The integration of wireless power transfer (WPT) and mobile edge computing (MEC) provides an effective solution for overcoming the energy and computational limitations of Internet of Things (IoT) devices by enabling them to harvest energy from radio frequency signals and offload data to edge servers. A crucial challenge in wireless powered MEC (WP-MEC) networks is how to efficiently optimize offloading decisions and resource allocation to enhance overall system performance. In this paper, we investigate the partial offloading strategy within a WP-MEC network consisting of multiple HAPs. The optimization problem is formulated as a Mixed-Integer Non-linear Programming (MINLP) problem with variables of WPT duration, offloading decisions and energy allocation. To solve this problem, we propose a deep reinforcement learning (DRL)-based framework, which employs a neural network architecture combining convolutional and fully connected layers to output offloading decisions. Additionally, we design an optimization algorithm for joint optimization of WPT duration and offloading proportions. Numerical results demonstrate the proposed method achieves better performance than the existing DRL methods, which demonstrates the efficiency of the proposed method.

A covert ISAC transmission scheme based on an innovative heterogeneous sub-connected hybrid analog and digital (H2AD) multiple-input multiple-output (MIMO) transceiver is investigated in this paper. Specifically, H2AD ISAC system possesses the capability to detect the point-like target while covertly transmitting confidential information to a singleantenna legitimate user, and enabling secure transmission without detection by the warden. The objective is to maximize the covert transmission rate for legitimate users while adhering to the Cramér-Rao bound (CRB) threshold. However, due to the coupling of multiple variables under the H2AD transceiver framework, the optimization problem becomes non-convex. To tackle the challenging, an alternating optimization algorithm based on Dinkelbach’s transformation and semidefinite relaxation (DTSDR) is proposed to design the analog and digital beamforming along with the sensing signal. Then, by utilizing historical system states and optimizing for long-term returns, an improved distributional soft Actor-Critic with three refinements (DSACv2) algorithm framework based on deep reinforcement learning (DRL) is proposed. Simulation results demonstrate that incorporating the novel H2AD MIMO antenna array into ISAC system design enhances the covert performance while ensuring target sensing performance.

In recent years, unmanned aerial vehicle (UAV) has swept across the communication industry for its superior mobility and flexibility. UAV-aided communicaiton has been widely concerned by the academia and industry in space-air-ground integrated network, disaster relief network, Internet of Thing (IoT) system and so on. With the development of UAV-aided communication technology, the performance of transmission has been significantly improved. Subsequently, the problem of security transmission of UAV-aided communication has become increasingly prominent. In this paper, the security capacity of a UAV-aided IoT system is studied. Firstly, a channel field multiple access (CFMA) wireless sensor data transmission framework is proposed in UAV-aided IoT system. It contains a UAV base station, multiple IoT terminals and several malicious users. Under this framework, the UAV base station regularly flies over the area where the IoT system is located. The IoT terminals transmit sensing data based on the CFMA scheme. At the same time, surrounding malicious users cooperatively eavesdrop the sensing data of IoT terminal. Secondly, the system security capacity is analyzed and given under proposed framework. Through analysis, it can be concluded that the CFMA framework inherently possesses anti-eavesdropping capabilities, and the security capacity differences among different users are not significant. This is highly compatible with the requirements of the UAV-IoT system. Finally, the simulation results prove that the CFMA framework can meet the sensing data security transmission in the UAV-aided IoT system.

Next-generation wireless network should accomplish integrated sensing, communication, and computation (ISCC) capabilities. This paper proposes a novel covert transmission scheme based on active reconfigurable intelligent surface (RIS)-enabled full-duplex (FD) unmanned aerial vehicle (UAV)-ISCC framework, where the multi-functional UAV realizes simultaneous target sensing and uplink (UL) covert communication, as well as performing edge computing (EC) for users. To maximize the minimum covert transmission rate (CTR) among all UL users, UAV transmit beamforming and trajectory, RIS weights, power allocation and signal processing in a FD UL transmission system are jointly devised. To tackle the intractable non-convex problem, we leverage second order cone programming (SOCP), penalty-dual-decomposition (PDD) and successive convex approximation (SCA), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the active RIS and UAV techniques into the optimization design, the covert transmission performance of ISCC systems are improved while ensuring a certain level of target sensing and EC performance.

In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom) enabled integrated sensing, communication and computation (ISCC) systems. We consider the joint design of transmit beamforming at the BS and the reflecting coefficients at the RIS as well as the computation resource allocation of each user. The optimization problem for the max computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the alternative optimization (OA) and the alternating direction method of multipliers (ADMM) algorithm is developed. Furthermore, a more computationally efficient approach is introduced, which utilizes transmit beamforming based on an accelerated primal gradient (APG) method. Furthermore, the approximation principle is proposed to transform non-convex constraints in the optimization of the reflection coefficients at RISs. Simulation results show that introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

Abstract The channel in massive multiple‐input multiple‐output systems is fast‐varying that the pilot signal needs to be sent frequently. To obtain timely channel state information with less pilot overheads, a symbol detection aided channel prediction scheme is proposed in this paper. Then, the prediction error lower bound of the proposed scheme within one interval of effective prediction is analysed. Besides, the approximate close‐form post‐processing signal to noise ratio is derived for zero‐forcing detector with imperfect channel predictions. Numerical simulations are implemented to verify the validity of theoretical analysis. The results show that the theoretical expressions have a close match with the real simulated performance under various simulation parameter settings. In addition, the frequency of transmitting the pilot signals can be significantly reduced when adopting this proposed method. Moreover, the application of the proposed scheme can be further expanded when combining it with channel coding, thereby greatly improving the spectrum efficiency of the system.

In extremely low signal-to-noise ratio (SNR) region, the useful features of the signal are weakened by higher-power noise, making it difficult for conventional direction-of-arrival (DOA) estimation methods to adequately exploit and extract the low-SNR signal features. Thus, a generative adversarial network (GAN) is presented to learn the underlying features and complex distributions of high-SNR covariance matrices. The introduced GAN establishes a mapping between low-SNR and high-SNR covariance matrices, thereby generating first-rate high-SNR covariance matrices that closely resemble real high-SNR matrices. Also, it effectively captures signal features that are overwhelmed by excessive noise power. Additionally, to improve the performance of convolutional neural network (CNN)-based DOA estimation models in medium-to-high SNR ranges, a deep residual attention network (DRANet) is designed to significantly enhance DOA estimation accuracy in such SNR region. By integrating residual and attention modules, the network effectively filters key features. This enhances feature learning and adaptability, allowing it to capture DOA-related features more proficiently. The experimental results indicate that the developed GAN-DRANet approach can approach the CRLB in the extremely low SNR range and improves the estimation resolution limits of the other two DL-based methods, DNN and CNN, in medium to high SNR conditions.

In future networks, the integrated sensing, communication and computation (ISCC) has gradually become a research hotspot. In this paper, we investigate a novel computation resource allocation scheme for reconfigurable intelligent surfaces (RIS) backscatter communication (BackCom)-aided ISCC system. We consider the joint design of transmit beamforming at BS and the reflecting coefficients at RIS as well as the computation resource allocation of each user. The optimization problem for the max-min computation efficiency (CE) under the constraints of power consumption, the Cramér-Rao bound (CRB) for angles estimation and communication requirement of each user is formulated. To deal with the intractable optimization problem, the block coordinate descent (BCD) algorithm is utilized to tackle the joint optimization problem. We propose the penalty function-based successive convex approximation (SCA) method to optimize the reflecting coefficients and the majorization-minimization (MM) framework to design the transmit beamforming, respectively. In addition, considering the high complexity of the proposed SCA based algorithm, we design a low-complexity beamforming and reflection coefficient scheme for a special case of single target scenario. Simulation results show that the introduction of RIS-BackCom can improve the efficiency of computing and maintain the tradeoff between CE and sensing performance.

In this paper, we propose a novel covert transmission scheme for reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) system with imperfect channel state information (CSI). Considering full and partial channel uncertainty models, we jointly devise the digital and holographic beamforming along with the receive filter to maximize the worst-case and outage-constrained achievable rate (AR) of communication users while guaranteeing the sensing capability and covertness requirement. The resulting optimization problems are difficult to solve owing to the non-convexity caused by the semi-infinite constraints (SICs) and the coupled design variables. After approximating the worst-case and outage constraints by exploiting the S-procedure, successive convex approximation (SCA) and Bernstein-type inequality, we propose a secure solution that efficiently optimizes all variables by using convex optimization methods. To understand the proposed algorithm better, both the convergence and computational complexity are discussed. Simulation results show that by incorporating the RHS technique into the optimization design, the covert transmission performance of ISAC systems are improved while ensuring a certain level of sensing performance.

Smart cities have entered into a new era with the widespread commercialization of 5G. As a key supporting technique for smart cities, Internet of everything (IoE) puts forward higher requirements for real-time data and massive access. In this article, we propose a novel embedded cognitive radio (CR) enabled rate splitting multiple access (ECR_RSMA) solution, in which the secondary user (SU) can access more than one idle spectrum holes of the primary users (PUs) by spitting the data rate without causing additional interference to the PUs. To ensure the quality of service (QoS) of each user, a composite successive interference cancellation (SIC) decoding scheme is specially designed, and the channel prediction algorithm is employed to combat the channel aging effect. The effectiveness of the proposed ECR_RSMA is verified through computer simulations. Compared with the state-of-the-art MA schemes, such as CR_NOMA, the proposed solution can significantly improve the performance in terms of the spectrum efficiency as well as the outage probability.

The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs’ offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

The smart grid is expected to be integrated with advanced communication and control technologies to enhance its efficiency and reliability, enabling bidirectional information and energy exchanges between power providers and consumers. Considering the substantial data generated by the smart grid, mobile-edge computing (MEC) is introduced to address the need for considerable computing capacity. Limited attention has been directed toward exploring the application of MEC in smart grid scenarios. In this article, we innovatively consider maximizing the sum of computing rate and weighted energy trading benefit for a scenario with the distributed energy resource aggregation, smart grid, and MEC. We formulate the considered utility maximization as a mixed integer nonlinear programming (MINLP) problem. In order to solve this problem, we break it down into two distinct subproblems: 1) the binary offloading decision problem and 2) the energy allocation problem. We propose a deep reinforcement learning (DRL)-based framework to determine the offloading decision and design an optimization algorithm for the energy allocation problem. The simulation results indicate that the proposed algorithm achieves approximately 98% of the traversal algorithm’s performance with only one-thousandth of its processing time.

In this article, channel estimation (CE) of an active intelligent reflecting surface (IRS) aided uplink Internet of Things (IoT) network is investigated. First, the least square (LS) estimators for the direct channel and the cascaded channel are presented, respectively. The corresponding mean-square errors (MSEs) of channel estimators are derived. Subsequently, in order to evaluate the influence of adjusting the transmit power at the IoT devices or the reflected power at the active IRS on Sum-MSE performance, two situations are considered. In the first case, under the total power sum constraint of the IoT devices and active IRS, the closed-form expression of the optimal power allocation (PA) factor is derived. In the second case, when the transmit power at the IoT devices is fixed, there exists an optimal reflective power at active IRS. To further improve the estimation performance, the convolutional neural network (CNN)-based direct CE (CDCE) algorithm and the CNN-based cascaded CE (CCCE) algorithm are designed. Finally, simulation results demonstrate the existence of an optimal PA strategy that minimizes the Sum-MSE, and further validate the superiority of the proposed CDCE/CCCE algorithms over their respective traditional LS and minimum MSE (MMSE) baselines.

In recent years, wireless powered mobile edge computing (WP-MEC) is one of solutions to the problem of insufficient computing power and battery capacity of current edge devices (EDs). In this paper, we consider a WP-MEC network with multiple EDs and study the problem of maximizing the long-term computation rate of the system under the premise of maintaining the stability of the system data queue. Specifically, the objective function is described as a complex non-convex problem, we used Lyapunov optimization theory to decouple the multi-stage continuous random problem into sub-problems of deterministic frames. The problem of determining frames requires joint optimization of wireless power transfer (WPT) duration, local computing frequency, transmission duration, and energy required for offloading. In order to efficiently optimize these variables, we design a DRL-based algorithm combined with the CVX solver, the DRL algorithm learns the WPT duration, and the convex optimization algorithm obtains the system offloading strategy. From the simulation results, our algorithm can achieve performance close to that of the one-dimension exhaustive search algorithm while ensuring the stability of the data queue.

Reconfigurable intelligent surface (RIS), as a revolutionary technique, appears to ameliorate undesirable propagation environment in a controllable manner, which has the potential to substantially boost security of private information in the future smart radio environment. Much recent attention has been directed to the RIS-enhanced secure communication, among which the terrestrial network scenarios are generally concerned, while the exploration of aerial-ground communication integrated with RIS remains an open issue. Furthermore, in the available research, the ideal assumption of line-of-sight channel for aerial-ground link cannot be exploited in complex urban environment. Inspired by this, we provide in this paper the secrecy rate maximization problem under a complex urban scenario, where the drone's high mobility and RIS's tunable capability are utilized to against the potential eavesdropper. Although the established problem is intractable to tackle, we devise an iterative algorithm combining alternating optimization and successive convex approximation method as the solution, which jointly optimizes the phase shifts, the drone's trajectory along with transmit power for the single-user scenario. The proposed designs are also extended to the multi-user multi-eavesdropper system. Eventually, extensive simulation experiments indicate the remarkable benefits brought by our proposed scheme on secrecy performance and the necessity of optimizing phase shifts.

Large-scale connectivity support is a critical challenge in the massive machine-type communications scenario. Grant-free random access (RA) is a promising solution because it can reduce severe signaling overhead in contention-based RA procedure. However, there will still be collisions due to the random selection of spectrum resources by the devices. Therefore, we propose a distributed Q-learning-assisted grant-free RA scheme to alleviate the collisions between devices. Considering the characteristic of the machine-type communications devices with bursty traffic, the random packet arrival model is adopted in this paper. In order to cope with the difficulties brought by the random transmission of devices to Q-learning, an action reward based on the active probabilities of devices is designed. In addition, we introduce the power domain nor-orthogonal multiple access to further enhance the number of accessible devices. Numerical results demonstrate the advantages of the proposed scheme from the devices' successful access probability.

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.

With the evolution of wireless networks, new techniques including massive multiple-input multiple- output (MIMO) and millimeter wave are adopted to satisfy the demands for diversified services. However, it has been verified by field tests that the traditional wide sense stationary assumption for wireless channel does not hold anymore. As a result, traditional channel state information (CSI) acquisition methods, especially the statistical CSI acquisition, cannot be applied straightforwardly in such a circumstance. In this paper, we propose a pre-processing method for channel sensing in the non-stationary environment. Specifically, the data sampled from channel training is treated as a channel image, where the statistical channel state is represented by gray-scale. Then the computer vision technique, specifically, the edge detection method, is used on the channel image to detect the homogeneous sub-regions. Within each sub-region, the channel is statistically stationary, and then the CSI can be obtained by existing methods. It is verified by simulation results that, the proposed method can help to improve the CSI acquisition accuracy in the non- stationary environment.

Smart city is coming into urban life with the development of information and communication technologies. As an integral part of the smart city, massive heterogeneous Internet of Things devices face enormous challenges in multiple accessing and signal processing. In this paper, we propose an innovative and flexible scheduling method under the channel field multiple access framework to meet these requirements. In particular, artificial intelligence (AI) technology is adopted by the proposed solution. It is proved that the proposed AI-based scheduling method can obtain better performance than the existing methods by low complexity, especially, such performance gain becomes more significant as the number of devices increases.

Safety messages propagation is the major task for Vehicular Cyber‐Physical Systems in order to improve the safety of roads and passengers. However, reducing traffic and car accidents can only be achieved by disseminating safety messages in a timely manner with high reliability. Although mathematical modeling of the delay of safety messages is extremely beneficial, analyzing the safety messages propagation is considerably complex due to the high dynamics of vehicles. Moreover, most previous works assume vehicles drive independently and the interaction between vehicles is not taken into consideration. In this paper, we proposed an analytical model to describe the performance of safety messages propagation in the VCPSs under platoon‐based driving pattern. Infrastructure‐less and RSU‐supported scenarios are evaluated independently. The analytical model also takes into account different transmission situations and various system parameters, such as communication range, traffic flow, and platoon size. The effectiveness of the analytical model is verified through simulation and the impacts of different parameters on the expected transmission delay are investigated. The results will help determine the system design parameters to satisfy the delay requirement for safety applications in VCPSs.