maintain uninterrupted QoS across diverse and heterogeneous traffic demands. Unlike existing static or single-layer solutions, our proposed TADRA integrates RAN Intelligent Controllers (RICs) with a Hierarchical Traffic-Aware Multi-Agent Twin-Delayed (TMT) algorithm to optimize the allocation of computation and radio resources. This joint optimization problem is NP-hard, highly dynamic, and coupled across agents, making TMT a tractable and adaptive alternative. This hierarchical framework performs traffic prioritization at the upper (application) layer and resource allocation at the lower (MAC) layer, facilitating adaptive decision-making under diverse vehicular traffic patterns. Numerical results demonstrate that our solution provides substantial gains over MATD3, MADDPG, and GA, achieving 17% lower latency, 10% higher throughput, 14% lower energy consumption, and 6.5% higher reliability.
Hayla Nahom Abishu, Ahmed Badawy, Amr Mohamed et al.· IEEE Transactions on Network...· 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
6G vehicular services, including cooperative perception, augmented reality navigation, and high-definition map updating, need computation support close to moving vehicles. Vehicular Edge Computing (VEC) is a natural solution, but the offloading decision becomes difficult when wireless channel conditions, vehicle density, and edge server loads vary simultaneously. In this paper, we study joint task offloading and resource allocation in 6G VEC with high- and low-frequency cooperation (HL-FC). We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP). Each vehicle decides its offloading ratio, transmission power, server association, and edge CPU request from local observations. To evaluate the proposed policy, we build a lightweight equation-driven Python simulator and compare MAPPO with Local-only, Edge-only, Random, and Greedy policies. Compared with Edge-only, MAPPO reduces the average system cost by 32.15%, 23.51%, and 17.13% under 10, 15, and 20 vehicles, respectively. It also improves the task completion rate by 21.00, 20.49, and 17.65 percentage points. Additional blockage experiments show that HL-FC keeps the policy more robust than high-frequency-only transmission under severe high-frequency blockage. The results reveal that MAPPO delivers better performance when edge resources become congested than in lightly loaded scenarios.
Zi-Heng Gu· 2026 8th International Confe...· 0 citations
A comprehensive and structured review of methods for MACNs, with particular emphasis on AI-driven solutions and their relationship to classical and hybrid alternatives, and offers insights into the design of AI-driven MACNs that are efficient, scalable, and adaptive to evolving network and service demands.
Shafkat Khan Siam, Muhammad Yeasir Arafat, Muhammad Morshed Alam et al.· Artificial Intelligence Revi...· 0 citations
Reliable low-latency communication is a critical requirement in enterprise wireless networks such as hospitals, offices, and campuses. This paper proposes an earliest deadline first (EDF)-Lyapunov-Robbins-Monro (ELR), a stochastic scheduling algorithm for IEEE 802.11bn (Wi-Fi 8) Multi-Access Point Coordination Coordinated-Spatial Reuse (MAPC C-SR) networks that jointly accounts for queue stability and deadline-aware latency regulation under bursty traffic. A Lyapunov drift-based criterion for a group is adopted to ensure queues remain stable under varying traffic loads. Since the optimal balance between queue backlog and deadline urgency cannot be determined a priori under bursty traffic, EDF term is incorporated into the selection metric with a tunable balance parameter $\alpha$, governed by Robbins-Monro stochastic approximation scheme. The proposed algorithm addresses the inability of existing schedulers to track sudden congestion under bursty traffic, by dynamically adjusting $\alpha$ to suppress sharp delay spikes. Simulations over a four-access point (AP) enterprise deployment under bursty Markov-Modulated Poisson Process (MMPP) traffic demonstrate that ELR achieves 14.23%, 13.26%, and 7.97% reduction in 99th percentile delay over maximum number of packets (MNP), oldest packet (OP), and traffic alignment tracker (TAT) respectively under high load with 16 stations (STAs).
Hiya Shah· International Conference on...· 0 citations
Vehicular ad hoc networks (VANETs) have emerged as a critical enabler of intelligent transportation systems, particularly when integrated with 5G infrastructure to achieve high-throughput, low-latency vehicle-to-everything (V2X) communication. Nevertheless, optimizing message routing in such environments remains a significant challenge, as the operational complexity and prohibitive cost of large-scale physical deployments severely limit empirical evaluation of alternative transmission strategies. This paper presents a stochastic Petri nets (SPNs) model for evaluating routing configurations in 5G-enabled vehicular ad hoc networks (5G-VANETs). The proposed model evaluates mean response time, drop probability, utilization, and throughput, enabling the identification of communication bottlenecks without requiring physical deployment. By abstracting the system's stochastic behavior through SPN formalism, the model supports both steady-state analysis and sensitivity evaluation under varying traffic workloads. Results demonstrate that Route 1, with direct RSU connection, achieves the lowest mean response time and highest throughput, while Route 3, which relays messages through a rear vehicle and an auxiliary RSU, yields the lowest drop probability. A sensitivity analysis based on Design of Experiments reveals that cloud capacity and cloud service time are the dominant factors affecting mean response time. The SPN model thus enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment.
José Miquéias Araújo, L. Lopes, Luiz Nelson Lima et al.· Journal of Internet Services...· 0 citations