Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1152-1157· 0 citations· 15 references
Abstract
Cellular vehicle-to-everything (C-V2X) sidelink Mode 4 relies on sensing-based semi-persistent scheduling (SB-SPS) to enable distributed resource allocation without infrastructure support. However, conventional SB-SPS suffers from resource collisions and suboptimal slot reuse under high vehicle density, particularly in multi-lane highway environments with dynamic topology changes. This paper proposes a federated learning–assisted slot-specific SB-SPS framework that enhances distributed scheduling efficiency while preserving decentralized operation. Instead of centralized optimization, vehicles locally learn slot occupancy patterns and collaboratively update a lightweight global model through federated aggregation. The proposed approach integrates slot-level sensing statistics with adaptive candidate resource selection, enabling improved collision avoidance and resource reuse efficiency. Simulation results under representative multi-lane highway scenarios demonstrate that the proposed method significantly reduces packet collision probability and improves packet reception ratio compared with conventional SB-SPS and heuristic-based approaches while maintaining scalable and communication-efficient model updates. The results indicate that federated edge intelligence can effectively enhance distributed sidelink resource management in future B5G and 6G vehicular networks.
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
The evolution of sixth‐generation (6G) wireless networks demands ultra‐reliable low‐latency communication (URLLC), massive connectivity, and high‐capacity data transmission in highly dynamic environments. Space–Air–Ground Integrated Networks (SAGINs) have emerged as a promising architecture by seamlessly integrating satellites, unmanned aerial vehicles (UAVs), and terrestrial infrastructure to provide ubiquitous connectivity. However, stochastic traffic arrivals, UAV mobility, time‐varying wireless channels, and the coexistence of enhanced Mobile Broadband (eMBB) and URLLC services make traffic offloading and resource allocation highly challenging. These factors transform the optimization task into a stochastic mixed‐integer nonlinear programming (MINLP) problem. To address this challenge, this paper proposes a Quantum Federated Reinforcement Learning (QFRL)‐based traffic offloading framework for RSMA‐enabled SAGINs. The optimization problem is formulated as a constrained Markov decision process (CMDP), allowing distributed small cells to jointly optimize traffic offloading ratios, bandwidth allocation, RSMA power distribution, and UAV trajectory planning while satisfying stringent delay and reliability requirements. A variational quantum circuit (VQC)‐based actor‐critic architecture is developed to improve learning efficiency and policy representation in high‐dimensional continuous action spaces. In addition, a federated aggregation mechanism enables privacy‐preserving distributed learning and scalable coordination across the space, air, and ground segments. The proposed framework employs temporal‐difference learning and parameter‐shift gradient optimization to ensure stable convergence under stochastic network dynamics. Simulation results demonstrated that the proposed QFRL framework reduces traffic dropping probability by 28%–35%, decreases URLLC delay by 22%–30%, improves network availability by 18%–25%, and enhances traffic offloading efficiency by 20%–27% compared with Differentiated Federated Soft Actor‐Critic (DFSAC), Double Q‐Learning delay sensitive replay memory (DSRPM), and Nash Equilibrium Iteration Offloading (NEIO‐G) schemes, respectively.
Ishan Budhiraja, Abhay Bansal, B. Unhelkar et al.· Transactions on Emerging Tel...· 0 citations
Dynamic spectrum access (DSA) in 5G IoT setups with cognitive radio is characterized by rapid and decentralized decision-making processes in highly non-stationary wireless environments, limited communication needs, and restrictive bounds. In this work, we present F-DMRL, a federated, communication-efficient decentralized meta-reinforcement learning framework for allowing a massive number of IoT devices to meta-learn collectively about spectrum-access strategies in a decentralized way without centralized control and without an extensive amount of inter-agent communication. Our method incorporates lightweight federated meta-parameter aggregation with gradient sparsification and periodic communication, allowing devices to only compress the meta-updates during this process and then adapt locally for task-specificity. We have presented analytical speedup guarantees and upper bounds on communication cost under bounded environmental drift and shown that using the approach proposed here, F-DMRL preserves convergence properties while posing a large reduction in coordination overhead at the same time. Simulations across various 5G IoT spectrum environments showed that F-DMRL performed faster adaptation (up to 45% fewer episodes), higher spectral efficiency, and lower interference probability compared to centralized meta-RL, federated DRL, and traditional decentralized RL baselines. Simulation results averaged across 10 independent runs demonstrate improvements of 45% faster adaptation and 60–80% lower communication overhead relative to baseline methods, while maintaining stable convergence.
Jayesh Kumar Dabi, Priyadarshi Ashok Dahat· International Journal of Wir...· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
The rapid growth of Internet of Vehicles (IoV) applications has imposed strict requirements on low-latency and energy-efficient computing services. This letter investigates a multi-Uncrewed Aerial Vehicle (UAV)-assisted IoV system, where multiple Mobile Edge Computing (MEC)-enabled UAVs (MUs) collaboratively provide computing services for vehicular terminals (VTs). To improve service capability, we propose an energy-efficient task offloading and load balancing scheme that jointly considers vehicle mobility, task offloading and migration, and computing resource allocation to formulate an optimization problem. To solve this problem, a collective learning (CL)-enabled multi-agent reinforcement learning (CL-MARL) algorithm is proposed, where each agent learns optimal policies through centralized training and collective cooperative learning. Simulation results demonstrate that the proposed scheme outperforms benchmark strategies in terms of energy efficiency, task completion rate, and load balancing.
Yongbin Wang, Peng Lin, Yan Liu et al.· IEEE Wireless Communications...· 0 citations
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.
M. Barbosa, K. Dias· IEEE Transactions on Vehicul...· 0 citations