The increasing demand for ultra-reliable and low-latency communication (URLLC) in vehicle road cooperation systems (VRCS) has propelled the development of intelligent and efficient optimization techniques. This paper presents a Quantum Deep Q-Network (QDQN) based approach for minimizing latency in a Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface (STAR-RIS) enabled VRCS. STAR-RIS improves signal coverage and energy efficiency by simultaneously serving users in both transmission and reflection modes. However, latency optimization remains a critical challenge due to dynamic environments and computational complexity. The proposed QDQN technique integrates quantum computing principles with deep reinforcement learning (DRL) to accelerate decision making and optimize resource allocation in real time. Using quantum parallelism and entanglement, QDQN reduces convergence time while effectively learning the dynamic state of the communication environment. The simulation results demonstrate that the proposed method achieves a significant latency reduction compared to conventional DRL and classical Q-learning techniques. This study highlights the potential of quantum-enhanced reinforcement learning for future URLLC applications in intelligent vehicular networks.

With the advent of smart and autonomous vehicles, a number of novel data-intensive and latency-critical vehicular communication applications have emerged. However, dynamic vehicular mobility and urban environments introduce severe propagation challenges, leading to increased latency. In order to reduce latency in Vehicle Road Cooperative Systems (VRCS), this research introduces a unique architecture that combines Asynchronous Federated Learning (AFL) with Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RIS). The proposed system leverages a Markov Decision Process (MDP)-based optimization framework to minimize latency by jointly optimizing STAR-RIS elements and offloading decisions. Our approach allows vehicles to asynchronously update global models, ensuring robust learning while adapting to dynamic network conditions. The simulation results show that the recommended strategy provides at least a 20 % reduction in latency in AFL when compared to FL.

The rapid expansion of the Industrial Internet of Things (IIoT) in vehicular networks has significantly increased the demand for energy-efficient and low-latency communication to support intelligent transportation systems. However, the associated carbon footprint poses major challenges to sustainable development. To resolve these problems, we propose an Energy-Efficient and Latency-Minimizing Simultaneously Transmitting and Reflecting-Intelligent Reflecting Surface (STAR-IRS)-Assisted Vehicle-Road Cooperative System (VRCS) within a Carbon-Aware IIoT environment, leveraging Quantized Federated Reinforcement Learning (Q-FRL). The STAR-IRS dynamically enhances signal strength and energy efficiency by adjusting transmission and reflection coefficients, ensuring robust connectivity in complex vehicular environments. Q-FRL helps adjust STAR-IRS settings in real time while reducing computational complexity through quantized decision-making. Adaptive quantized DDPG improves energy efficiency, whereas adaptive quantized DDQN minimizes latency under carbon-aware constraints. Simulation results substantiate the performance of the proposed framework, demonstrating superior energy efficiency, lower latency, and reduced carbon emissions. Specifically, the adaptive quantized DDPG (AQ-DDPG) reduces energy consumption by 31.57%, while gradient quantized DDPG (GQ-DDPG) and fixed (4-bit) DDPG improve it by 16.84% and 6.31%, respectively, compared to fixed (2-bit) DDPG. Furthermore, AQ-DDPG reduces vehicle carbon emissions by 15% and 10% compared to FQ-DDPG and GQ-DDPG, respectively.

The vehicle-road cooperation system (VRCS) facilitates vehicle-to-vehicle (V2V) communication for future vehicle usage in sixth generation (6G) networks. The implementation of the 6G network has made it possible for V2V communication to enhance network density, optimize transmission mode selection, and offer connectivity between vehicles while guaranteeing Quality of Service (QoS). However, there are inherent challenges, such as limited bandwidth, diverse QoS requirements, interference, and power constraints, associated with resource allocation and mode selection in V2V and vehicle-to-everything (V2X) communication. In this article, we jointly optimized the mode selection and resource allocation problems in VRCS by using simultaneous transmission and reflection-reconfigurable intelligent surface (STAR-RIS). The proposed model utilizes quantum federated reinforcement-learning (QFRL)-based augmented intelligence algorithms within the STAR-RIS VRCS framework. The proposed QFRL algorithm is a promising solution for advanced decision making, automation to improve traffic flow, reduces traffic congestion, and improve safety in the STAR-RIS assisted VRCS. Additionally, by leveraging the unique processing advantage of quantum computing will make the VRCS more capable of handling the enormous amount of real-time data that IoT devices send, which is necessary for the intelligent services it offers. The proposed model QFRL-based STAR-RIS assisted VRCS approach maximizes vehicle-to-infrastructure (V2I) user capacity while meeting the reliability requirement of V2V pairs. Finally, the simulation results prove the superiority of the QFRL algorithm against baseline schemes like quantum federated learning (QFL), federated reinforcement learning (FRL), and federated learning (FL) algorithms for V2V pairs. Furthermore, the performance evaluation findings indicate that the proposed STAR-RIS assisted QFRL algorithm performs 20.5%, 32.2%, and 46.7% better than QFL, FRL, and FL.

Unmanned aerial vehicles (UAVs), which can help with high-speed communications and provide better coverage, are an important component of next-generation wireless networks. Because of its high mobility and aerial nature, it is suitable for a wide range of mobile wireless communications-based applications. However, low data rates with limited transmission power constitute a significant difficulty in wireless communication that lowers network performance. To overcome this issue, integrating a UAV with a relay device capable of delivering high data speeds while utilising minimum transmission power is a promising approach. In this research, we presented an edge-cutting framework called UAV-IRS, in which an Intelligent reflective surface (IRS) supports unmanned aerial vehicles (UAVs) that traverse areas with low signal strength. Furthermore, we discussed the applications, challenges and research directions of UAV-IRS in vehicle-to-everything (V2X) communication. We considered a case study of UAV-IRS in V2X communication. The performance evaluation demonstrates how the viable data rate and minimum transmission power decrease with distance as the number of IRS elements increases.