This paper investigates covert communications in an uncrewed aerial vehicle (UAV) assisted cellular system in the presence of a ground eavesdropper(Eav). To enhance the secrecy performance of a secure user (SU) while guaranteeing the quality-of-service (QoS) requirements of common users, a dual-UAV cooperative non-orthogonal multiple access (NOMA) framework with a two-slot transmission scheme, referred to as Spatio-Temporal-Energy Domain Cooperative NOMA (STEDC-NOMA), is proposed. In the considered architecture, an aerial base station (UAV-BS) and an aerial relay (UAV-Relay) collaboratively serve ground users, where the transmission is divided into two time slots and different common users are paired with the SU in each slot for NOMA superposition. By jointly exploiting temporal-domain two-slot cooperation, spatio-domain dual-UAV diversity, and energy-domain NOMA superposition, additional degrees of freedom are introduced for covert communication with fairer access. Based on this framework, a joint optimization problem of user pairing and resource allocation is formulated to maximize the overall secrecy capacity of the SU under the QoS constraints of common users. To support the proposed scheduling strategy, closed-form power allocation solutions are derived for the two slot respectively. Numerical results demonstrate that, compared with the previous benchmark schemes, the proposed STEDC-NOMA significantly improves the secrecy performance while satisfying the QoS demands and enhancing the access fairness of all users.

As a key paradigm in sixth-generation (6G) systems for enabling physical–virtual mapping and intelligent network management, Digital Twin (DT) networks demand high-precision, low-latency modeling and continuous updates of wireless environments. However, in dynamic urban scenarios, channel conditions are highly non-stationary, and traditional sensing schemes suffer from limitations in coverage, information timeliness, and scheduling efficiency. To address these challenges, this paper proposes an Integrated Communication-Computing-Intelligence-Controlling (ICCIC) network based on layered unmanned aerial vehicles (UAVs). The system comprises mobile edge servers (ESs) with trajectory control and scheduling capabilities, and statically deployed working UAVs responsible for wireless channel measurements. The collected data is offloaded to ESs for edge computing and local model construction, which supports subsequent DT channel modeling. A multi-objective optimization problem is formulated to jointly schedule UAV trajectories and data offloading, with Age of Information (AoI) and service delay as performance metrics. The optimization is subject to constraints including maximum task execution time and service fairness. To capture multi-type interactions among UAVs during sensing and coordination, we build a multi-relational heterogeneous graph and encode it with a graph neural network (GNN) to obtain structured system states. On this basis, we adopt a QMIX-based multi-agent reinforcement learning (MARL) framework under centralized training and decentralized execution (CTDE) is developed to learn cooperative scheduling policies. Simulation results demonstrate that the proposed method achieves superior performance in sensing timeliness, scheduling responsiveness, and policy convergence, providing robust support for efficient and reliable DT channel modeling.

The convergence of communication, sensing, and computing capabilities is a key trend in future 6th generation mobile (6 G) networks. Integrating unmanned aerial vehicles (UAVs) with federated learning can further enhance network performance in these areas while reducing resource overhead and protecting data privacy. However, due to limited spectrum resources and data heterogeneity, lack of client scheduling not only increases bandwidth pressure but also degrades training performance. Moreover, incentive allocation in federated learning directly influences whether UAVs accept client selection and participate in collaborative learning tasks. In order to solve the above problems, this paper designs an incentive assignment scheme for UAV clients in federated intelligent inspection based on communication-sensing-computing integration. This scheme comprehensively considers two dimensional metrics, client data quality and contribution value, for UAV incentive allocation and selection, hence abbreviated as the Multi Dimensional Scheme (MDS). MDS accounts for the communication, sensing, and computational energy consumption of UAVs, establishing a federated learning candidate pool through contract theory. Subsequently, UAVs that contribute more to model training are selected from the candidate pool via Bayesian optimization. Experiments conducted on multiple datasets show that, compared to existing methods, MDS effectively improves the accuracy of model training while reducing incentive costs.

In Industrial Internet of Things (IIoT), timely detection of equipment failures and predictive maintenance are crucial. Leveraging Federated Learning (FL) allows for distributed model training on inspection devices, enabling predictive maintenance without compromising data privacy. However, Traditional FL faces communication and scalability challenges in large scale industrial scenarios. While hierarchical federated learning (HFL) improves flexibility, it struggles in signal-unstable scenarios. This paper proposes a UAV-assisted HFL framework for distributed remote inspection in IIoT, where UAVs enhance communication via high-altitude links and act as edge servers to collect and aggregate model parameters, reducing the central server’s communication burden and improving training efficiency. In this framework, energy-constrained edge clients face challenges of energy efficiency and data silos, while UAV deployment and energy limitations must also be addressed. To optimize fair and energy-saving training, we formulate an optimization problem to minimize energy consumption based on communication and training costs. This is decomposed into two sub-problems: (1) client selection, tackled as a multi-objective optimization using a MAB-based algorithm with a customized reward function balancing energy use and fairness; (2) UAV scheduling, addressed with a heuristic algorithm to optimize edge server deployment. Combining these schemes enables efficient scheduling for large-scale IIoT inspections. Finally, simulation experiments demonstrate the proposed strategy’s significant advantages in reducing system energy consumption, enhancing model accuracy, and improving fairness.

Fault prediction is an important application in the Industrial Internet of Things (IIoT) to ensure the safety of industrial systems and factories. Currently, deep learning-based fault prediction models are more popular, and multi-factory co-operation is required to improve the accuracy and generality of fault prediction models. Federated learning can coordinate multiple clients to train models together while protecting client privacy, and thus is widely used for training fault prediction models. How to incentivise more factories to participate in model training is crucial, however, most of the existing incentive mechanisms focus on the problem of fair measurement of client contributions and ignore the problem of incentive allocation in scenarios with limited incentive budgets. In this paper, we design a self-safeguarding incentive mechanism for large scale-federated industrial maintenance based on double layer reinforcement learning, known as Dual Layer Incentive (DLI). The method enables the central server to achieve higher model training accuracy within a limited incentive budget through rational allocation of incentives, which ultimately reduces the overall cost of model training. In addition, we categorise participating clients into “diligent clients”“, inefficient clients” and “malicious clients” based on their contributions and design tailor-made incentives for each client type, which saves training costs and enhances the safety of the model training process. Finally, the proposed approach is evaluated through experiments using various datasets. The results show that the method significantly improves the accuracy and safety of industrial fault prediction model compared to other existing methods.

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks.

Scenario identification plays a crucial role in enhancing the performance of vehicle-to-vehicle (V2V) communication systems. It enables smart vehicles to adjust driving speed in allowable range according to the surrounding circumstance automatically and avoid possible crashes. However, existing methods for scenario identification in vehicular networks have cumbersome processing of information sequence and huge energy consumption. This paper proposes a novel scenario identification method using joint multislice and cooperative detection aided residual network (Resnet), which can extract features from non-equalized signal at the receiver (Rx. signal) automatically and realize scenario recognition. Simulation results demonstrate that the proposed Resnet-based scenario identification method can achieve high classification accuracy with small model size.

Nonorthogonal multiple access (NOMA) significantly improves the connectivity opportunities and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless communications. Meanwhile, emerging B5G services demand for higher SE in the NOMA-based wireless communications. However, traditional ground-to-ground (G2G) communications are hard to satisfy these demands, especially for the cellular uplinks. To solve these challenges, this article proposes a multiple unmanned-aerial-vehicles (UAVs)-aided uplink NOMA method. In detail, multiple hovering UAVs relay data for a half of ground users (GUs) and share the spectrums with the other GUs that communicate with the base station (BS) directly. Furthermore, this article proposes a K-means clustering-based UAV deployment scheme and location-based user pairing (UP) scheme to optimize the transceiver association for the multiple UAVs-aided NOMA uplinks. Finally, a sum power minimization-based resource allocation problem is formulated with the lowest Quality-of-Service (QoS) constraints. We solve it with the message-passing algorithm and evaluate the superior performances of the proposed scheduling and paring schemes on SE and energy efficiency (EE). Extensive simulations are conducted to compare the performances of the proposed schemes with those of the single UAV-aided NOMA uplinks, G2G-based NOMA uplinks, and the proposed multiple UAVs-aided uplinks with a facility location framework-based UAV deployment. Simulation results demonstrate that the proposed multiple UAVs deployment and UP-based NOMA scheme significantly improves the EE and the SE of the cellular uplinks at the cost of only a little relaying power consumption of UAVs.

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

Signa1 modulation recognition (SMR) is an essential and challenging topic in orthogonal frequency-division multiplexing (OFDM) systems, and also it is the fundamental technique for signal detection and recovery. However, traditional feature extraction based SMR methods cannot effectively acquire the characteristics of the OFDM signals. Hence, the modulated OFD-M signal cannot be reliably identified. In this paper, we propose a deep learning (DL) based SMR method for recognizing OFDM signals, which is combined with a convolutional neural network (CNN) trained on in-phase and quadrature (IQ) samples. In the network model, the batch normalization (BN) layer and dropout layer are used to speed up model training and prevent overfitting, respectively. Three convolution layers with different convolution kernels perform well than traditional feature extraction methods in obtaining intrinsic properties of OFDM signals. The same number of multiple modulated signals are mixed and sent to the trained model for identification. Experiments are conducted to show that the method we proposed performs better than the traditional methods, mainly reflected in a higher probability of correct classification (PCC) and better consistency.

Accurate flight delay prediction contains great reference value for airline business and passenger travel. Recent studies have been concentrated on applying machine learning methods to predict the probability of flight delay. Most of the previous prediction methods are built for a single air route or airport. This paper explores a broader spectrum of factors that may potentially affect the flight delay and proposes a gradient boosting decision tree (GBDT) based models for generalized flight delay prediction. To build a dataset for the proposed model, automatic dependent surveillance-broadcast (ADS-B) messages are received, pre-processed, and integrated with other information such as weather condition of airport, flight schedule, and airport information. Since the delay prediction results can be given with higher resolution, the designed prediction tasks contain four different classification tasks. Experimental results show that the proposed GBDT-based model can obtain higher prediction accuracy (87.72% for the binary classification) when handling our limited dataset.

This article aims to improve spectrum efficiency (SE) for the unmanned aerial vehicle (UAV)-relayed cellular uplinks, through distinguishing both line-of-sight (LoS) and non-LoS (NLoS) links. Meanwhile, aiming to accommodate the air-to-ground (A2G) cooperative nonorthogonal multiple access (NOMA)-based cellular users (CUs) with a high energy efficiency (EE), a joint resource allocation (RA) problem is further considered for the UAV and the CUs. To solve the problem, first, an access-priority-based receiver determination (RD) method is derived. According to the RD result, the heuristic user association (UA) strategies are given. Then, based on the UA result, transmission powers of the CUs and the UAV are initialized based on their quality-of-service (QoS) demands. Furthermore, the subchannels are assigned to the associated CUs and the UAV with the reweighted message-passing algorithm. Finally, the transmission power of the CUs and the UAV is jointly fine-tuned with the proposed access control schemes. Compared with the traditional orthogonal frequency-division multiple access (OFDMA) scheme and the traditional ground-to-ground (G2G) NOMA scheme, simulation results confirm that the UAV-aided NOMA with the proposed joint RA scheme yields better performances in terms of the SE, the EE, and the access ratio of the CUs.

Support vector machine (SVM) is a powerful machine learning (ML) technology and the distinctive generalization ability makes it one of the most popular approximation tools in the field of Internet-of-Things (IoT)-based marine data processing. However, SVM has been criticized for trial and error of parameters, especially, kernel function. How to determine a suitable kernel for SVM in a specific problem has been rather tricky. To give a systematic research of the field, we concentrate on the self-adaptive selection of kernel functions in the framework of SVM for IoT-based marine data prediction. Specifically, we adopt the optimal kernel for obtaining competitive SVM and devises a kernel selection criteria of such high-efficiency models. Experiments are conducted via IoT-based real-world marine data sets of different characteristics. The results demonstrate that our proposed self-adaptive SVM model can autonomously provide a suitable kernel for given marine environmental factor prediction, and outperform the alternative with the linear combination of multiple kernels. Besides, the superior performance is verified from the perspective of statistic analysis.

The Internet of Things (IoT) has attracted significant attentions in the fifth generation mobile networks and the smart cities. However, considering the large numbers of connectivity demands, it is vital to improve the spectrum efficiency (SE) of the IoT with an affordable power consumption. To improve the SE, the nonorthogonal multiple access (NOMA) technology is newly proposed through accommodating multiple users in the same spectrums. As a result, in this paper, an energy efficient resource allocation (RA) problem is introduced for the NOMA-based heterogeneous IoT. At first, we assume the successive interference cancellation (SIC) is imperfect for practical implementations. Then, based on the analyzing method for cognitive radio networks, we present a stepwise RA scheme for the mobile users and the IoT users with the mutual interference management. Third, we propose a deep recurrent neural network-based algorithm to solve the problem optimally and rapidly. Moreover, a priorities and rate demands-based user scheduling method is supplemented, to coordinate the access of the heterogeneous users with the limited radio resource. At last, the simulation results verify that the deep learning-based scheme is able to provide optimal RA results for the NOMA heterogeneous IoT with fast convergence and low computational complexity. Compared with the conventional orthogonal frequency division multiple access system, the NOMA system with imperfect SIC yields better performance on the SE and the scale of connectivity, at the cost of high power consumption and low energy efficiency.

The Internet of Things (IoT) has significant importance in the beyond fifth generation (B5G) communication systems. However, the IoT is vulnerable to disasters because the network is mains powered and the devices are delicate. In this paper, an unmanned aerial vehicle (UAV) is utilized to assist with emergency communications in a heterogeneous IoT (Het-IoT) and a distributed nonorthogonal multiple access (NOMA) scheme is proposed without the requirement of successive interference cancelation (SIC). In order to accommodate the communications of the surviving users and IoT devices efficiently, a multiobjective resource allocation (MORA) scheme is proposed for the UAV-assisted Het-IoT. At first, the original MORA problem is formulated and decoupled with the user power initialization. Then, based on a reweighted message-passing algorithm (ReMPA), the subchannels are assigned to the devices and the users in a stepwise manner. Finally, the transmitting power of the users and the devices is jointly fine-tuned using an iterative access control scheme. The simulation results confirm that the distributed SIC-free NOMA (DSF-NOMA) based on the MORA scheme provides satisfactory communication performances for the users and the devices with a tradeoff between the two subsystems. Compared with the traditional orthogonal frequency division multiple access (OFDMA) scheme and the MORA scheme with random subchannel assignment, the proposed DSF-NOMA-based MORA scheme yields better performances in terms of the sum rate of the users and the access ratio of the devices.

Heterogeneous network (HetNet) with energy harvesting is a promising technique to provide perpetual power supplies and ubiquitous coverage as well as high data rate for next-generation wireless communications. In this article, we consider a robust power allocation and power splitting (PS) problem for downlink simultaneous wireless information and power transfer (SWIPT)-enabled HetNets. The robust energy-efficiency (EE) maximization problem of femtocell users (FUs) is formulated under the outage-probability interference power constraint of macrocell user (MU), the maximum allowable transmission power of FU, and the EE-based outage constraint of each FU. The originally fractional optimization problem with the probabilistic constraint is NP-hard and difficult to solve. Without knowing the distribution of uncertain parameters, a min–max probability machine approach isfirstintroduced to convert the semi-infinite optimization problem into a deterministic one which is transformed into a deterministic convex one by using the Dinkelbach method and the quadratic transformation approach. An iterative power allocation and PS scheme is obtained based on convex optimization methods. Finally, the effectiveness of the proposed algorithm is demonstrated by simulation results from the perspective of EE and robustness.

In this paper, an energy efficient resource allocation (RA) problem is introduced for a NOMA based heterogeneous IoT. Particularly, the successive interference cancelation (SIC) is assumed imperfect for implementations. Accordingly, a stepwise scheme is presented with the mutual interference management. Specifically, a deep learning based algorithm is proposed to solve the problem optimally and rapidly. The simulation results verify that the proposed RA scheme provides the optimal results for the NOMA based heterogeneous IoT with fast convergence and low computational complexity. Compared with the OFDMA scheme, the NOMA based scheme yields better performance on the spectrum efficiency (SE) and the scale of connectivity, at the cost of high power consumption and low energy efficiency (EE).

Recently, many technological issues concerning co-operative spectrum sensing (CSS) of cognitive radio networks (CRNs) have been studied, but most of them focus on maximizing spectral efficiency (SE) under the opportunistic spectrum access (OSA) scheme. In this paper, we investigate the mean energy efficiency (EE) maximization problem under the hybrid spectrum sharing (HSS) scheme. Due to channel fading, the effects of reporting channel errors on the EE should be considered. Specifically, the minimum transmit data rate constraint is imposed to ensure the quality of service (QoS) requirements of secondary users (SUs). Our goal is to maximize the mean EE while maintaining the sensing accuracy by jointly optimizing the sensing slot length and the number of cooperative SUs, subject to the rate constraint and the transmit and interference power constraints. To address the non-convexity of the optimization problem, we propose an energy-efficient CSS iterative power adaptation algorithm. Simulation results demonstrate that the proposed algorithm can achieve higher average EE than the conventional OSA scheme.

In cognitive radio networks (CRNs), spectrum sensing data falsification (SSDF) is one of the most typical attack which hugely degrades the detection performance of cooperative spectrum sensing (CSS). SSDF and defense strategies have been an active field of research, but countermeasures of existing researches are sensitive to the number of malicious users (MUs). In this paper, we propose a two-stage credit threshold (TSCT) scheme based CSS to counter arbitrary number of MUs who exist in CRNs. We divide the network region into cells according to channel condition, and CSS procedure is conducted into two stages, which are the secondary user (SU) stage and cell stage. Our proposed scheme can effectively remove MUs in the SU stage and weaken the bad effects of remnant MUs in the cell stage. In comparison to existing schemes, simulation results show that the proposed scheme can provide with better detection performance regardless of detection rounds, and can work well when MUs outnumber SUs while previous schemes fail.

Cooperative spectrum sensing is one of the key technologies to accurately detect the primary user (PU) activity in cognitive radio networks (CRNs). However, collaboration among multiusers provides malicious users (MUs) with an opportunity to launch spectrum sensing data falsification (SSDF) attack. Various approaches have been proposed regarding how to mitigate the negative effect of SSDF attack, while extensive references have strong assumptions such as MUs are in minority and need more decision samples. In this paper, we develop a general SSDF attack model. We further propose a robust data fusion scheme, named robust weighted sequential probability ratio test (RWSPRT), which can deal with various attack probabilities. In the proposed RWSPRT, according to the correct decision ability, the reputation value (RV) of each SU is integrated into weight coefficient of weighted sequential probability ratio test (WSPRT) to improve the performance of cooperative spectrum sensing. Simulation results show that RWSPRT performs more robust than traditional data fusion techniques whereas requires less number of samples, even when a large number of MUs exists in CRNs.

In this paper, we propose a novel resource allocation (RA) scheme based on interference minimization (IM) for cognitive radio networks (CRN). In the approach, we focus on an efficient scheme of subchannels assignment, power allocation and access control for the orthogonal frequency division multiple access (OFDMA)-based secondary users (SUs), accessing licensed spectrums of primary users (PUs) with underlay approach. Different from traditional schemes, we consider more for PUs' capacity protection, by taking the interference introduced to PUs as minimization objective. Compared with other schemes, the simulation results show that our proposed scheme can reserve more PUs' capacity, while guaranteeing the rate and the Quality of Service (QoS) of SUs.