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.

We present a blockchain-assisted mobile edge computing architecture for adaptive resource distribution in wireless communication systems, where the blockchain acts as an overhead system that provide command and control functionalities. In this context, achieving consensus across nodes while also ensuring the functionality of both MEC and blockchain systems is a big difficulty. Furthermore, resource distribution, frame size, and the number of sequential blocks generated by each contributor are important to Blockchain aided MEC functionality. As a result, a strategy for dynamic resource distribution and block creation is presented. To strengthen the efficiency of the overlapped blockchain system and enhance the quality of services (QoS) of the clients in the technologies to facilitate MEC system, spectrum allocation, frame size, and number of developing blocks for each distributor are framed as a joint optimization method that takes into account time-varying communication channels and MEC server saturation is defined. We use deep reinforcement learning (RAMBAN) to address this issue because standard approaches are ineffective. The simulation findings demonstrate that the efficacy of the suggested strategy when compared to different baseline approaches.

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 Internet of Things (IoT) has become a revolutionary concept that connects various devices and systems to enable smooth communication and data exchange. In this vast network, unmanned aerial agents (UAAs)-assisted mobile edge computing (MEC) communication plays a crucial role in facilitating direct interaction between edge devices. This aspect of IoT goes beyond traditional interactions between humans and machines. It creates a dynamic environment where devices collaborate autonomously, share information, and perform tasks. UAA-assisted MEC network offers several benefits, such as supports short range communication, reduced delay, improved scalability, and enhanced energy efficiency. Furthermore, for the purpose of enhancing the widespread interconnection and exceptionally dependable minimal delay in the fifth generation (5G) and beyond network, the utilization of nonorthogonal multiple access (NOMA) can be considered. Within this context, the impact of federated learning (FL) on NOMA-based UAV-assisted MEC network in wirelesspowered communication networks is examined. Initially, the transmitters extract energy from the radio frequency signals emitted by the MEC server. Subsequently, the transmitters utilize NOMA to establish communication with the receivers by utilizing the stored harvested energy. The formulation of a stochastic optimization problem is proposed with the aim of improving energy consumption (EC) and minimizing delay. Results indicate that the proposed scheme exhibit superior accuracy compared to baseline schemes, achieving an accuracy 98.37% after 59 communication rounds. The FL is employed to attain the objective and accelerate the local training data across the UAA-assisted MEC network.

Uncrewed aerial vehicles (UAVs) have made a substantial contribution to vehicle communications in recent times, and they provide viable ways to improve connection in contemporary transportation networks. However, maintaining consistent signal coverage, the limited computation capacity of UAVs, and getting past obstructions to maintain direct communication with vehicles is still tedious. To address the same, In this paper, an edge-enabled digital twin (DT) of UAV with an intelligent reflecting surface (IRS)-aided vehicular network is investigated. We specifically concentrate on the issue of minimizing the net energy consumption of the system in task-offloading while simultaneously optimizing IRS phase-shift, power allocation and task-offloading parameters through the use of DT architecture. We first describe the specified non-convex optimization issue as a Markov decision process (MDP) to address it. Eventually, we propose a hybrid federated learning (HFL) algorithm that aims to maximize energy efficiency (EE) by optimising related parameters. This method also enhances the system’s overall performance by lowering energy consumption and using the combined experiences of several agents. Compared to the benchmark schemes, HFL proves to be 20.5% and 47.6% more efficient than MAD2PG and DQN respectively. Simulation results affirm that the suggested method outperforms the benchmark techniques in terms of EE and learning accuracy.

The battery issue with Internet of Things (IoT) devices has been identified as a feasible solution in the shape of forthcoming backscatter communication technology. Wireless sensor networks, for example, that use backscatter communication technology can effectively monitor remote situations without requiring regular battery maintenance or replacement. Unfortunately, the transmission range of backscatter communication is limited. To overcome this issue, we proposed a solution that employs several unmanned aerial vehicles (UAVs) to aid in data collection. These UAVs may approach the backscatter sensor node (BSN), activate it, and then collect data. Our goal is to lower the overall flight duration required for rechargeable UAVs after the data collection mission is completed. The simulation results show that the proposed algorithms PDQN may outperform multiagent deep deterministic policy gradient (MADDPG), deep deterministic policy gradient (DDPG), and deep Q-network (DQN) approaches.

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.

In this new digital era, social media has created a severe impact on the lives of people. In recent times, fake news content on social media has become one of the major challenging problems for society. The dissemination of fabricated and false news articles includes multimodal data in the form of text and images. The previous methods have mainly focused on unimodal analysis. Moreover, for multimodal analysis, researchers fail to keep the unique characteristics corresponding to each modality. This article aims to overcome these limitations by proposing an efficient transformer-based multilevel attention (ETMA) framework for multimodal fake news detection, which comprises the following components: a visual attention-based encoder, a textual attention-based encoder, and joint attention-based learning. Each component utilizes different forms of attention mechanisms and uniquely deals with multimodal data to detect fraudulent content. The efficacy of the proposed network is validated by conducting several experiments on four real-world fake news datasets: Twitter, Jruvika fake news dataset, Pontes fake news dataset, and Risdal fake news dataset using multiple evaluation metrics. The results show that the proposed method outperforms the baseline methods on all four datasets. Furthermore, the computation time of the model is also lower than the state-of-the-art methods.

Device-to-device communication (D2D-C) is an leading edge technique in 5G and forthcoming 6G networks due benefits for enhanced spectrum efficiency and energy-efficiency (EE). Despite these potential advantages, co-channel interference (CO-CI), cross-channel interference (CR-CI), and massive connectivity are the major issues in D2D-C. In order to handle these issues, an interference mitigation technique for D2D mobile groups (D2Gs) utilizing up-link Non Orthogonal Multiplexing (NOMA) is presented to improve the EE of the overall network. D2Gs boost the SE by sharing the sub-channels (SCs) to cellular users (CUs), and NOMA links a huge number of D2D users (DUs) to D2D transmitters (DT). The problem is formulated as a mixed-integer nonlinear programming (MINLP) problem with associated SCs and power restrictions of the CUs and DUs. A deep reinforcement learning (DRL) based distributed deep deterministic policy gradient (D3PG) approach is considered to enhance the EE by addressing the resource allocation and power control of DUs. Numerical outcomes showed that the suggested scheme overcomes state-of-the-art techniques in terms of results.

We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods” while the FRL performs best among all.

To promote interaction between physical IoT assets and digital services, the rapid expansion of the Internet of Things (IoT) necessitates digitizing industrial processes. Integrating digital twins into an IoT network enables real-time virtualization of physical entities, allowing for efficient real-time control, rapid maintenance, and better decision-making. Furthermore, a digital twin-enabled IoT network may generate a vast amount of data, posing storage, processing, and security difficulties. In this study, we presented a blockchain and software-defined networking (SDN) integrated framework for offering decentralized and secure data operations in IoT networks to address these concerns. To filter malicious packets, the proposed system incorporates a packet analyzer and feature extraction modules at the SDN control layer. The blockchain is then constructed using an elliptic curve point technique for authenticating IoT devices. The results reveal that, when compared to the existing model, our suggested approach performs significantly better in terms of latency and throughput.

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.

Vehicle Edge Computing (VEC)-assisted computational offloading brings cloud computing closer to user equipment (UEs) at the edge of the access network by delivering various services to the UEs with limited processing power and battery. However, in fifth-generation and beyond 5G (B5G) networks, where UEs’ service requests and locations change dynamically, the deployment of static edge server deployments may lead to an increase in latency and total energy consumption. This paper presents a latency-energy-aware, efficient task offloading scheme for connected autonomous vehicular networks. Firstly, vehicles are assembled into clusters, in which vehicle can transmit tasks to the other vehicle, while on the other hand, the VEC server is used for processing the data. We developed a joint resource allocation and offloading decision optimization problem to minimize network latency and total energy usage. Due to the non-convex character of the optimization issue, we employed the Markov decision process (MDP) to convert it to a reinforcement learning (RL) problem. Then, we used a soft-actor critic-based scheme to achieve the optimal policy for resource allocation and task offloading to reduce the total latency and energy consumption for connected autonomous vehicles. Simulation analysis reveals that the proposed scheme attains 46.6% and 17.2% lesser delay, and 28.8% and 20.0% consumes less energy than the Hybrid DRL with Genetic Algorithm (HDRL-GA) and DRL based collaborative Data Scheduling (DRL-CDSS) state-of-art schemes.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surface (RIS) are the emerging technologies for 5G and beyond networks. These two techniques reduce inter-user interference and enhance the network's coverage performance. Despite this advantage, these two techniques are not able to satisfy the diversified quality of service (QoS) requirements of cellular mobile users under the presence of existing multiple access schemes. To tackle this issue, we integrate non-orthogonal multiple access (NOMA) with both these techniques. In this paper, our goal is to maximise the energy efficiency (EE) of the overall network by optimising the powers of UAVs and the phase shift matrix of RIS. The formulated problem is in a mixed-integer non-convex programming form. So, to solve this problem, a deep deterministic policy gradient (DDPG) approach is used in a centralised manner under a time-varying channel. The proposed NOMA-RIS scheme for multi-UAV networks achieves higher EE than the orthogonal multiple access (OMA)-RIS and random selection schemes, according to numerical results.

In this paper,Interference Reduction Scheme for Device-to-Device (D2D) Mobile Groups Using Uplink NOMA (ISHU)is proposed to maximize the throughput of the network. To achieve this goal, we integrated uplink non-orthogonal multiple access (NOMA) in the D2D mobile groups (DMGs). DMGs improve the spectral efficiency by sharing the resources with cellular mobile users (CMUs) whereas, uplink NOMA in DMGs associate the large number of D2D mobile users (DMUs) with the D2D transmitter (DDT). ISHU jointly optimizes the user group association and resource allocation in the uplink NOMA-enabled DMGs. The problem of joint user association and resource allocation is formulated as a mixed integer non-linear programming. To address this problem, we divided the problem of interference mitigation in two sub-problems and solved it independently. First, for joint user group association and sub-carrier assignment, a 3-D matching game is designed between the DMUs, DDT, and sub-carriers, respectively. Second, to optimize the power of DMUs across each sub-carriers, the branch and bound (BB) technique is used. Also, to reduce the complexity of ISHU scheme, the successive convex approximation low complexity (SCALE) technique is used across each sub-carrier. Simulated results demonstrated that ISHU provides 3.846 and 26.92 percent superior throughout as compared to the existing uplink conventional NOMA and OFDMA schemes.

Device-to-device (D2D) communication is one of the promising technology of the fifth-generation (5G) network. In D2D, the devices are in close proximity to each other communicate directly with or without depending upon the base station (BS), resulting in large gain, low latency, and high energy efficiency. Also, it improves the spectral efficiency by sharing the spectrum resources with cellular mobile users (CMUs). Despite these advantages, co-channel interference and eavesdropping attack on the D2D links are two major challenges. To overcome these issues, we used the power domain non orthogonal multiple access (PDNOMA) techniques with the D2D mobile groups (DMGs) under the social-domain scenario. The successive interference cancellation technique of PD-NOMA in the DMGs mitigate the intra-user and co-channel interference among the D2D receivers (DDRs), resulting in an increase in signal to interference noise ratio (SINR) and better quality of services. Furthermore, to improve the spectral efficiency, and reduce the security risk of the eavesdropper on the DMGs over each resource block (RB) in the presence of dynamic channel environment of imperfect channel state information, we used the coalition game approach. The simulated results show the proposed scheme achieves 5.5% and 27.77% higher sum rate and ensure 8.3% and 41.6% higher information secrecy as compared to first-order algorithm (FOA) and orthogonal frequency division multiple access (OFDMA) schemes.

In the last few years, we have witnessed the usage of billions of Internet-of-Things (IoT)-enabled devices in different applications starting from e-healthcare, transportation, agriculture, etc., across the globe. These interconnected devices share information using the Internet to improve the Quality of Service of the end users. There is a requirement of synchronization among the devices to provide scalability, reliability, and connectivity. Despite these advantages, proximity gain, interference, and fairness are various challenges for these devices in IoT which need to be resolved. To overcome these issues, we propose deep reinforcement learning (DRL)-based control scheme in the underlay of device-to-device (D2D) communication. D2D communication reuses the spectrum resources with cellular user equipment (CUE) to improve spectral efficiency. We propose the joint resource block (RB) scheduling and power control scheme to improve the sum rate of the network while considering the users' fairness among all the links. To solve this problem, first, we transform the nonconvex optimization problem into a multiagent reinforcement learning formulation using the Markov decision process (MDP). Then, to solve the RB allocation, we used the multiagent deep Q-network (DQN) framework to reduce the output dimension and improve the learning efficiency. Then, to convert the stochastic policy into deterministic policy, and to improve the fairness we combine the DQN with deep deterministic policy gradient to form the distributed deep deterministic policy gradient (DDDPG) scheme. Finally, to control the power of both the CUEs and D2D transmitters (DTs), we integrated the conventional optimization scheme with the DDDPG (CO-DDDPG). This combination enhances the convergence speed and reduces the computational complexity of the overall network. Numerical results show that the proposed scheme improves the network sum rate of 11.76% and the fairness 4.21% as compared to the state-of-the-art existing distributed DRL schemes.

Energy management of mobile devices is a crucial issue in fifth generation (5G) network due to their limited battery capacity. Simultaneous Wireless Information and Power Transfer (SWIPT) is an emerging technique which allows mobile devices to harvest energy from radio frequency (RF) signals. Moreover, Non-Orthogonal Multiple Access (NOMA) serves multiple users simultaneously using the same subchannel inter-user interference mitigation. By considering the aforementioned issues, in this paper, we propose a subchannel assignment scheme for SWIPT-NOMA based pico base station/femto base station with macro-cellular networks. The energy-efficient subchannel assignment is a probabilistic mixed non-convex optimization problem by considering imperfect channel state information (CSI). To address this problem, many-to-many matching theory is used in the proposal. Numerical results show that the proposed algorithm performs better in terms of numbers of PUs/FUs, average energy efficiency (EE) of the Picocells/Femtocells, in comparison to the orthogonal frequency division access scheme and conventional NOMA.

In the last few years, there has been an exponential increase in the deployment of 5G-based test beds across the globe with an aim to reduce the latency for accessing various applications. The integration of generic services such as enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), critical machine-type communication (cMTC), and ultra-reliable low-latency communications (URLLC) can improve the performance of 5G-based applications. This service heterogeneity can be achieved by network slicing for an optimized resource allocation and an emerging technology, Tactile Internet, to achieve low latency, high bandwidth, service availability, and end-to-end security. In this paper, we discuss the application-specific nonorthogonal multiple access (NOMA)-based communication architecture for Tactile Internet which allows nonorthogonal resource sharing from a pool of eMBB, mMTC, cMTC, and URLLC devices to a shared base station. We summarize various variants of NOMA and their suitability for future low latency Tactile-Internet-based applications.

Device-to-device (D2D) two-hop cooperative communication improves the network coverage and throughput to provide the quality of service and quality of experience to the end users. Nonorthogonal multiple access (NOMA) can be used at the D2D transmitter to improve the spectral efficiency of the network. But, two-hop transmission with NOMA suffers from delay and interference from the neighboring nodes. To resolve the aforementioned issues, in this paper, we propose Tactile Internet (TI) driven delay assessment for D2D communication (DIYA) scheme, which works in two phases. In the first phase, a full duplex communication at relays (intermediate nodes) is used to have the first- and second-hop transmission simultaneously in the same time slot. Then, TI-based communication is used at D2D transmitter to increase the speed of transmission. In the second phase, pricing-based three-dimensional (3-D) matching is proposed to improve the throughput of the cell edge users along with the mitigation of cochannel interference. Also, the power of the D2D transmitter is optimized using successive convex approximation with low complexity, which converts the nonconvex optimization problem of subchannel allocation and power control into convex problem. Numerical results demonstrate that DIYA achieves higher throughput with reduced delay in comparison to other existing orthogonal multiple access (OMA) and NOMA-based schemes.

In the last few years, we have witnessed an exponential increase in the popularity of Internet-enabled smart devices. In this era, various smart devices generate a huge amount of data during computing and communication. However, fixed infrastructure, especially in the dense population, have the issues of coverage and connectivity in this environment. But, 5G technology based femtocell emerges as one of the solutions for the aforementioned issues. Hence, in this paper, we investigate the non-orthogonal multiple access (NOMA) transmission with 5G enabled cognitive femtocell to attain higher spectral efficiency and to maximize the sum rate of femto users (FUs) with guaranteed QoS. A paring algorithm between strong and weak users has been proposed to reduce the NOMA interference between different FUs. To achieve higher data rates, the calculation of sum rate for an even/odd number of FUs in a femtocell is also proposed. Numerical results show that the proposed schemes effectively improve the sum rate of the cognitive femtocell under NOMA transmission to minimize the interference.