Integrated Sensing, Communication, and Computation (ISCC) has the potential to meet diverse requirements of Internet of Vehicles (IoV), such as high reliability and low power consumption. However, existing works have not fully considered the problems of unreliable communication links and inefficient data processing under resource constraints in non-ideal environments. To address these issues, this paper proposes a predictive Integrated Sensing, Communication and Computation Over-the-Air (ISCCO) approach based on Orthogonal Time Frequency Space (OTFS) modulation. It takes high Doppler shifts, network dynamics, and resource constraints into account. In particular, the Road Side Unit (RSU) performs target tracking while communicating with the downlink users through Space Division Multiplexing (SDM), and receives the transmission results of the uplink. For the downlink, a predictive beamforming approach based on Extended Kalman Filtering (EKF) is employed, while Over-the-Air computation (AirComp) is utilized for the uplink. The transmit power and receive beamformer at the RSU, along with the transmit power of the uplink users, are jointly optimized through two formulated optimization problems: sensing performance maximization and power consumption minimization. To solve these problems, we adopt an Alternating Optimization (AO)-based algorithm for finding the local optimal solution. Simulation results validate the effectiveness of the AO-based algorithm, and the analysis of the trade-offs between multi-dimensional performance of ISCC and power consumption is conducted.

In this paper, we propose a novel physics-informed generative learning approach, named RadioDiff-k2, for accurate and efficient multipath-aware radio map (RM) construction. As future wireless communication evolves towards environment-aware paradigms, the accurate construction of RMs becomes crucial yet highly challenging. Conventional electromagnetic (EM)-based methods, such as full-wave solvers and ray-tracing approaches, exhibit substantial computational overhead and limited adaptability to dynamic scenarios. Although existing neural network (NN) approaches have efficient inferencing speed, they lack sufficient consideration of the underlying physics of EM wave propagation, limiting their effectiveness in accurately modeling critical EM singularities induced by complex multipath environments. To address these fundamental limitations, we propose a novel physics-inspired RM construction method guided explicitly by the Helmholtz equation, which inherently governs EM wave propagation. Specifically, based on the analysis of partial differential equations (PDEs), we theoretically establish a direct correspondence between EM singularities, which correspond to the critical spatial features influencing wireless propagation, and regions defined by negative wave numbers in the Helmholtz equation. We then design an innovative dual diffusion model (DM)-based large artificial intelligence framework comprising one DM dedicated to accurately inferring EM singularities and another DM responsible for reconstructing the complete RM using these singularities along with environmental contextual information. Experimental results demonstrate that the proposed RadioDiff-k2framework achieves state-of-the-art (SOTA) performance in both image-level RM construction and localization tasks, while maintaining inference latency within a few hundred milliseconds. Code is available at https://github.com/UNIC-Lab/RadioDiff-k.

Certificate management is needed for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing certificate management mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of certificate revocation checking (CRC) cannot be guaranteed in the presence of active adversaries; The certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is under constrained networks (e.g., it enters dead zones where direct communication with base stations fails).Worse still, compromising the secret key of a single certificate authority (CA) leads to certificate forgery. In this paper, we propose a forward-secure and privacy-preserving certificate management scheme, dubbed SNCM, for satellite networks, where a forward-secure signature algorithm is used to issue certificates. We utilize a neighboring-assisted forwarding paradigm in SNCM to support CRC in constrained networks. SNCM is secure against adversaries who invalidate CRC results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCM utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the CA and base stations handle CRC tasks from satellites in a cooperative way, which frees the CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCM, implement an SNCM prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.

Radio maps (RMs) are essential for environment-aware communication and sensing, providing location-specific wireless channel information. Existing RM construction methods often rely on precise environmental data and base station (BS) locations, which are not always available in dynamic or privacy-sensitive environments. While sparse measurement techniques reduce data collection, the impact of noise in sparse data on RM accuracy is not well understood. This paper addresses these challenges by formulating RM construction as a Bayesian inverse problem under coarse environmental knowledge and noisy sparse measurements. Although maximum a posteriori (MAP) filtering offers an optimal solution, it requires a precise prior distribution of the RM, which is typically unavailable. To solve this, we propose RadioDiff-Inverse, a diffusion-enhanced Bayesian inverse estimation framework that uses an unconditional generative diffusion model to learn the RM prior. This approach not only reconstructs the spatial distribution of wireless channel features but also enables environmental building outlines perception, just relying on pathloss, through integrated sensing and communication (ISAC). The proposed method operates on routine communication measurements, without new waveforms, specialized feedback, or protocol changes, thereby enabling a plug-and-play ISAC capability. Remarkably, RadioDiff-Inverse is training-free, leveraging a pre-trained model from Imagenet without task-specific fine-tuning, which significantly reduces the training cost of using a generative large model in wireless networks. Experimental results demonstrate that RadioDiff-Inverse achieves state-of-the-art performance in accuracy of RM construction and environmental reconstruction, and robustness against noisy sparse sampling.

Sixth generation (6G) systems require environment-aware communication, driven by native artificial intelligence (AI) and integrated sensing and communication (ISAC). Radio maps (RMs), providing spatially continuous channel information, are key enablers. However, generating high-fidelity RM ground truth via electromagnetic (EM) simulations is computationally intensive, motivating machine learning (ML)-based RM construction. The effectiveness of these data-driven methods depends on large-scale, high-quality training data. Current public datasets often focus on single-input single-output (SISO) and limited information, such as path loss, which is insufficient for advanced multi-input multi-output (MIMO) systems requiring detailed channel state information (CSI). To address this gap, this paper presents UrbanMIMOMap, a novel large-scale urban MIMO CSI dataset generated using high-precision ray tracing. UrbanMIMOMap offers comprehensive complex CSI matrices across a dense spatial grid, going beyond traditional path loss data. This rich CSI is vital for constructing high-fidelity RMs and serves as a fundamental resource for data-driven RM generation, including deep learning. We demonstrate the dataset’s utility through baseline performance evaluations of representative ML methods for RM construction. This work provides a crucial dataset and reference for research in high-precision RM generation, MIMO spatial performance, and ML for 6G environment awareness. The code and data for this work are available at: https://github.com/UNIC-Lab/UrbanMIMOMap.

Certificate revocation checking (CRC) is a fundamental component for securing certificates which has been widely deployed in satellite networks to support security-related services. However, directly utilizing existing CRC mechanisms in satellite networks would cause critical issues in terms of security, privacy, and practicality. Typically, the trustworthiness of checking results cannot be guaranteed in the presence of active adversaries; the certificate to be checked contains the satellite’s identity, which is sensitive in some applications but could be exposed during CRC; CRC cannot be trivially launched when the satellite is being under constrained networks (e.g., it enters dead zones where direct communication with base stations fails). In this paper, we propose a privacy-preserving and lightweight CRC scheme, dubbed SNCRC, for satellite networks, where a neighboring-assisted forwarding paradigm is utilized to support CRC in constrained networks. SNCRC is secure against adversaries who invalidate checking results or violate related sensitive information about the satellite, which is achieved by utilizing authenticated encryption with associated data (AEAD). Furthermore, SNCRC utilizes a 2-layer revocation checking protocol to perform lightweight CRC, where the certificate authority (CA) and base stations handle CRC tasks from satellites in a cooperative way, which frees CA from heavy costs and reduces CRC delay significantly. We analyze the security of SNCRC, implement an SNCRC prototype, and conduct a comprehensive performance evaluation, which demonstrates its security, efficiency, and practicality.

Overcoming the limitations of individual-vehicle line-of-sight (LOS) sensing holds significant importance for guaranteeing the safety of driving. With a wider perception field, vehicle-infrastructure (VI) collaborative perception can provide vehicles with more comprehensive perception assistance, which has received widespread attention in recent years. However, the perception data fusion between infrastructure and vehicles is still impeded by issues such as large data volume and complex processing procedures, constituting a threat to driving safety. To deal with these issues, this paper proposes a VI collaborative environment perception approach based on sparse bird's eye view (BEV) features. By leveraging the representation of sparse BEV, features can be fused within a unified perspective in a lightweight manner, thereby enhancing the efficiency of feature fusion and reducing redundancy. Additionally, we present a solution for processing the overlapping features between the EGO-vehicle and road side unit (RSU) by taking the union of the coordinate points. Finally, the applicable vehicle and RSU datasets are collected through Carla. The experimental results demonstrate that the proposed approach can effectively mitigate the limitations of individual-vehicle perception by compensating for occluded information and provide a more comprehensive perception field.

Currently, end-to-end autonomous driving systems that employ imitation learning effectively learn and optimize driving strategies by integrating the driving behaviors of human experts with modern deep learning techniques. However, challenges such as scene diversity, data security, and training time must still be addressed to develop a model that is applicable across various traffic scenarios while maintaining high accuracy. To tackle these issues, this paper proposes a personalized feder-ated imitation learning algorithm that aggregates locally trained imitation learning decision models from vehicles operating in different scenarios through a distributed training approach, thereby enhancing training efficiency and model accuracy. Specifically, considering the variability in communication link quality and potential disconnections caused by the mobility of vehicles in a connected vehicle network, we introduce a personalized user selection algorithm. Building on this foundation, we employ a federated imitation learning method to efficiently and rapidly train a driving decision model with comparable performance while safeguarding data privacy. Extensive simulation results confirm the superiority of the proposed algorithm in terms of training speed and model accuracy.

To fulfill future diverse user requirements, 6G networks are envisioned to provide everyone-centric customized services ubiquitously and precisely. However, the diversity in user requirements and the heterogeneity in network resources challenge conventional network operators in network management and service provision. In this article, we investigate the artificial intelligence (AI) service provision in the multilayer heterogeneous network. To provide ubiquitous intelligence to users with different computing requirements, an intelligence-native network architecture is designed. Based on the proposed architecture and the AI model stitching mechanism, we formulate the joint AI provision and access selection problem as a mixed integer nonlinear programming (MINLP) problem to maximize the average user satisfaction value and user satisfaction rate. Then, a heuristic solution based on Dung Beetle algorithm is proposed to optimize the AI model selection, AI service deployment, user access, and stitching coefficient jointly. Extensive simulations are conducted to evaluate the performance of our proposed architecture and algorithm.

Presents corrections to the paper, (Correction to “Toward Native Intelligence: An Efficient and Flexible AI Services Provision Scheme in Multilayer Heterogeneous Networks”).

Traffic flow prediction has emerged as a critical component in advancing smart cities. Nevertheless, precisely forecasting traffic flow remains a formidable challenge, attributable to the intricate and dynamic spatiotemporal interdependencies inherent within traffic data. Contemporary methodologies often overlook the different impacts exerted at each timestamp and treat temporal correlations, rendering them incapable of extracting temporal patterns at multiple scales. Furthermore, these approaches fail to account for the neighboring and functional relationships among nodes within the spatial module. In this work, we introduce an innovative Multiscale and Gated transFormer (MSGFormer) architecture, MSGFormer, to overcome the inherent limitations for accurate traffic flow estimation. We have introduced a multiscale sampling strategy wherein we sample from the original data at three scales: 1) recent; 2) daily; and 3) weekly. This approach enables the generation of corresponding subsequences that encapsulate temporal information across different granularities. Subsequently, each generated subsequence is projected into a latent space and systematically combined with positional, temporal, and spatial embeddings. The positional embedding comprises relative positional embedding and time stamp embedding corresponding to days and weeks, aiming at capturing the sequential and cyclical characteristics of the data. Furthermore, at the inception of the transformer encoder, a gated unit, composed of a neighboring mask and a functioning mask, is employed to capture both static and dynamic spatial correlations effectively. Comprehensive experiments have been conducted on four real-world traffic data sets. The experimental results robustly validate that our model attains significantly higher predictive accuracy in comparison to other baseline models.

Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.

Federated learning (FL) presents a novel paradigm for constructing global models by leveraging distributed client data while preserving privacy. Despite clients’ readiness to contribute computational resources via WiFi networks, the concurrent model uploads often trigger the competitive backoff mechanism inherent in the carrier sense multiple access with collision avoidance (CSMA/CA) protocol, which impairs the training efficiency and performance of FL. To address this challenge, this paper proposes an innovative sliding window-based asynchronous update approach for federated learning, named as FedSW. By properly configuring the sliding window size at the wireless access point (AP) of the WiFi network, FedSW orchestrates local training, model upload, aggregation, and distribution in harmony with the sliding window progress. This synchronization significantly improves training efficiency and model performance. Furthermore, the versatility of FedSW is demonstrated through its seamless integration with state-of-the-art (SOTA) algorithms. Our methodology is rigorously evaluated against FL benchmarks, showcasing its superior effectiveness. Simulation results confirm that FedSW consistently outperforms conventional benchmarks in terms of convergence, regardless of the sliding window size, while significantly reducing latency.

Due to the stringent latency requirements for computationally intensive rendering in virtual reality (VR) transmission and the limitations of computational resources on VR devices, extensive research has focused on task offloading with joint communication and computing resource scheduling to address these issues. Traditional model-based theoretical methods face challenges with long online processing times, while data-driven methods lack interpretability. This paper proposes a knowledge-driven rendering task offloading strategy for immersive wireless VR with mobile edge computing (MEC). The rendering approaches include local, MEC, and collaborative offloading between VR devices and MEC servers. First, we formulate an optimization problem to maximize user quality of experience (QoE), which is defined as the weighted sum of latency and video resolution. To solve the optimization problem, we propose a knowledge-driven belief propagation (KD-BP) algorithm where the structure of the BP algorithm is regarded as knowledge. Specifically, the operations with high computational complexity in the BP algorithm are replaced by a deep neural network, termed the knowledge-fused deep learning (DL) method. Finally, numerical results show that when the number of users reaches 10, the proposed KD-BP algorithm significantly reduces online processing latency and closely matches the convergence speed and performance compared to the BP algorithm.

In recent years, unmanned aerial vehicles (UAVs) have experienced rapid development and have been widely used in many fields. Equipped with both communication modules and sensing modules, UAVs are capable of conducting integrated communication and target detection, thus greatly improving spectrum efficiency and system performance. In this article, we consider a scenario where multiple UAVs collaborate to detect targets and transmit the collected data to a central UAV. Addressing the problem of communication and perception scheduling, we first analyze the target detection and communication performance, and then formulate the joint communication and perception scheduling problem as two optimization problems, with the objectives being maximizing the average utility function (MAUF) and minimizing the completion time (MCT), respectively. To solve the formulated problems, we first consider the dynamic characteristics of the environment, and model the problems as two Markov decision processes. Regarding the UAVs as multiple agents, we then propose a multiagent double deep Q-network (DDQN)-based MAUF algorithm and a multiagent DDQN-based MCT algorithm to determine the communication and perception scheduling strategies of the UAVs. Simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

This paper proposes a novel knowledge-driven approach for resource allocation in wireless networks using the graph neural network (GNN) architecture. To meet the millisecond-level timeliness and scalability required for the dynamic network environment, our proposed approach, named UWGNN, incorporates the deep unrolling of the weighted minimum mean square error (WMMSE) algorithm, referred to as domain knowledge, into GNN, thereby reducing computational delay and sample complexity while adapting to various data distributions. Specifically, by unrolling WMMSE algorithm into a series of interconnected submodules, UWGNN aligns closely with the optimization steps of the algorithm. Our analysis reveals the effectiveness of the deep unrolling method within UWGNN, which decomposes complicated end-to-end mappings, leading to a reduction in model complexity and parameter count. Experimental results demonstrate that UWGNN maintains optimal performance with computation latency 3 to 4 orders of magnitude lower than the WMMSE algorithm and exhibits strong performance and generalization across diverse data distributions and communication topologies without the need for retraining. Our findings contribute to the development of efficient and scalable wireless resource management solutions for distributed and dynamic networks with strict latency requirements.

In vehicular edge computing networks, the real-time, on-demand scheduling of scarce network resources for environmental perception, task offloading, computation, and feedback is vital. However, these coupled processes make resource allocation challenging. Moreover, existing real-time channel measurement techniques in complex vehicular topologies present load, accuracy, and customization difficulties. To address these issues, this paper proposes an on-demand environmental perception and resource allocation strategy. Specifically, with the introduction of a channel knowledge base, we first analyze the coupling relationship between environmental perception, communication, and computation. A model is then proposed for task offloading to schedule the granularity of environment perception, communication resources, and computational resources dynamically. Subsequently, the resource allocation problem is formulated as an optimization problem, aiming to minimize system processing delay and maximize resource utilization while ensuring perception accuracy. To address this, a two-phase optimization-assisted deep reinforcement learning (DRL) algorithm is proposed. The initial phase uses convex optimization to approximate a solution. The second phase proposes a DRL-based algorithm to intelligently schedule dynamic network resources, with the first phase’s solution guiding the initial exploration space to enhance DRL training efficiency. Extensive simulation experiments verify the effectiveness of our proposal.

Task offloading is a potential solution for computation-intensive vehicular applications due to limited on-board computing resources. However, traditional model-driven methods are hindered by long online processing time, while data-driven methods are deficient in interpretability and generalizability. To overcome this challenge, this paper formulates the resource allocation for task offloading in connected autonomous vehicles (CAVs) as a multi-objective optimization problem, and proposes a novel knowledge-driven algorithm that integrates both model-driven and data-driven methods. Specifically, the framework of a model-driven alternating minimization (AM) algorithm, which solves the formulated problem via alternatively optimizing power allocation subproblem and bandwidth and CPU frequency allocation subproblem, is regarded as knowledge. Inspired by such knowledge, our proposed knowledge-driven neural network consists of two long short term memory networks (LSTMs) to alternatively updating these two subproblems. Furthermore, to get away from the local optimum usually occurred in the AM algorithm, our proposed knowledge-driven neural network updates network parameters with the global loss function. Simulation results demonstrate that our method outperforms both the AM algorithm and the LSTM without knowledge.

In this paper, multi-antenna UAV-enabled joint communication and sensing scenario is examined. Taking into account the flight energy of the UAV, multi-antenna transmission and user service requirements are jointly considered, the problem of UAV communication, sensing precoding and flight trajectory is formulated as a multi-objective optimization problem which jointly maximizes the minimum data rate of communication users and the minimum discovery probability of targets. Since the minimum rate maximization problem of communication users is a non-convex optimization problem, which is difficult to solve directly, we decompose the original optimization problem into communication precoding design subproblem and UAV trajectory design subproblem. Then we solve the two subproblems successively by applying an alternate iteration method. Specifically, a zero-forcing (ZF) algorithm is put forward for solving the communication precoding design subproblem. A successive convex approximation (SCA) algorithm is applied to determine the optimal trajectory of the UAV. Based on the optimal trajectory of the UAV, the sensing positions selection problem is modeled as a weighted distance minimization problem, and then the extensive search algorithm is applied to obtain the optimal locations. Finally, a ZF algorithm-based joint communication and sensing precoding is presented. The effectiveness of the proposed algorithm is verified by simulations.

In recent years, unmanned aerial vehicles (UAVs) have been experienced rapid development and have been widely used in many fields. Equipped with both communication modules and sensing modules, UAVs are capable of achieving integrated communication and target detection, thus greatly improving spectrum efficiency and system performance. In this paper, we consider a scenario where multiple UAVs collaborate to detect targets and transmit the collected data to a central UAV. Addressing the problem of communication and perception scheduling, we first analyze the detection performance and communication performance, and then formulate the joint communication and perception scheduling problem into an average utility function maximization problem. To solve the formulated problem, we first consider the dynamic characteristics of the environment, and model the problem as a Markov decision process. Regarding the UAVs as multiple agents, we propose a multi-agent double deep Q-network based joint communication and perception scheduling algorithm. Simulation results demonstrate the effectiveness and superiority of the algorithm.

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at https://github.com/UNIC-Lab/LOGNN.

The rapid development of Internet of Things (IoT) technology can significantly promote the development and deployment of smart roads, enabling efficient and reliable road information sensing and analysis. As an important part of smart roads, timely and accurate detection of road cracks can improve service life of roads and reduce road management and operating costs. In this paper, we propose a vibration-sensor-based crack detection scheme for smart roads. In this scheme, by deploying the vibration sensor on the roadside, the changes in the vibration signals caused by the vehicle passing through the range of the sensor can be collected in real time. Then, considering that the seismic waves caused by vehicle driving are mostly distributed in the low-frequency range, we perform low-pass filtering on the collected vibration signals to retain the low-frequency vibration signals. After that, in order to distinguish the crack state of the road, we extract the vibration signal features of the normal road and the cracked road in the time domain, frequency domain and time-frequency domain, respectively. Based on the extracted features, we use logistic regression (LR), support vector machine (SVM) and random forest classification (RFC) machine learning algorithms to realize road crack detection. Finally, we conduct experiments to evaluate the performance of the proposed road crack detection scheme. The experimental results verify the high accuracy of the proposed scheme, and the accuracy of LR, SVM and RFC are 93.3%, 93.3% and 96.7%, respectively.

With the development of communication technology and virtual reality (VR) technology, virtual Metaverse services are gradually entering people’s lives to provide immersive experience. As one of the important travel tools for people, vehicles have the opportunity to become the carrier of Metaverse, thereby enhancing the driving experience and entertainment experience of vehicle users (VUs). However, due to the high-speed movement of vehicles, how to dynamically adapt to environmental changes to allocate transmission and computing resources so that VUs can better experience VR services in the Metaverse has become a challenge. To this end, in this paper, we propose an environment-aware dynamic resource allocation scheme for VR video services in vehicle Metaverse, aiming to efficiently allocate computing and communication resources to maximize the quality of experience (QoE) of VUs when requesting VR video services. Specifically, we first establish the system model which includes network model, communication model, and VR video model. Then, considering the dynamic changes in the driving environment, we design a QoE model for each VU based on its VR video buffer. After that, we design a deep deterministic policy gradient (DDPG) algorithm to optimally allocate communication and computing resources to maximize the QoE of each VU. The simulation results show that our scheme can bring the highest reward to the VUs compared with the benchmark schemes.

By offloading computation-intensive tasks of vehicles to roadside units (RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can relieve the onboard computation burden. However, existing model-based task offloading methods suffer from heavy computational complexity with the increase of vehicles and data-driven methods lack interpretability. To address these challenges, in this paper, we propose a knowledge-driven multi-agent reinforcement learning (KMARL) approach to reduce the latency of task offloading in cybertwin-enabled IoV. Specifically, in the considered scenario, the cybertwin serves as a communication agent for each vehicle to exchange information and make offloading decisions in the virtual space. To reduce the latency of task offloading, a KMARL approach is proposed to select the optimal offloading option for each vehicle, where graph neural networks are employed by leveraging domain knowledge concerning graph-structure communication topology and permutation invariance into neural networks. Numerical results show that our proposed KMARL yields higher rewards and demonstrates improved scalability compared with other methods, benefitting from the integration of domain knowledge.

In digital twins enabled space-air-ground integrated networks (DT-SAGINs), the DT of a vehicle (DT-V) needs to constantly migrate between the infrastructures deployed on the path of the vehicle as the vehicle moves to provide stable and continuous driving services for the vehicle. However, each DT-V has differentiated migration requirements and the heterogeneous network infrastructures have various migration performances. Therefore, how to design a scheme that jointly considers the above factors to determine the optimal migration strategy for each DT-V becomes a challenge. In this paper, we propose a game-based migration scheme for the DT-Vs in DT-SAGINs. In this scheme, we first design a two-layer DT migration architecture, where each vehicle has two DTs and each network infrastructure only has one DT. The two DTs of the vehicle are respectively deployed in the cloud layer (Primary DT-V) and the edge layer (Second DT-V). In contrast, the DT of each network infrastructure is deployed in the cloud layer (DT-I). Based on the designed architecture, the interaction of the Primary DT-Vs and the DT-Is deployed in the cloud layer is formulated as a matching game, where an integrated algorithm that couples bilateral matching and dynamic programming is designed to obtain the optimal migration strategy for each Second DT-V deployed in the edge layer to maximize its average utility. The simulation results show that the proposed scheme can lead to a higher utility for each Second DT-V than the conventional schemes.

Space-air-ground integrated networks (SAGIN) have been envisioned as the promising and key network architecture for the 6G vehicular networks to provide seamless coverage for the connected vehicles. To access the most appropriate network quickly, this paper proposed a knowledge-driven network access approach, where the communication knowledge is explicitly integrated into neural networks, to deal with multiple tasks in SAGIN-based vehicular networks. Specifically, the formulated long-term network access problem is handled by asynchronous advantage actor-critic algorithm (A3C) in reinforcement learning. During this process, the space-time correlation knowledge is introduced to effectively reduce the action space in channel selection and the reward shaping exploiting the problem-specific communication and mathematical knowledge is adopted to solve the sparse reward problem in reinforcement learning. In addition, by modifying the sub-net learning rate of the A3C algorithm with experimental experience, this paper speeds up the network convergence speed by 1.5%. Numerical results also show that integrating knowledge into traditional deep reinforcement learning algorithm can improve the reward by 4%.

The combination of digital twins (DT) and heterogeneous vehicular networks (HetVNets) can significantly enhance the resource integration capability and performance of the network. In DT-HetVNets, vehicles need to selectively synchronize the data to be updated or cached to their DTs deployed in the cloud for data interaction and decision-making. However, considering that vehicles have diversified data synchronization requirements and network infrastructures have differentiated access capabilities, how to formulate optimal network access strategies and resource pricing strategies for vehicles and network infrastructures becomes a key challenge in the data synchronization process. To this end, we propose a utility-based on-demand data synchronization scheme in DT-HetVNets. In this scheme, we first establish the DT model and communication model in DT-HetVNets. Then, we design the utility functions of the DTs of vehicles and infrastructures by comprehensively considering their requirements. According to the utility functions, we model the decision-making process between the DTs of vehicles and the DTs of infrastructures as a Stackelberg game, where an iterative algorithm is proposed to obtain the Stackelberg equilibrium. The simulation results show that our scheme can bring them the highest utilities compared with the traditional schemes.

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.

Unmanned aerial vehicles (UAVs) have been regarded as an efficient approach for collecting data in wireless sensor networks (WSNs), benefited from their mobility and flexibility. In this work, we investigate the data collection problem in UAV-assisted WSNs. In order to improve data collection efficiency, we first propose a multi-scenario parallel data collection scheme which allows data packets being transmitted through various modes/links simultaneously. Then, addressing the importance of completing data collection within a short time duration, we formulate a constrained optimization problem which minimizes the data collection time of the sensor nodes (SNs) by jointly designing UAV flight trajectory, cluster head mode selection, SN clustering strategy and UAV velocity. To resolve the optimization problem, we first consider the data transmission performance between SNs and present an SN clustering scheme based on a modified K-means algorithm. Given the clustering strategy, the optimization problem is then converted into three sub-problems, i.e., CH mode selection, UAV trajectory design, and flight velocity optimization. Firstly, jointly considering the data collection time of the cluster heads in various transmission modes and the spectrum resources of the sink node, we propose a greedy method-based CH mode selection scheme. Then, we map the UAV trajectory optimization problem as a traveling salesman problem and propose a simulated annealing-based algorithm to determine the flight trajectory for the UAV. Finally, by applying discrete time segment scheme, the UAV velocity optimization subproblem is transformed into a sequence of convex flight time minimization problems and a segment optimization-based flight velocity control strategy is presented. Numerical results reveal that the proposed data collection algorithm can achieve$25{\mathrm{\% }}$and$12{\mathrm{\% }}$performance gains comparing to the existing algorithms and the benchmark scheme, respectively.

Caching popular contents at the base station (BS) in maritime Internet of Things (IoT) networks makes sensor nodes be free from frequently responding to user requests, which can remarkably save the energy consumption of sensor nodes. However, to ensure the freshness of contents, cached contents need to be updated periodically. Frequent content updating can minimize the age of information (AoI) of contents while increase the energy consumption of sensor nodes. To make a better tradeoff between the AoI and energy consumption, in this paper, both the cache placement and content updating interval are jointly optimized to minimize the weighted sum of AoI of contents and energy consumption of sensor nodes. As the formulated problem is a mixed integer nonlinear programming problem, the cache placement and the content updating interval are alternatively optimized. For the cache placement problem, a local optimal solution is achieved via the binary constraint reformulation and successive convex approximation. For the content updating problem, the optimal solution with semi-closed form is derived. Simulation results show that our proposed algorithm outperforms other benchmarks in terms of the weighted sum of AoI and energy consumption.

Vehicle platooning is an effective way to improve the efficiency and safety of transportation systems, in which a group of vehicles maintains a moving pattern by minimizing the tracking error of each vehicle. In this paper, a joint optimization of radio resource allocation for kinetic status information transmission and platoon control is considered under resource-constrained conditions to maintain the targeted inter-vehicle spacing. The formulated problem is approximately solved by the decomposition method, where the radio resource allocation and the platoon control are considered alternatively in two stages. In the first stage, a tracking error based scheduling strategy is presented for radio resource allocation. In the second stage, the control inputs of each vehicle are optimized based on the model predictive control (MPC). Simulation results show that the proposed scheme can achieve the objective of platoon control while having a low tracking error compared with other scheduling strategies.

Unmanned aerial vehicles (UAVs) have emerged as a promising solution to provide wireless data access for ground users (GUs) in various applications. In this paper, we study UAV deployment problem in an integrated access and backhaul network, where a number of UAVs are deployed as aerial base stations (ABSs) or aerial relays (ARs) to forward GUs’ data packets to the remote gateway via multi-hop transmissions. Aiming at minimizing the system cost, which is defined as the weighted sum of UAV deployment cost and the energy consumption required for data transmission, a constrained system cost minimization problem is formulated, where UAV deployment, GU association and route selection problem are optimized. To solve the formulated non-convex problem, we propose a two-stage heuristic algorithm. In the first stage, we focus on the optimal design of the access links and propose a joint ABS deployment and resource allocation algorithm. Specifically, a modified K-means based clustering scheme is proposed to determine ABS deployment and GU association strategy. Given the obtained ABS deployment strategy, in the second stage, we then design a joint AR deployment, route selection scheme for the backhaul links and propose a minimum circle algorithm-based AR deployment and route selection strategy. Numerical results verify the effectiveness of the proposed algorithm.

Unmanned aerial vehicle (UAV) has been considered as an efficient solution to collect data from wireless sensor networks (WSNs) due to its flexibility and mobility. In this paper, we consider a UAV-assisted WSN, wherein the UAV starts from directly above the sink node to collect data and returns to the initial position. To improve data collection performance, sensor node (SN) can be clustered and the cluster head (CH) is responsible for collecting data of cluster members (CMs) and forwarding the data to the sink node. Two data forwarding modes are considered, i.e., the direct transmission mode from CH to the sink node, and the UAV collection mode from CH to UAV and then to the sink node. A data collection time minimization problem is formulated, where the clustering strategy, CH transmission mode selection, UAV trajectory and UAV velocity are jointly optimized. To solve this non-convex problem, a modified K-means algorithm-based clustering scheme is proposed firstly. Based on which, a CH transmission mode selection strategy is designed, and then a traveling salesman problem (TSP)-based UAV trajectory planning algorithm is proposed. Numerical results demonstrate the effectiveness of the proposed algorithm.

The maritime communication network is assembled by emergent network technologies. However, the adverse maritime environment impedes the efficiency of resources allocation in maritime communication network. Here we show a joint sleeping scheduling and opportunistic transmission scheme in delay-tolerant maritime wireless communication networks based on software defined networking (SDN) to find a better tradeoff between the energy consumption and the delay. Specifically, an energy-limited delay tolerant networking (DTN) node deployed in the ocean receives/transmits data from/to vessels within its communication range. To further save the energy, a long-term energy minimization problem is formulated with sleeping scheduling and opportunistic transmission. After that, a multi-objective minimization problem of energy and delay is first modeled by Lyapunov optimization (LO), which then is solved by convex optimization. Both mathematical analyses and simulation results demonstrate how the maritime communication network allocates satisfactorily with the proposed allocation scheme.

Accidents often occur at sea, so effective maritime search and rescue is essential. In the current process of sea search and rescue, the operation efficiency of large search and rescue equipment is low and it cannot provide stable communication link. In this article, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are used to form a cognitive mobile computing network for co-operative search and rescue, and reinforcement learning (RL) is used to plan search path and improve communication throughput. Based on the scene of marine search and rescue, the grid method is used to model the search and rescue area. Meanwhile, an intragroup communication architecture based on UAVs and USVs is designed to assist intragroup communication by recognizing the link channel state between UAVs. Search and rescue path planning is carried out through the strategy iteration of Markov decision process (MDP). Furthermore, distributed RL is used to recognize the channel state and perform mobile computing, so as to optimize the data throughput in the communication group. The simulation results show that we have successfully completed the path planning task. Compared with conventional methods, RL based on different reward functions has better throughput performance under the same number of UAVs auxiliary communications.

Fog computing can aggregate the computing resources to handle the unprecedented amounts of data and becomes a promising technology in the future 5G smart Internet of Things (IoT) networks. This paper considers an IoT video data analysis system where smart IoT cameras can transmit all data to the base station or analyze the data locally. After receiving smart cameras offloading data, the base station can partially redistribute the analyzing task to the smart user equipment. The smart cameras and base station task offloading scheme and the uplink-downlink bandwidth allocation are jointly optimized to minimize the system level delay. The problem is a mixed integer non-linear problem, and the objective function contains the sum of several segmented maximum, which makes it very challenging to solve. Firstly, the smart device 0-1 binary task offloading is relaxed into a continuous form, with adding an upper bound to guarantee the solution can be as close as possible to the integer. Then introduced by a change of variables in handling the segmented maximum, all non-convex constraints are transformed with slack variables and successive convex approximation. To further ensure the iteration algorithm convergence, the disciplined iteration algorithm is proposed to prevent the iteration from getting stuck. The simulation results verify that the assisted smart user equipment can reduce the system delay combining with the proposed resource allocation algorithm.

In this paper, we propose a destination-aided jamming scheme for secrecy simultaneous wireless information and power transfer (SWIPT) in cognitive relay networks. In which, an energy-constrained secondary transmitter (ST) assists to forward the traffic from a primary transmitter (PT) to a primary receiver (PR) and collaborates with PR-aided jamming to prevent the eavesdropper around PR from eavesdropping, in exchange for communicating with its own receiver in the same frequency. To maximize the rate of ST, we jointly design the relay processing matrix, beamforming vector and power split ratio under the constraint of PT secrecy rate demand. For tackling the non-convex problem, the semi-definite relaxation technique and Charnes-Cooper transformation are adopted. Simulation results demonstrate that our proposed scheme can significantly improve the performance of communications.

We investigate the max-min weighted downlink signal- to-interference ratio (SINR) problem under uplink SINR constraints and practical per-antenna constraints in full- duplex systems. The successive convex approximation (SCA) method is adopted to iteratively deal with this non-convex problem. Within each SCA iteration, to lower the complexity, a parallel beamforming algorithm based on alternating direction method of multipliers (ADMM) is proposed. Specifically, local variables are introduced to decompose the problem to multiple independent subproblems with closed-form solutions. Numerical results show that our proposed algorithm can achieve the similar performance with existing algorithms, but runs much faster especially in large-scale systems.

Pre-caching popular videos at the local storage of base stations (BSs) can significantly alleviate the tremendous backhaul burden. In this paper, we consider a cache-enabled cloud radio access network (C-RAN) scenario, where multiple BSs cooperatively serve multiple users. Each BS has a local storage and connects to the central processor (CP) via a backhaul link. Since multiple users may simultaneously submit the same request, the multicasting is also exploited to further offload the wireless traffic. The joint BS clustering and beamforming are optimized to maximize the weighted sum quality of experience (QoE) subject to the transmission power constraint and the backhaul capacity constraint. To solve this mixed-integer nonlinear programming, we first equivalently reformulate it as a sparse beamforming problem. Then, the reweighted ℓ1-norm technique is adopted to approximate the non-convex backhaul constraint and the successive convex approximation (SCA) method is applied to deal with the non-convex QoE objective. Simulation results show that cache strategies have great impact on the QoE performance and our proposed scheme significantly outperforms the traditional rate maximization scheme.

For the recent emerging applications such as augmented reality (AR), delay is a key performance evaluating the quality of user experience (QoE). Caching the execution results of the popular AR applications' computational tasks can significantly reduce the execution delay. In this paper, we consider the mobile edge computing (MEC) server and the cloud can proactively cache the execution results of computational tasks. Then, in our proposed scenario, there are four optional ways to process a task. They are, respectively, computing tasks locally, offloading tasks to the MEC server to computing, returning the task's computation results directly from the MEC server's cache, and returning the task's computation results from the cloud's cache. The computation offloading, resource allocation and task proactive caching are jointly optimized to minimize the execution latency subject to the constraints of the radio, computation and storage resources. To solve this complex mixed-integer nonlinear programming (MINLP) problem, we first propose a proactive caching algorithm for collaboration between the cloud and the MEC server to determine the task's caching status. Then, we propose a heuristic algorithm based on greedy strategy to solve the remaining problem, which includes resource allocation and the selections of task's execution mode. By analyzing the simulation results and comparing with an exhaustive algorithm, effectiveness and optimality of our proposed schemes are verified.

In this paper, we investigate the security for cognitive radio networks with simultaneous wireless information and power transfer (SWIPT). In such a system, an energy-limited secondary user (SU) helps relay the traffic from a primary user (PU) to the primary receiver (PR) and assists PU secure communication using beamforming technology, in return to serve its own secondary receiver in the same spectrum. In order to further enhance the security of PU traffic performance and increase the energy harvested by SU, we propose a destination-assisted scheme in which the PR transmits jamming signal to confuse the eavesdropper, while jamming signal can also be used to power SU. The beamforming vectors and power split ratio are jointly designed to maximize the secrecy rate of PU while satisfying the rate demand of SU. It boils down to a challenging non-convex problem. We resolve this issue by a general two-stage procedure. First, by fixing the power split ratio, we obtain the optimal beamforming vectors by applying the semi-definite relaxation (SDR) technique and the Charnes Cooper transformation. Then, the problem is solved by a one-dimension search to obtain the optimal power split ratio. Extensive simulations are provided and the results demonstrate that our proposed scheme has good performance.

Device‐to‐device (D2D) communication can increase network coverage, spectrum efficiency and energy efficiency (EE) within the existing cellular infrastructure, which makes it a promising architecture for the future networks. Due to the diversification of services, heterogeneous statistics quality‐of‐service constraints are considered in this study, where cellular users are concerned about the delay constraint and D2D user groups pay more attention to the outage probability of data transmission. The power allocation problem of cellular users can be solved by optimising the capacity‐payoff power‐loss game model. Upon exploiting Lagrange dual decomposition and the Newton iteration method, the power optimisation problem of cellular users is transformed into a parameter optimisation problem. Due to the limited energy resource of D2D users, EE is the focus of D2D user groups. Using fractional programming and convex optimisation techniques, energy‐efficient optimal power allocation algorithms of D2D users are proposed subject to the outage probability constraint. As a result, a power allocation algorithm based on hierarchical game is conceived for efficiently solving the power optimisation problem. The simulation results show that the proposed algorithm can obtain a performance improvement compared with other algorithm and converge within a certain number of iterations.

Emerging ultra-dense networks (UDN) can increase the network coverage and improve the overall throughput which makes it a promising network technology. However, the massive deployment of low power, small coverage micro base stations makes the traditional cell selection algorithm more complex and resource allocation less efficient. To solve these problems, this paper proposes a joint cell selection and hierarchical resource allocation algorithm. To improve the overall system performance, the proposed cell selection algorithm is executed according to the throughput of users. Meanwhile, a heuristic sub-channel allocation algorithm is proposed to improve the resource utilization. In addition, the different service requirements of mobile devices significantly increase the burden of power consumption. So the power allocation process takes into account the balance between the throughput and power consumption. Simulations demonstrate that the proposed hierarchical algorithm achieves a large performance improvement compared with the other algorithm in system throughput and energy efficiency (EE).

Cloud Radio Access Network (C-RAN) architecture with optical fiber fronthaul has been confirmed as a promising solution to achieve high capacity and low latency signal transmission, which has been a key technology and trend of the evolving fifth generation (5G) cellular networks. However, with the fiber fronthaul increasing, the complexity and cost of the CRAN fronthaul networks will grow exponentially. Accordingly, the hybrid fronthaul network of wireless and optical will be the direction of C-RAN architecture design in the future. In this paper, we study the wireless fronthaul C-RAN system in downlink and propose a robust precoder design. The channel state information (CSI) at the baseband unit (BBU) pool and remote radio head (RRH) cluster is assumed to be imperfect, where the additive channel state information error is modeled as Gaussian distributed. Based on this model, we propose a robust C-RAN precoder design that minimizes the total transmit power under a signal-to-interference-plus-noise ratio (SINR) constraint at each user terminal. The original goal is to establish SINR constrained power allocation formulations in the form of convex conic optimization problem. The analysis results reveal that the original problem formulation is non-convex, in general. We develop a novel conservative approximation scheme for handling the non-convex constraint. Furthermore, we solve the optimization problem by transforming it into a semidefinite program with relaxation, which can be efficiently solved. Simulation results show the advantage of using the proposed power-conserving robust precoding algorithm.

Ultra-dense networks (UDN) can increase the network coverage and improve the overall throughput which make it a promising network technology. However, traditional resource allocation algorithms are concerned with the improvement of the overall performance of the network. This paper considers the quality of service (QoS) and energy consumption of each femtocell and proposes a game-theoretic hierarchical resource allocation algorithm in UDN. Firstly, a modified clustering algorithm is performed. Then we transform this resource allocation problem to a two-stage Stackelberg game. In sub-channel resource allocation, we aim to maximize the throughput of the whole system by cluster heads (CHs). The power allocation takes account of the balance between QoS requirement and transmit power consumption. Simulation results show that the proposed algorithm can obtain a performance improvement compared with other algorithms.

A hybrid satellite-aerial-terrestrial system has been recently studied as a promising candidate to meet the urgent communication needs of emergency relief, and a good resource allocation policy is important to solve the contradiction between the sudden growth of victims' demand and the shortage of wireless resource in emergency situation. This paper addresses the joint relay selection and power allocation for an OFDMA-based hybrid satellite-aerial-terrestrial cooperative network, aiming at maximizing the energy efficiency (EE) with power constraints, quality of service (QoS) requirements and backhaul capacity. The optimization problem not only is a mixed 0-1 nonlinear program, but also contains a fractional objective function and a non-convex constraint condition. To tackle this complicated optimization problem, we firstly relax the binary variables and then transform the fractional objective function into a subtractive one. In each iteration, the power allocation solution and relay selection policy are approached via dual decomposition method. Simulation results illustrate the impact of total transmit power and backhaul capacity on EE and system capacity. What is more, relay selections highly enhance the system performance.

Proactive caching at the mobile network edge has been considered as a promising technology for enhancing users' Quality of Experience (QoE) and reducing redundant transmissions over the already overburdened cellular networks. The problem of video file caching in wireless Device-to-Device (D2D) communication networks, in which mobile users designated as helper users store popular video files and serve other requesting users via D2D localized transmissions, is studied in this paper. As personalized video recommendation systems are widely applied in video sites such as YouTube and Netflix, they cause mobile users' diversification and individuation in file preferences and users may make selfish caching decisions. Moreover, designing the file placement in caches is a task of hugely computational complexity due to the vast number of involved files and users. In this paper, we simultaneously cluster users and files into different interest groups and then propose a greedy intra-cluster caching scheme to greatly reduce its complexity. And we also compare the performance of each clustering algorithm while the file preferences matrix becomes high dimensional, sparse and highly asymmetric. Simulation results confirm that, with markedly reduced complexity, our proposed greedy caching scheme with spectral clustering using cosine similarity as the distance measure achieves near-optimal delay performance.

In this study, an energy harvesting (EH)‐based cooperative non‐orthogonal multiple access (NOMA) system is considered, where node S simultaneously sends independent signals to a stronger node R and a weaker node D. The authors focus on the scenario that the direct link between S and D is too weak to meet the quality of service (QoS) of D. Based on the NOMA principle, R, the stronger user, has prior knowledge about the information of the weaker user, D. To satisfy the targeted rate of D, R also serves as an EH decode‐and‐forward relay to forward the traffic from S to D. In the sense of equivalent cognitive radio concept, R viewed as a secondary user assists to boost D’s performance, in exchange for receiving its own information from S. Specifically, transmitter beamforming, power splitter and receiver filter are jointly designed to maximise R’s rate with the predefined QoS constraint of D and the power constraint of S. Since the problem is non‐convex, they propose an iterative approach to solve it. Moreover, a zero‐forcing based low‐complexity solution is also presented. Simulation results demonstrate that, both two proposed schemes have better performance than the direction transmission.

This study considers a multi‐pair massive multiple‐input multiple‐output two‐way relay network where the M ‐antenna relay simultaneously serves K pairs of single‐antenna users in the same time–frequency resource. For a more general case of Ricean fading channel, the authors propose a fixed‐gain maximum ratio combing/maximum ratio transmission relay scheme. The approximate expressions on the ergodic sum rate with such scheme are derived in two cases: (i) M , which is much larger than the users’ Ricean factors, is large enough; and (ii) if M is large enough but bounded, the Ricean factors of all the users go to infinity. Simulation results show that the approximate results are very tight. Based on the result for the first case, they further discuss the power‐scaling laws, which reveal that despite the Ricean fading channel and the interference at the relay, the achievable sum rate can still remain unchanged if the transmitted power at each user or at the relay is or both are made inversely proportional to M .

This letter considers secrecy simultaneous wireless information and power transfer (SWIPT) in full-duplex (FD) systems. In such a system, FD capable base station (FD-BS) is designed to transmit data to one downlink user and concurrently receive data from one uplink user, while one idle user harvests the radio-frequency (RF) signals' energy to extend its lifetime. Moreover, to prevent eavesdropping, artificial noise (AN) is exploited by FD-BS to degrade the channel of the idle user, as well as to provide energy supply to the idle user. To maximize the weighted sum of downlink secrecy rate and uplink secrecy rate, we jointly optimize the information covariance matrix, AN covariance matrix, and receiver vector, under the constraints of the sum transmission power of FD-BS and the minimum harvested energy of the idle user. Since the problem is nonconvex, the log-exponential reformulation and sequential parametric convex approximation (SPCA) method are used. Extensive simulation results are provided and demonstrate that our proposed FD scheme extremely outperforms the half-duplex scheme.