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.

The application of deep learning (DL) in the field of network intrusion detection (NID) has yielded remarkable results in recent years. As for malicious traffic classification tasks, numerous DL methods have proved robust and effective with self-designed model architecture. However, the design of model architecture requires substantial professional knowledge and effort of human experts. Neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal, which is a subdomain of automatic machine learning (AutoML). After that, Differentiable Architecture Search (DARTS) has been proposed by formulating architecture search in a differentiable manner, which greatly improves the search efficiency. In this paper, we introduce a model which performs DARTS in the field of malicious traffic classification and search for optimal architecture based on network traffic datasets. In addition, we compare the DARTS method with several common models, including convolutional neural network (CNN), full connect neural network (FC), support vector machine (SVM), and multi-layer Perception (MLP). Simulation results show that the proposed method can achieve the optimal classification accuracy at lower parameters without manual architecture engineering.

Malware traffic classification (MTC) is a key technology for anomaly and intrusion detection in secure Industrial Internet of Things (IIoT). Traditional MTC methods based on port, payload, and statistic depend on the manual-designed features, which have low accuracy. Recently, deep-learning methods have attracted a significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep-learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this article proposes three methods based on semisupervised learning (SSL), transfer learning (TL), and domain adaptive (DA), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the classification accuracy with few labeled samples. Then, we use the DA method to solve the mismatch problem between the source domain and the target domain in the TL process. The proposed method is not only applicable to the shallow network but also to the deep neural network structure, and can achieve better classification results. Experimental results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples in IIoT. The source code for all the experiments is available at GitHub.The code of this article can be downloaded from GitHub link:https://github.com/yzjh/Keras-MTC-DA-Ladder.

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

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.

Adaptive modulation and coding (AMC) technique plays an important role in vehicle-to-vehicle (V2V) systems. It enables smart vehicles to keep a good quality of communication for a better driving experience. However, the existing AMC methods for V2V system did not consider multiple scenarios and the amount of calculation is relatively large. In this paper, we propose a simple convolutional neural networks (CNN)-based AMC method which can extract features of channel and noise estimation from receiver, the transmitter will adjust in the light of modulation strategy to ensure the quality of V2V communication. Simulation results reveal that our proposed method performs better in terms of packet error rate (PER), throughput, classification accuracy with a lower prediction time.

Malware traffic classification (MTC) is a key technology for solving anomaly detection and intrusion detection problems. And hence it plays an important role in the field of network security. Traditional MTC methods based on port, payload and statistic depend on the manual-designed features, which have low accuracy. Recently, deep learning methods have attracted significant attention due to their high accuracy in terms of classification. However, in practical application scenarios, deep learning methods require a large amount of labeled samples for training, while the available labeled samples for training are very rare. Furthermore, the preparation of a large amount of labeled samples requires a lot of labor costs. To solve these problems, this paper proposes two methods based on semi-supervised learning (SSL) and transfer learning (TL), respectively. Our proposed methods use a large amount of unlabeled data collected in the Internet traffic, which can greatly improve the accuracy classification with few labeled samples. Through experiments, we obtained the best method to improve the accuracy of few labeled samples in different situations. Experiment results show that our proposed methods can satisfy the requirement of MTC in the case of few labeled samples.

Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.

Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.

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.

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.

Compared with the conventional degrees of freedom, the orbital angular momentum (OAM), which describes the helical phase structure of electromagnetic wave, provides a new degree of freedom. As a new multiple access scheme, mode division multiple access (MDMA) is constructed in millimetre wave frequency band utilising the orthogonality and high dimensionality in this study. Various traditional resources such as frequency, time and code pattern have been shared. Therefore, addresses of signals from different terminal users can be distinguished by OAM mode to realise multi‐address connection. In this study, the theoretical analysis of the number of terminals in MDMA scheme is carried out. According to the analysis results, infinite terminals can be connected together in the ideal case. Moreover, the simulation results show that compared with the conventional multi‐input multi‐output millimetre wave communication systems, the performance indicators of MDMA millimetre wave communication systems are improved remarkably.

The ‐norm penalised (LP) normalised least mean square algorithm converges faster than the LP normalised least mean fourth algorithm does, but the latter can achieve better steady‐state performance, particularly in regions with low signal‐to‐noise ratios (SNRs). To simultaneously take advantage of both merits, a sparse hybrid adaptive filtering algorithm is proposed in various SNR environments. Specifically, the authors construct a cost function that uses the statistical error term and sparse penalty term. The first term is designed by a hybrid error function of the second‐ and fourth‐order statistical errors, respectively, and the second term is obtained using a sparse constraint function. The hybrid error term can be easily balanced by a proportional parameter . Moreover, they devise a non‐uniform step size in the proposed algorithm to further balance the convergence speed and estimation error. Simulation results are provided to validate the proposed algorithm in various SNR environments.

This paper studies the relay selection schemes in mobile communication system over Nakagami-m channel. To make efficient use of licensed spectrum, both single relay selection (SRS) scheme and multirelays selection (MRS) scheme over the Nakagami-m channel are proposed. Also, the intercept probability (IP) and outage probability (OP) of the proposed SRS and MRS for the communication links depending on realistic spectrum sensing are derived. Furthermore, this paper assesses the manifestation of conventional direct transmission scheme to compare with the proposed SRS and MRS ones based on the Nakagami-m channel, and the security-reliability trade-off (SRT) performance of the proposed schemes and the conventional schemes is well investigated. Additionally, the SRT of the proposed SRS and MRS schemes is demonstrated better than that of direct transmission scheme over the Nakagami-m channel, which can protect the communication transmissions against eavesdropping attacks. Additionally, simulation results show that our proposed relay selection schemes achieve better SRT performance than that of conventional direct transmission over the Nakagami-m channel.