Unmanned aerial vehicle (UAV) communication is of crucial importance for heterogeneous practical wireless communications. However, it is susceptible to the severe spectrum scarcity with the rapidly expanding market of wireless broadband, multimedia users, and high data-rate applications. Exploring the underutilized unlicensed spectrum through spectrum sharing is promising to tackle this issue, but the openness of the unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a licensed and unlicensed UAV spectrum sharing network against uncertain jamming attack is studied. Moreover, to overcome the high complexity of the pure model-based optimization resource allocation schemes, the low learning efficiency and strong data dependency of data-driven deep reinforcement learning (DRL) methods, a novel optimization-driven DRL framework is proposed for the resource allocation. In particular, a model-based optimization module is exploited to derive the worst-case lower bound and a better informed target value of the formulated complex non-convex optimization problem. Furthermore, the model-based informed target value is integrated into the DRL to guide the agents for better strategies. Simulation results demonstrate that our proposed scheme can significantly improve the convergence speed and achieve a better reward performance than the pure DRL based scheme. It is also shown that the exploitation of the unlicensed spectrum can achieve approximately twice the sum transmission rate compared to using only the licensed spectrum.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, the densely populated users and diverse services with high data rate demands has triggered an increasing scarcity of UAV spectrum utilization. To tackle this problem, it is promising to incorporate the underutilized unlicensed spectrum with the licensed spectrum to boost network capacity. However, the openness of unlicensed spectrum makes UAVs susceptible to security threats from potential jammers. Therefore, a spectrum sharing UAV network coexisting with licensed cellular network and unlicensed Wi-Fi network is considered with the anti-jamming technique in this paper. The sum rate maximization of the secondary network is studied by jointly optimizing the transmit power, subchannel allocation, and UAV trajectory. We first decompose the challenging non-convex problem into two subproblems, 1) the joint power and subchannel allocation and 2) UAV trajectory design subproblems. A low-complexity iterative algorithm is proposed in a alternating optimization manner over these two subproblems to solve the formulated problem. Specifically, the Lagrange dual decomposition is exploited to jointly optimize the transmit power and subchannel allocation iteratively. Then, an efficient iterative algorithm capitalizing on successive convex approximation is designed to get a suboptimal solution for UAV trajectory. Simulation results demonstrate that our proposed algorithm can significantly improve the sum transmission rate compared with the benchmark schemes.

Integrating narrowband Internet of things (NB-IoT) into low earth orbit (LEO) satellite communications plays a promising role in serving massive devices and extensive coverage. In the receiver, the active user equipments (UEs) detection and timing synchronization in the random access procedure is essential but greatly challenged by the large frequency offset and long delay characterized by the LEO satellite. This paper presents the preamble allocation and receiver design of beam domain random access for NB-IoT integrated LEO satellite communications equipped with large-scale array antenna. Leveraging the inherent sparsity of the LEO satellite beam domain channel, we first design a UE grouping-based preamble allocation scheme to alleviate the collision by channel orthogonality for UEs simultaneously accessing. Building on the designed scheme, we propose a joint Doppler-beam domain active UE detection method that resorts to large frequency offset, which significantly improves the detection rate and obtains the coarse estimation of frequency offset. To achieve accurate timing synchronization, we formulate the timing offset estimation problem based on the maximum likelihood criterion. Following this, a modified and refined phase difference timing offset estimation algorithm is proposed, which employs the complete frequency hopping pattern of the preamble. To reduce the computational complexity, we propose an estimation algorithm possessing high performance with the prior information of estimated frequency offset. In comparison with the conventional method, the simulation demonstrates the superior performance of the proposed active UE detection and timing synchronization algorithms.

In wireless communications, unauthorized broadcasting within licensed spectrum bands disrupts legitimate signals and interferes with adjacent frequencies, risking critical systems. Existing methods for detecting unauthorized broadcasting often underutilize known signal data, reducing their effectiveness in dynamic, open-set scenarios. To address this, we propose a novel framework combining a temporal convolutional autoencoder (TCAE) with boundary-guided support vector data description (BGSVDD) for accurate detection of unauthorized signals in the radio frequency spectrum. The TCAE captures temporal signal features effectively with an adaptive temporal convolutional network (ATCN), while the BGSVDD uses a small set of known unauthorized samples to create robust decision boundaries with our proposed adaptive misclassification penalty (AMP) loss. Moreover, a global-local support vector (GLSV) strategy enables efficient online model updates, maintaining detection performance in evolving wireless environments with minimal resource overhead. Experiments with real-world broadcast signals show our method outperforms state-of-the-art techniques, especially under challenging interference conditions. Tests on public datasets further confirm its strong generalization across diverse spectrum protection applications.

Spectrum sharing unmanned aerial vehicle (UAV) network is a promising technology for future communication systems to mitigate the spectrum scarcity problem. However, the future sixth-generation large-scale wireless communication networks are expected not only to provide a high data rate for massive numbers of users but also to meet their stringent service requirements. Particularly in dynamic spectrum sharing UAV networks, the coupling of multi-dimensional resources and diverse user demands make the efficient and real-time resource allocation exceptionally challenging. A partially observable deep multi-agent active inference (PODMAI) framework is proposed to tackle these issues. The variational free energy is minimized to update the policy exploiting the belief based learning method. A decentralized training and execution multi-agent strategy is designed to navigate the challenges posed by partially observable information. To further satisfy the dynamic user demand and supplement partial observations, a joint spatial-temporal-attention prediction network is designed to construct the demand prediction enhanced PODMAI framework for resource allocation. Exploiting the established framework, an intelligent spectrum allocation and trajectory optimization scheme is elaborated for a spectrum sharing UAV network with multi-modal dynamic transmission rate demands. Simulation results demonstrate that our proposed scheme outperforms benchmark schemes in terms of the network sum transmission rate. Additionally, our proposed scheme exhibits faster convergence compared to the conventional reinforcement learning. Overall, our proposed framework can enrich intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Air-ground integrated network is able to make up for the limitations of small coverage as well as fixed resource deployment of ground 5G network and provide flexible services via edge computing. However, duplicated data may be offloaded to edge server, resulting in waste of network resources. In the meantime, due to the limited on-board energy storage of unmanned aerial vehicle (UAV), the endurance and trajectory optimization of UAV need to be focused on. Moreover, the high dynamics of network nodes will lead to the dilemma of information uncertainty and curse of dimensionality. In order to tackle the above challenges, we design a UAV-assisted heterogeneous cache-computing network model, among which, devices are divided into two categories: the request ground device (RGD) which requires task offloading and the free ground device (FGD) which can process offloaded tasks from the RGD through the device-to-device (D2D) link. Then we formulate a problem of jointly optimizing cache strategy, task segmentation, computing resource allocation and UAV trajectory planning to minimize the system latency constrained by UAV energy recharging. Due to the coexistence of discrete and continuous variables as well as coupling between long-term energy constraint and short-term decision making, we decompose it into two sub-problems. Low complexity matching algorithm is used to select the optimal UAV task caching strategy, while Lyapunov optimization is leveraged to decompose the second sub-problem, for which CVX is utilized to solve the task segmentation and local computing resource allocation, and soft actor-critic (SAC) approach is used to realize the computing resource allocation of FGD and UAV plus trajectory planning. Extensive simulation results showcase the advantages of our algorithm in reducing the system latency compared to benchmarks.

Unmanned aerial vehicles (UAVs) are widely used for object detection. However, the existing UAV-based object detection systems are subject to severe challenges, namely, their limited computation, energy and communication resources, which limits the achievable detection performance. To overcome these challenges, a UAV cognitive semantic communication system is proposed by exploiting a knowledge graph. Moreover, we design a multi-scale codec for semantic compression to reduce data transmission volume while guaranteeing detection performance. Considering the complexity and dynamicity of UAV communication scenarios, a signal-to-noise ratio (SNR) adaptive module with robust channel adaptation capability is introduced. Furthermore, an object detection scheme is proposed by exploiting the knowledge graph to overcome channel noise interference and compression distortion. Simulation results conducted on the practical aerial image dataset demonstrate that our proposed semantic communication system outperforms benchmark systems in terms of detection accuracy, communication robustness, and computation efficiency, especially in dealing with low bandwidth compression ratios and low SNR regimes.

Spectrum maps reflect the utilization and distribution of spectrum resources in the electromagnetic environment, serving as an effective approach to support spectrum management. However, the construction of spectrum maps in urban environments is challenging because of high-density connection and complex terrain. Moreover, the existing spectrum map construction methods are typically applied to a fixed frequency, which cannot cover the entire frequency band. To address the aforementioned challenges, a UNet-based data-and-semantic dual-driven method is proposed by introducing the semantic knowledge of binary city maps and binary sampling location maps to enhance the accuracy of spectrum map construction in complex urban environments with dense communications. Moreover, a joint frequency-space reasoning model is exploited to capture the correlation of spectrum data in terms of space and frequency, enabling the realization of complete spectrum map construction without sampling all frequencies of spectrum data. The simulation results demonstrate that the proposed method can infer the spectrum utilization status of missing frequencies and improve the completeness of the spectrum map construction. Furthermore, the accuracy of spectrum map construction achieved by the proposed data-and-semantic dual-driven method outperforms the benchmark schemes, especially in scenarios with low sampling density.

Fake news detection is of crucial importance and has received great attention. However, the existing fake news detection methods rarely consider the news release time, which limits the achievable detection performance, especially for detecting the instant fake news clusters that have sudden and aggregated characteristics. To tackle this issue, a temporal enhanced multimodal graph neural networks (TEMGNNs) method is proposed. The multimodal graph with semantic complementary enhancement is developed by feature aggregation of textual information, image information, and external knowledge. Moreover, the associations among different modalities are obtained by using the graph attention networks and the weights of each modality are adaptively learned. Furthermore, the aggregation of news with adjacent time and the same topic to form a temporal news cluster and learning temporal features for fake new detection by using our proposed graph neural networks. Extensive experiments results obtained on two public datasets demonstrate that our proposed method has the best performance compared with the benchmark methods. It is also shown that the exploitation of the temporal information and multimodal information benefits for fake news detection.

Recommendation systems are of crucial importance due to their wide applications. Knowledge graph (KG) enabled recommendation schemes have attracted great attention due to their superior performance and interpretability. However, the rich social information is not exploited for those systems, which limits the recommendation performance . In this paper, a novel explainable recommendation scheme is proposed by exploiting our designed social enhanced knowledge graph attention network (SKGAN). The hidden relations among users and items are learned and used for recommendation with the collaborative KG (CKG) and the user social graph (USG). Moreover, the high-order semantic information in both CKG and USG are obtained by using the graph convolution networks (GCNs) and the node level attention algorithm. Furthermore, a graph level user-specific attention algorithm is proposed to capture the user personalized preference between CKG and USG. Extensive experiment results demonstrate that normalized discounted cumulative gain (NDCG), precision, recall and hits ratio (HR) achieved with our proposed recommendation system are the best among those obtained with the state-of-the-art benchmark recommendation systems.

Automatic modulation classification (AMC) is of crucial importance in the sixth generation wireless communication networks. Deep learning (DL)-based AMC schemes have attracted extensive attention due to their superior accuracy compared with the conventional methods. However, a pure data-driven DL method relies on a large amount of labeled training samples and the classification accuracy is poor, especially in the low signal-to-noise ratio (SNR). In order to tackle this problem, two data-and-knowledge dual-driven AMC schemes are designed. A novel data and semantic knowledge driven AMC scheme is proposed by exploiting the semantic attribute information of different modulations. Moreover, a prior knowledge driven multi-task learning visual model is established to improve the classification performance in low SNR. Furthermore, another novel data and multi-domain knowledge joint driven AMC scheme is proposed by using the semantic attribute knowledge and the prior knowledge based multi-task learning visual model. Extensive simulation results demonstrate that our proposed data-and-knowledge dual-driven AMC schemes achieve the best performance compared with the benchmark schemes in terms of classification accuracy. Moreover, it is shown that the expert knowledge spawns for AMC accuracy improvement and a decrease in the required number of training samples.

Unmanned aerial vehicle (UAV) communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, it is susceptible to the severe spectrum scarcity and interference issues since it operates in the unlicensed frequency band. To tackle those issues, a dynamic spectrum sharing UAV network adopting an anti-jamming technique is considered. Two intelligent spectrum allocation and trajectory optimization schemes are designed, capitalizing on the proposed external interaction and internal inference based frameworks. For the first scheme, a novel external interaction based hybrid online-offline multi-agent actor-critic and deep deterministic policy gradient (MA2C-DDPG) framework is proposed taking into account the hybrid characteristics of discrete spectrum allocation and continuous UAV trajectory. As for the second scheme, another novel framework, the deep active inference (DAI) based on internal inference is proposed, which minimizes the internal variational free energy. Moreover, a belief learning based method is exploited to enhance the agents’ perception and improve the action selection in the dynamic spectrum sharing environment. Extensive simulation results demonstrate the high efficiency of our proposed schemes. It is shown that our proposed schemes significantly improve the secondary network sum transmission rate compared to various benchmark schemes. Moreover, the proposed MA2C-DDPG and DAI frameworks demonstrate the advantages in improving the training stability and convergence speed.

Through deploying satellites and unmanned aerial vehicles (UAVs) with onboard processing capability, the space-air-ground edge computing network (SAGECN) is poised to support ubiquitous access and computation offloading for Internet of Things (IoT) terminals deployed in remote areas. However, the current SAGECN faces several challenges in realizing its full potential, such as scarce spectrum resources, diverse computational demands, and dynamic network circumstances. To meet these challenges, we propose a cluster-non-orthogonal multiple access (C-NOMA)-enabled SAGECN model, where a satellite and multiple UAVs act as collaborative edge servers to execute tasks from IoT terminals. Since each offloaded task should be processed via a specific program, the edge servers carry out program caching, whilst transfer the tasks that do not match the cached programs to another server in a multi-hop manner. Considering the delay-sensitive requirements of computation tasks, we formulate a joint task offloading, communication-computation-cache resource assignment, and routing plan problem, aimed at minimizing the average system latency. To cope with this challenging issue, we partition it into three subproblems. First, a multi-agent learning-based approach is developed to collaboratively train the task offloading, flight trajectory, and program caching. As a step further, two optimization subroutines are embedded to perform routing plan, subchannel allocation, and power control, thereby rendering the overall solution. Experimental results reveal that our approach achieves outstanding performance in terms of system delay and spectrum efficiency.

This paper focuses on energy efficiency (EE) pre-coding design and computational-efficient precoding updating strategy for low earth orbit (LEO) satellite communications. Firstly, we formulate the EE precoding problem, which aims to maximize the EE metric under the quality of service (QoS) constraint and per-antenna power constraint (PAPC). By intro-ducing semidefinite relaxation, first-order Taylor approximation, and quadratic transformation, the problem is transferred into a convex one that can be efficiently solved. Moreover, due to the continuous movement of LEO satellites, precoding is performed frequently to maintain the high EE performance, leading to high computational complexity. Consequently, we consider prolonging precoding intervals to reduce complexity while alleviating severe performance degradation during the intervals. To this end, a computational-efficient beam direction change (BDC) algorithm is proposed to update pre coding vectors, which makes the main lobes of beams always point toward users. Furthermore, an adaptive method is proposed to adjust the precoding interval flexibly. Simulation results have indicated the effectiveness of the EE precoding algorithm and the BDC algorithm.

Resource allocation is of crucial importance in wireless communications. However, it is extremely challenging to design efficient resource allocation schemes for future wireless communication networks since the formulated resource allocation problems are generally non-convex and consist of various coupled variables. Moreover, the dynamic changes of practical wireless communication environment and user service requirements thirst for efficient real-time resource allocation. To tackle these issues, a novel partially observable deep multi-agent active inference (PODMAI) framework is proposed for realizing intelligent resource allocation. A belief based learning method is exploited for updating the policy by minimizing the variational free energy. A decentralized training with a decentralized execution multi-agent strategy is designed to overcome the limitations of the partially observable state information. Exploited the proposed framework, an intelligent spectrum allocation and trajectory optimization scheme is developed for a spectrum sharing unmanned aerial vehicle (UAV) network with dynamic transmission rate requirements as an example. Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes. Moreover, the convergence speed of the proposed PODMAI is significantly improved compared with the conventional reinforcement learning framework. Overall, our proposed framework can enrich the intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.

Abstract With the increasing number of user terminals (UTs), the interference among UTs might significantly decrease the throughput of the low earth orbit satellite communication system. In this paper, the user scheduling method is investigated to suppress user interference. Specifically, leveraging the strong spatial directivity of satellite channels, a low‐complexity angle‐based orthogonal user selection (AOUS) algorithm is proposed, which selects UTs with nearly orthogonal channels via angle information of UTs. A rate‐based proportionally fair (PF)‐AOUS algorithm is further proposed to ensure fairness among UTs, which combines the AOUS with the PF criterion. To reduce complexity, an improved angle‐based PF‐AOUS algorithm that schedules UTs according to their pitch angles rather than their rates is proposed. In addition, efficient precoding schemes for orthogonal UTs are designed by combining the steering vector and power allocation matrix, and it is shown that precoding can be converted into power allocation problems that further balance fairness and throughput. The numerical results indicate that the AOUS achieves a near‐optimal sum rate performance, and the angle‐based PF‐AOUS has the similar performance to the rate‐based PF‐AOUS, which achieves a high fairness index with the proposed precoding scheme.

Automatic modulation classification is of crucial importance in wireless communication networks. Deep learning based automatic modulation classification schemes have attracted extensive attention due to the superior accuracy. However, the data-driven method relies on a large amount of training samples and the classification accuracy is poor in the low signal-to-noise radio (SNR). In order to tackle these problems, a novel data-and-knowledge dual-driven automatic modulation classification scheme based on radio frequency machine learning is proposed by exploiting the attribute features of different modulations. The visual model is utilized to extract visual features. The attribute learning model is used to learn the attribute semantic representations. The transformation model is proposed to convert the attribute representation into the visual space. Extensive simulation results demonstrate that our proposed automatic modulation classification scheme can achieve better performance than the benchmark schemes in terms of the classification accuracy, especially in the low SNR. Moreover, the confusion among high-order modulations is reduced by using our proposed scheme compared with other traditional schemes.

Spectrum prediction is of crucial importance for realizing the cognitive Internet of Things to tackle the spectrum scarcity problem. Deep-learning-based spectrum prediction methods have attracted extensive attention due to their superior accuracy. However, the training speed of deep networks is low and the architecture of traditional networks is uninterpretable. In order to tackle these problems, a radio frequency machine-learning-driven spectrum prediction scheme is proposed by exploiting a novel model-enabled autoregressive (AR) network. A cell with only two parameters is exploited in each layer of the AR, which accelerates the network training. Moreover, the domain knowledge of the AR structure enables our proposed scheme to be explainable. Simulation results show that our proposed scheme has the best prediction accuracy than the long short-term memory (LSTM)-based scheme and the AR scheme. It is also shown that its convergence speed is higher than that of the LSTM-based scheme.

The development of an integrated satellite-terrestrial communication network has become one of the focuses in both academic and industry in order to provide genuine seamless coverage. For the integrated satellite and terrestrial 5G commu-nication systems, positioning information of user terminals (UTs) can be beneficial in addressing several challenges. In this paper, we propose to utilize 5G new radio synchronization signals to perform positioning. To simultaneously guarantee synchronization and positioning performances for UTs in any place of a cell coverage, we investigate the precoding design at the satellite side for joint synchronization and positioning (JSP) in 5G integrated satellite-terrestrial networks. By considering the missed detection probabilities and angle of departure estimation for the UTs, we provide the precoding design criteria for synchronization and positioning, respectively. Then we introduce the constraint of equal transmit power on every antenna. Based on the criteria and constraint, we formulate the optimization problem for JSP and exploit the conjugate gradient algorithm under the manifold op-timization framework to design the precoder. Simulation results show that the proposed precoder can ensure that JSP achieves satisfactory performances within the whole cell coverage.

In this paper, a land mobile satellite (LMS) multiple-input multiple-output (MIMO) is considered, where two satellites simultaneously communicate with a mobile user terminal (UT). Spatial degree of freedom brought by the two satellites is introduced in the channel modeling, aside of other channel parameters including time correlation, shadowing, multipath fading and Doppler effect. Then an algorithm table using Markov multiple-state transition is provided to generate the LMS MIMO channels. Based on the modeled LMS MIMO channels, signal transmission between two satellites and the UT using space-time block coding is considered. Simulation results show that compared to the single satellite communications, the dual-satellite MIMO communications can achieve better bit error rate performance under the same signal-to-noise-ratio condition. In particular, the performance of dual-satellite single-polarization communications is slightly worse than that of single-satellite dual-polarization communications, since the spatial correlation is stronger than the polarization correlation.

Integrated satellite-terrestrial communications networks aim to exploit both the satellite and the ground mobile communications and thus provide genuine ubiquitous coverage. For 5G integrated low earth orbit (LEO) satellite communication (SatCom) systems, the timing advance (TA) is required to be estimated in the initial random access procedure of communications in order to facilitate the uplink frame alignment among different users. However, due to the inherent characteristics of LEO SatCom systems, the existing 5G terrestrial uplink TA scheme is not applicable in the satellite networks. In this paper, we investigate location-based TA estimation for 5G integrated LEO SatCom systems. We propose to take the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements obtained in the downlink timing and frequency synchronization phase for geographical location estimation, which are made from the satellite at different time instants. The location estimation is then formulated as a quadratic optimization problem. We propose an approximation method based on iteratively performing a linearization procedure on the quadratic equality constraints to solve this problem. Numerical results show that the proposed method can effectively assure uplink frame alignment among different users in typical LEO SatCom systems.

In vehicle‐to‐vehicle (V2V) communications, achieving reliable physical layer performance is a challenging task due to the highly dynamic nature of V2V propagation channels. Frame timing estimation, as one of the most critical signal processing procedures that rely on channel statistics, has to be appropriately enhanced to tackle this challenge. This study presents a novel frame timing estimation scheme based on both the available periodical preambles in IEEE 802.11p standard. By designing the fourth‐order statistics‐based correlation and differential normalisation functions, the proposed timing metric not only is capable of possessing an extensible correlation length, but also achieves the robustness to multipath effect and large carrier frequency offset. From the standpoints of hypothesis testing and classification, the proposed approach can effectively increase the distinction between correct and wrong timing indexes in terms of the class‐separability criteria, and consequently has a significantly improved timing estimation performance compared with the existing methods. Simulation results consist with theoretical analysis under the typical V2V channel model, and demonstrate that the proposed method can significantly reduce both the probabilities of false alarm and missed detection, and make the selection of a suitable threshold for frame detection much easier.

Reasonable design and effective detection of the random access preamble has become a challenging task due to the unique characteristics of mobile satellite communications. To tackle this challenge, we first design a universal long sequence structure by concatenating multiple short Zadoff-Chu sequences that are insensitive to carrier frequency offset (CFO), and then propose the new principles of parameter selection for short sequences to ensure the minimum utilization of root sequence and the independence of the cyclic shift offset on the beam radius. To further reduce the detection complexity and improve the multi-user access performance, a fast timing detection approach is also presented by leveraging the piecewise cumulative detection and the multi-peaks joint estimation to obtain an accurate timing advance for each access user. Simulation results and complexity analysis validate the effectiveness of the new preamble in a typical satellite communication environment, and reveal that the proposed timing detection can achieve the robustness to CFO and offer outstanding performance improvements especially in multi-user scenarios while having a notably reduced computational complexity.

This study investigates the problem of frame timing estimation in orthogonal frequency division multiplexing systems. Conventional timing estimation methods, which take advantage of the correlation property of a given preamble, always experience performance degradation in multipath fading channels with severe channel dispersion. To achieve accurate timing estimation, the authors propose a robust threshold‐based timing detection method independent of the preamble structure. Based on the autocorrelation and cross‐correlation, a novel timing metric with an extended correlation length is proposed to mitigate noise and resist large carrier frequency offsets. Due to the superior statistical property of the proposed timing metric, the threshold can be easily determined with no need for the process of noise variance estimation. Simulation results under different multipath fading channels demonstrate that the proposed method achieves a remarkably improved timing accuracy compared to the existing methods.