Aug 2026· IEEE Transactions on Vehicular Technology· 0 citations· 26 references
Computer Science
TL;DR
This paper leverages Open RAN to manage V2X communication and proposes a multi-agent reinforcement learning (MARL) resource-aware system that aims to mitigate interference, optimize resource usage, and enhance quality of service by optimally selecting between sidelink and network transmissions.
Abstract
Future applications in the 6G-based Internet of Vehicles will leverage sidelink (SL) transmissions in Vehicle-to-Everything (V2X) scenarios. However, SL-based direct communication can significantly increase interference among vehicles and between vehicles and other entities of the Intelligent Transportation System. Thus, both Vehicle-to-Vehicle communications and Vulnerable Road Users (VRUs) uplink resources may be degraded or subject to starvation. Existing solutions primarily focus on improving resource allocation and pair selection. Nonetheless, they lack a comprehensive approach to tackle the communication modes and the entire network. To address these challenges, this paper leverages Open RAN to manage V2X communication and proposes a multi-agent reinforcement learning (MARL) resource-aware system. Open RAN provides control loops through a global view of the network and also an open interface-based framework for machine learning models applied to resource decision-making. Meanwhile, the MARL model aims to mitigate interference, optimize resource usage, and enhance quality of service by optimally selecting between sidelink and network transmissions. To reduce system complexity, this work employs a clustering strategy. Each agent manages a group of pairs, rather than assigning one agent to each pair. The solution supports this design by adopting a centralized training with decentralized execution approach, empowered by Open RAN. The strategy uses offline training and an off-policy approach, in which each agent stores experience for fine-tuning. Results indicate that the MARL approach reduces average loss by 21% and latency by 19% in Vehicle-only scenarios. In coexistence VRU scenarios, loss and latency drop by 18% and 30%, respectively, compared to the single-agent approach.
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.
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.
Naif S. Alshammari, Abdullah Alsaleh· Electronics· 0 citations
During disaster scenarios and periods of extreme data demand in next-generation wireless communications, conventional terrestrial networks (TNs) often become unreliable or fail entirely, leading to critical service disruptions. In such contexts, non-terrestrial networks (NTNs) emerge as a promising solution to ubiquitous and resilient connectivity. Furthermore, the open radio access network (ORAN) paradigm facilitates network disaggregation and flexible functional splitting among its key components, namely the central unit (CU), distributed unit (DU), and radio unit (RU), which can be deployed across heterogeneous NTN platforms according to service requirements. However, this flexibility introduces significant challenges in terms of network complexity and real-time control. To address these challenges, this paper proposes an intelligent ORAN-enabled NTN framework for emergency communication scenarios. The proposed system leverages graph neural networks (GNNs) to model the dynamic network topology and employs a reinforcement learning (RL)-based Q-learning algorithm, formulated as a Markov decision process (MDP), to enable adaptive and real-time network control. In this framework, network nodes are treated as states, and optimal decisions are learned based on system dynamics. The spatial distribution of user equipment (UE) is modeled using an inhomogeneous Poisson point process (IPPP) with a rejection sampling technique, capturing realistic user density variations. Simulation results demonstrate that the proposed GNN-enhanced RL approach significantly improves network performance in terms of latency and service reliability, thereby enabling efficient and robust operation under emergency conditions.
Recently, the development and deployment of intelligent controllers for radio access networks (RAN) has attracted significant attention from network operators and international telecommunications organizations, driven by rapid advances in artificial intelligence. Mobility management plays a fundamental role in ensuring seamless connectivity and service quality in 5G RAN. In fact, optimal control in 5G RAN is highly challenging due to its complex, dynamic, and distributed environment. Many approaches have been proposed to address this problem, particularly those based on deep reinforcement learning (DRL). However, contrary to the dense reward assumption in many DRL-based studies, mobility feedback in practical RAN environments is characteristically sparse and delayed. In this paper, we propose WHO (World Model for Handover Optimization), a novel method designed to bridge the gap between sparse feedback and efficient learning in 5G networks. WHO utilizes a world model to convert event-driven rewards into dense predictive signals, facilitating robust multi-agent optimization. Field experiments involving 13 base stations and 39 cells show that the proposed method significantly improves handover performance and network stability compared to conventional DRL approaches, achieving 19–40% higher prediction precision and up to 32% improvement in key performance indicators (KPIs).
Uyen Thi Thu Truong, Doan Van Nguyen, Do Ngoc Tuan et al.· International Conference on...· 0 citations
Integrated sensing, communication, and computation (ISCC) provides a critical enabling platform in supporting the diverse services in the Internet of Vehicles (IoV). However, effective heterogeneous IoV service provisioning relies on both communication-centric and beyond-communication performance metrics, making unified resource allocation challenging. Moreover, competition from concurrent services for limited multi-dimensional resources is intensified in dynamic vehicular environments. In this paper, we investigate the resource allocation problem for concurrent communication and target classification services in an ISCC-enabled IoV system. To solve the problem, we first introduce the value of service (VoS) to unify communication rate and classification accuracy into a common measure that captures the degree of heterogeneous service fulfillment. To reduce the complexity of dynamic problem optimization, we propose a digital twin-assisted proximal policy optimization (DTPPO) algorithm, in which the digital twin exploits both current and historical information to generate predictive information, thereby enhancing policy learning in dynamic environments. Furthermore, we develop a large language model (LLM)-enhanced DTPPO (LLM-DTPPO) algorithm, which leverages the contextual understanding and domain knowledge of LLMs to reshape the reward function and improve resource allocation performance under multi-dimensional resource competition. Simulation results based on real-world vehicle mobility traces demonstrate that the proposed algorithms outperform existing benchmark schemes.
Bangzhen Huang, Zhang Liu, Lianfen Huang et al.· IEEE Transactions on Network...· 0 citations
: Space-Air-Ground Integrated Networks (SAGIN) provide a multi-layered, wide-coverage computing infrastructure for distributed urban sensing systems. However, their heterogeneity and dynamics pose unprecedented challenges for task offloading and resource allocation. Existing methods struggle to simultaneously address the complexity of cross-layer decision-making and reliability assurance under uncertain conditions. This paper proposes a novel framework, termed DRL-RA, which synergistically integrates Deep Reinforcement Learning (DRL) with reliability-aware optimization. The framework consists of two complementary components: (1) a Dueling Double Deep Q-Network (D3QN) module that learns adaptive policies to make offloading decisions among various options including local execution, terrestrial edge, UAVs, and satellites; (2) a Reliability-Aware Multi-Objective Optimization Framework (RA-MOOF) that introduces explicit reliability guarantees through cross-layer link reliability modeling, node availability estimation, and smooth reliability proxy functions. Addressing the heterogeneous communication characteristics of the SAGIN architecture, this paper establishes a complete cross-layer delay model and composite reliability metrics. The reliability formulation is defined under explicitly stated conditional-independence assumptions, and the proposed smooth constraint terms are treated as surrogate CMDP costs rather than exact hard chance-constraint guarantees. Extensive experiments in a SAGIN simulation environment demonstrate that the proposed method improves the task completion rate by 3.8%, reduces average latency by 11.1%, and increases system reliability by 3.9% compared to state-of-the-art benchmarks. The optimization-only RA-Opt baseline is used as a non-real-time optimization reference for assessing reliability-aware offloading decision quality, while deployment-time decision-latency comparisons are interpreted primarily among learned inference policies. Comprehensive ablation studies and statistical validation across multiple random seeds confirm the contributions of each component, while cross-layer offloading decision analysis verifies the effectiveness of the method across different network layer selections.
Fei-Yan Bu, Zheng Wang, Yong Pan et al.· Computers, Materials & C...· 0 citations
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