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Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum. Traditional denoising methods are intricate and require analytical expertise in gamma spectrum analysis. This paper proposes a segmentation‐enhanced Convolutional Neural Network‐Stacked Denoising Autoencoder (CNN‐SDAE) method based on convolutional feature extraction network and stacked denoising autoencoder to achieve gamma spectrum denoising, which adopts the idea of data segmentation to enhance the learning ability of the neural network. By dividing the complete gamma spectrum into multiple segments and then using the segmentation‐enhanced CNN‐SDAE method for denoising, the method can achieve adaptive denoising without manually setting the threshold. The experimental results show that our method can effectively achieve gamma spectrum denoising while retaining the characteristics of the gamma spectrum. Compared with traditional methods, the denoising speed and effectiveness have been significantly improved, and the proposed method demonstrates an approximately 1.72‐fold enhancement in smoothing performance than the empirical mode decomposition method. Furthermore, in terms of retaining gamma spectrum characteristics, it also achieves a performance improvement of approximately three orders of magnitude than the wavelet method. [ABSTRACT FROM AUTHOR]

In vehicular communications, channel estimation is a complex problem due to the joint time–frequency selectivity of wireless propagation channels. To this end, several signal processing techniques as well as approaches based on neural networks have been proposed to address this issue. Due to the highly dynamic and random nature of vehicular communication environments, precise characterization of temporal correlation across a received data sequence can enable more accurate channel estimation. This paper proposes a new pilot constellation scheme in combination with a small feed‐forward neural network to improve the accuracy of channel estimation in V2X systems while keeping low the implementation complexity. The performance is evaluated in typical vehicular channels using simulated BER curves, and it is found superior to traditional channel estimation methods and state‐of‐the‐art neural‐network‐based implementations such as feed‐forward and super‐resolution. It is illustrated that the improvement becomes pronounced for small subcarrier spacings (or low 5G numerologies); hence, this paper contributes to the development of more reliable mobile services across rapidly varying vehicular communication channels with rich multi‐path interference. [ABSTRACT FROM AUTHOR]

Delay tolerant networks (DTNs) is a network evolved from mobile networks. Differing from the traditional network, which has a stable end‐to‐end transmission path, DTNs are sparse and intermittently connected mobile ad hoc network, which are widely used in harsh environments, such as battlefields, seabed, space communication networks, and so on. In DTNs, intermittent connectivity, partitioned network, long delays and node mobility characteristics make the network fail to communicate frequently, therefore, how to successfully forward the message is of extreme importance. Up to now, almost all the traditional models in DTNs use the store‐carry‐forward method. This paper proposes a novel clustered DTN routing model based on sensing node relationship strength. The routing mechanism takes advantage of the number of other nodes encountered by the nodes in the process of movement and the changes in the number of nodes to calculate the strength of the relationship between nodes, and clusters DTN routing according to the strength of the relationship between nodes. Moreover, the relationship between nodes in a cluster and other clusters is used to transmit messages between clusters, and messages are transmitted within clusters according to the strength of the relationship between nodes. Simulation results show that the routing mechanism not only increases the success rate of message transmission, but also reduces the transmission delay of messages and improves network performance. [ABSTRACT FROM AUTHOR]

A column‐weight k$k$ LDPC code with the parity‐check matrix H$H$ is called decomposable if there exists a permutation π$\pi$ on the rows of H$H$, such that π(H)${\pi }(H)$ can be decomposed into k$k$ column‐weight one matrix. In this paper, some variations of edge coloring of graphs are used to construct some column‐weight three decomposable LDPC codes with girths at least six and eight. Applying the presented method on several known classes of bipartite graphs, some classes of column‐weight three decomposable LDPC codes are derived having flexibility in length and rate. Interestingly, the constructed parity‐check matrices based on the proper edge coloring of graphs can be considered as the base matrix of some high rate column‐weight three quasi‐cyclic (QC) LDPC codes with maximum‐achievable girth 20. The paper also leads to a simple characterization of elementary trapping sets of the decomposable codes based on the chromatic index of the corresponding normal graphs. This characterization corresponds to a simple search algorithm finds all possible existing elementary trapping sets in a girth‐6 or girth‐8 column‐weight 3 LDPC code which are layered super set of a short cycle in the Tanner graph of the code. Simulation results indicate that the QC‐LDPC codes with large girths lifted from the constructed base matrices have good performances over AWGN channel. [ABSTRACT FROM AUTHOR]

As the global data strategy deepens and data elements accelerate integrating and flowing more rapidly, the demand for data security and privacy protection has become increasingly prominent. Confidential computing emerges as a crucial security technology to solve security and privacy problem, and it is also a hot subject of in contemporary security technologies. Leveraging collaborative security in both hardware and software, it builds a trusted execution environment to ensure confidentiality and integrity protection for data in use. This paper provides a comprehensive overview of the development process of confidential computing, summarizing its current research status and issues, which focuses on the security requirements for data security and privacy protection. Furthermore, it deeply analyses the common technical features of confidential computing, and proposes a trusted confidential computing architecture based on collaborative hardware and software trust. Then, it elaborates on the research status and issues of confidential computing from four aspects: hardware security, architecture and key technologies, applications, and standards and evaluation. Finally, this paper provides a synthesis and outlook for the future development of confidential computing. In summary, confidential computing is currently in a rapidly developing stage and will play an important role in cyber security in the future. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle ad‐hoc network (UANET), the node speed of unmanned aerial vehicles (UAVs) may reach up to 400 km/h. The fast or slow movement of UAV nodes leads to different speeds of topology change of the nodes. Traditional optimized link state routing (OLSR) protocol cannot adaptively adjust the routing update period when the network topology changes, which may lead to the nodes calculating incorrect routing tables. This increases the average end‐to‐end delay and packet loss rate for packet transmission. To enhance the adaptability of OLSR routing protocol to network topology changes, this paper proposes a multi‐agent independent deep deterministic policy gradient‐OLSR (MA‐IDDPG‐OLSR) routing protocol based on distributed multi‐agent reinforcement learning. The protocol deploys DDPG algorithm on each UAV node, and each UAV node adaptively adjusts the Hello and TC message sending intervals, according to the one‐hop neighbouring nodes as well as its own state. Simulation results show that the proposed protocol is able to improve the throughput and reduce the packet loss rate as compared to traditional AODV, GRP, OLSR, and distributed multiple‐agent independent proximal policy optimization‐OLSR (MA‐IPPO‐OLSR), distributed multiple‐agent independent twin delayed deep deterministic policy gradient‐OLSR (MA‐ITD3‐OLSR) routing protocols. Since MA‐IDDPG‐OLSR relies only on local information, there is a minor performance degradation in MA‐IDDPG‐OLSR compared to centralized single‐agent DQN‐OLSR routing protocol. But it is more suitable to a completely distributed UAV network without a centralized node. [ABSTRACT FROM AUTHOR]

Communication security has become particularly crucial with the rapid development of the Internet of Things (IoT). Frequency hopping spread spectrum (FHSS) technology, a prevalent method in wireless communication, has a wide range of applications in the Internet of Things. Enhancing the security of frequency hopping sequences is an essential means to improve the security of frequency hopping communication in the Internet of Things, as the performance of frequency hopping sequences plays a crucial role in frequency hopping systems. This paper proposes constructing secure adaptive frequency hopping sequence sets based on the advanced encryption standard (AES) algorithm. As a block cipher algorithm with superior security, the AES algorithm can provide a fundamental guarantee for the security of the proposed frequency hopping sequences. The mapping methods from ciphertext sequences to frequency hopping sequences proposed in this paper can achieve the construction of frequency hopping sequences of any frequency set size to meet the requirements of adaptive frequency hopping. In addition, we also model and analyse the problem of overlapping spectrum band of the IoT groups in the industrial, scientific, and medical (ISM) band, aiming to achieve better packet transmission performance by adjusting the frequency set size. [ABSTRACT FROM AUTHOR]

Sybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost‐benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks. [ABSTRACT FROM AUTHOR]

With the continuous evolution and in‐depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal‐to‐noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two‐dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q‐learning is considered in this paper to obtain the sub‐optimal solution due to the non‐convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity. [ABSTRACT FROM AUTHOR]

Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms. [ABSTRACT FROM AUTHOR]

This paper studies the problem of joint deployment, user association, channel, and resource allocation in unmanned aerial vehicle‐enabled access network. Since different user equipments performing different tasks and have different data rate requirements, the priority‐based traffic fairness problem is investigated. This problem, however, is a mixed integer nonlinear programming problem with NP‐hard complexity, making it challenging to be solved. To address this issue, a self‐organized and distributed framework "sense‐as‐you‐fly" based on the decomposition process, which divides the original problem into several subproblems, is proposed. Assuming without central controller, we derive the closed‐form resource allocation scheme and propose distributed many‐to‐one matching to optimize user association subproblem. Considering the coupled characteristics, the multi‐unmanned aerial vehicle deployment and channel allocation subproblems are modelled as a local altruistic game. The existence of Nash equilibrium is proved with the aid of exact potential game and efficient best response learning‐based algorithm is proposed. The original problem is finally addressed by solving the sub‐problems alternately and iteratively. Simulation results verify its effectiveness. By jointly optimizing multidimensional variables, the proposed algorithm unlocks network performance gains, especially in resource‐limited regimes. [ABSTRACT FROM AUTHOR]

Intelligent reflecting surface (IRS) has recently been considered as a potential technology for realizing ultra‐reliable and low‐latency (URLLC) in wireless networks. This paper proposes a resource optimization scheme to maximize the sum‐rate for an IRS‐assisted downlink multiuser multi‐input single‐output (MISO) URLLC system. For the perfect CSI scenario, we jointly optimize each user's block‐length and packet‐error probability, the precoding vectors at the base station (BS), and the passive beamforming with discrete phase shifts at the IRS. Given the problem's complexity, we design a computationally efficient iterative algorithm using successive convex approximation (SCA) and semidefinite relaxation (SDR) techniques to obtain a locally optimal solution. Specifically, for the imperfect CSI scenario, we construct a robust resource optimization problem model and incorporate the S‐procedure to address the impact of channel uncertainty, proposing an iterative algorithm based on the alternating optimization (AO) method to achieve a locally optimal solution. Simulation results demonstrate that: 1) An IRS equipped with a 2‐bit quantized resolution phase shifter is sufficient to achieve a system sum‐rate comparable to that of an ideal phase shifter; 2) Compared to other Baseline schemes, Algorithm 2 exhibits better robustness and superior performance gains under imperfect CSI. [ABSTRACT FROM AUTHOR]

Fault recorded data has been proven to be effective for fault diagnosis of overhead transmission lines. Utilizing deep learning to mine potential fault patterns in fault recording data is an inevitable trend. However, it is usually difficult to obtain massive labeled fault recording data, which results in deep learning‐based fault diagnosis models not being adequately trained. Although data augmentation methods provide ideas for expanding the training data, existing data augmentation algorithms (e.g. random perturbation‐based augmentation) may lead to distortion of multi‐view data, that is, time domain data and frequency domain data of the fault recorded data, which results in the inconsistency of physical properties and statistical distributions of the generated data and the actual recording data, and misguides the training of the models. Hence, this study proposes a transmission line fault classification method via the multi‐view synergistic enhancement of fault recording data. The methodology proposes to start with a synergistic enhancement of multi‐view data such as time and frequency domains of fault recording data, and utilizes contrastive learning to further improve the performance of the fault classification model while ensuring that the generated data is not distorted. Experimental results on three real‐world datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

This paper investigates an intelligent reflecting surface (IRS) assisted downlink short packet transmission system, where an access point sends short packets to multiple devices with the help of an IRS. Specifically, a performance comparison between the frequency division multiple access and time division multiple access is conducted for the considered system, from the perspective of average age of information (AoI). To minimize the maximum average AoI among all devices, the resource allocation and passive beamforming are jointly optimized. However, the formulated problem is difficult to solve due to the non‐convex objective function and coupled variables. Thus, an alternating optimization based algorithm is proposed by exploiting the semidefinite relaxation and bisection search techniques. Simulation results show that time division multiple access can achieve lower AoI by exploiting the time‐selective passive beamforming of IRS for maximizing the signal to noise ratio of each device consecutively. Moreover, it also shows that as the length of information bits becomes sufficiently large as compared to the available bandwidth, the proposed frequency division multiple access transmission scheme becomes more favourable due to more flexible power allocation. [ABSTRACT FROM AUTHOR]

To solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy‐preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and a k‐anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc‐based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system. [ABSTRACT FROM AUTHOR]

The evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation. [ABSTRACT FROM AUTHOR]

Nowadays, the development of Artificial Intelligence of Things (AIoT) is advancing rapidly, and intelligent devices are increasingly exposed to more security risks on the network. Deep learning‐based intrusion detection is an effective security defence approach. Federated learning (FL) is capable of enabling deep learning models to be trained on local clients without uploading their data to a central server. This paper proposes a novel federated learning aggregation algorithm called fed‐dynamic gravitational search algorithm (Fed‐DGSA), which incorporates the GSA algorithm to optimize the weight updating process of FL local models. During the updating process, the decay rate of the gravity coefficient is optimized and random perturbations and dynamic weights are introduced to ensure a more stable and efficient FL aggregation process. The experimental results show that the detection accuracy of Fed‐DGSA has reached about 97.8%, and it is demonstrated that the model trained using Fed‐DGSA achieves higher accuracy compared to Fed‐Avg. [ABSTRACT FROM AUTHOR]

Worker selection is critical to the success of federated learning, but issues such as inadequate incentives and poor‐quality data can negatively impact the process. The existing studies have used the multi‐weight subjective logic model, but it is vulnerable to malicious evaluation and unfair to newly added nodes. In this paper, the authors propose an improved reputation evaluation algorithm that allows evaluations from different sources to influence each other and reduce the impact of malicious comments. The authors' approach effectively distinguishes between malicious and honest users and improves worker selection and collaboration in federated learning. [ABSTRACT FROM AUTHOR]

The AIoT, with its artificial intelligence capabilities, can further enhance Device‐to‐Device (D2D) communication. Based on Non‐Orthogonal Multiple Access (NOMA), D2D technology can effectively alleviate wireless spectrum resource pressure and improve the capacity of heterogeneous cellular networks. However, it also introduces significant system interference issues. In this paper, a resource allocation algorithm is proposed for the NOMA‐D2D heterogeneous cellular network, based on a multi‐agent deep reinforcement learning framework. Firstly, the algorithm allocates appropriate channels to D2D clusters. Then, the power allocation factors and D2D transmit power are jointly optimized to suppress the interference and improve the system performance. Simulation results show that both the channel allocation efficiency and the power control performance of the system can be significantly improved. [ABSTRACT FROM AUTHOR]

Multi‐user (MU) massive multiple input multiple output (mMIMO) is considered a potential technology for fifth generation (5G) and sixth‐generation (6G) wireless systems. The presence of the antenna arrays at the base station level to communicate with the users or to serve tens of single antenna users leads to excessively high system costs and power consumption. The deployment 1‐bit digital‐to‐analogue converters (DACs) in the base station can solve these problems. This paper starts by presenting an analytical study centered on the effects of 1‐bit DACs on the system envisaged for a Rayleigh‐type fading channel. Compact‐form expressions are derived for the symbol error rate. Afterwards, an efficient end‐to‐end deep learning technique to compensate for the joint effect of 1‐bit DAC and imperfect channel state information in downlink mMIMO systems. Moreover, to improve the performance of the considered system, a DAC mixed architecture is proposed, where a number of antennas use 1 bit DACs while the others do not. The simulations results showed the improvement in transmission quality of the downlink of the MU‐mMIMO system in the presence of hardware imperfections using the considered end‐to‐end compensation technique. [ABSTRACT FROM AUTHOR]

In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing. [ABSTRACT FROM AUTHOR]

At present, in order to conduct non‐destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map‐matching algorithm and double‐pole search algorithm (MM‐DPS) is proposed in this paper. The map‐matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double‐pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A‐star algorithm, the MM‐DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption. [ABSTRACT FROM AUTHOR]

A transmission power minimization problem for a reconfigurable intelligent surface (RIS) aided non‐orthogonal multiple access (NOMA) network has been studied here, which has assumed the channel state information is imperfect. The channel error in this paper has been modeled as two different error models, that is, bounded error model and statistical error model. First, a two‐step algorithm is proposed to decouple the decoding order and optimization variables under bounded error model. Then, for the statistical error model, the alternating optimization method is adopted to decouple optimization variables. Finally, the performance difference is compared between the proposed beamforming scheme and the non‐RIS scheme from two key parameters, the number of reflected elements and the error factor. Simulation results verify that passive RIS has the advantage on the system performance compared with the scheme without RIS. [ABSTRACT FROM AUTHOR]

Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%. [ABSTRACT FROM AUTHOR]

This paper discusses a new mapping scheme known as face coded modulation (FCM) system. In FCM, peak energy symbols are mapped onto an innermost ring according to the eight sockets in the human face, that is, brain, mouth, nostrils, eyes and ears. For example, FCM is formed when the constellation diagram from M‐ary quadrature amplitude modulation (MQAM) system is modified to reduce peak‐to‐average power ratio (PAPR) by relocating the four corner symbols of the MQAM, with peak energy, to the innermost ring in a way that forms the figure of a cross. Unlike APSK, FCM mapping introduces non‐uniform sequence of symbols on the ring, face width factor and multiple modulator circuits that can be used to lower power requirements for high power amplifiers (HPA) as used in MQAM transmission systems. Symbol error rate (SER) for FCM is calculated and the results compared with MQAM and MPSK. It is shown that at equal energy efficiency, FCM scheme has a better response to errors than both MPSK and MQAM and a better energy efficiency due to lowered PAPR than MQAM. Moreover, the simulation results exhibit a tight match for the proposed analytical framework when assessed under Additive White Gaussian Noise (AWGN) channel. [ABSTRACT FROM AUTHOR]

Delta compression, as a complementary technique for data deduplication, has gained widespread attention in network storage systems. It can eliminate redundant data between non‐duplicate but similar chunks that cannot be identified by data deduplication. The network transmission overhead between servers and clients can be greatly reduced by using data deduplication and delta compression techniques. Resemblance detection is a technique that identifies similar chunks for post‐deduplication delta compression in network storage systems. The existing resemblance detection approaches fail to detect similar chunks with arbitrary similarity by setting a similarity threshold, which can be suboptimal. In this paper, the authors propose Chunk2vec, a resemblance detection scheme for delta compression that utilizes deep learning techniques and Approximate Nearest Neighbour Search technique to detect similar chunks with any given similarity range. Chunk2vec uses a deep neural network, Sentence‐BERT, to extract an approximate feature vector for each chunk while preserving its similarity with other chunks. The experimental results on five real‐world datasets indicate that Chunk2vec improves the accuracy of resemblance detection for delta compression and achieves higher compression ratio than the state‐of‐the‐art resemblance detection technique. [ABSTRACT FROM AUTHOR]

This paper proposes a whale optimization algorithm (WOA)‐based partial transmit sequence (PTS) scheme called WOA‐PTS to reduce the high peak‐to‐average power ratio (PAPR) for universal filtered multi‐carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi‐carrier systems. In the conventional PTS technique, the optimization of phase rotation factors is achieved through multiplication with the sub‐blocks. In this undertaking, WOA optimization is integrated as a phase optimizer in the PTS‐based PAPR reduction scheme. The experimental results show that, when compared with PTS‐based UFMC signal, the UFMC signal with WOA‐PTS can achieve 4.15 dB PAPR reduction at the complementary cumulative distribution function value of 10−3, additionally power spectral density performance and bit error rate also improved. [ABSTRACT FROM AUTHOR]

This article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances. [ABSTRACT FROM AUTHOR]

Mobile networks are expanding quickly as a result of significant advancements in wireless technologies and solutions, especially with the recent introduction of the Fifth Generation New Radio. The growth in mobile networks requires the installation of massive numbers of base stations that bring concerns about increasing overall electromagnetic field (EMF) radiation exposure levels. The International Commission on Non‐Ionizing Radiation Protection (ICNIRP) has published guidelines that have been adopted by many regulators in many countries to control the overall radiation emitted from EMF transmitters. This paper studies the compliance boundary for a single site operating with multiple technologies including from the second generation (2G) to 5G colocated in the same site. The analysis is performed using a typical site configuration setup for the boundary calculations in the form of the Compliance Distance (CD). The calculation uses the power reduction factor and system load for more realistic results, and in situ measurements are conducted to validate the calculation's formula. The study also investigated the CD for four types of sites, macro, micro, small cell, and indoor sites. Additionally, the study analyzed the power densities (PDs) and total exposure ratio (TER) for the general public and occupational workers at each site. The results show that CD has shorter distances when the power factor is considered, and 5G makes the highest contribution to the TER at the CD in the main directions of the antenna. [ABSTRACT FROM AUTHOR]

This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER. [ABSTRACT FROM AUTHOR]

As a key component of high‐performance switches and routers, the packet forwarding engine (PFE) is mainly responsible for selecting the appropriate output port for tens of thousands of packets within an extremely short time frame. However, the performance of PFE is determined by the selected group membership algorithm. This paper puts forth a hybrid strategy–caching scalar‐pair and vectors routing and forwarding (CSVRF), consisting of virtual output port bitmap caching (VOPBC) and fractional‐N SVRF to address major multicast forwarding issues such as scalability by using content addressable memory. In CSVRF, a virtual output port bitmap cache is introduced, which includes the most popular combinations of output port bitmap and divides the big scalar‐pair into N sub‐groups to achieve parallel compute and the reusability of less bit‐length prime. The results demonstrate that the memory space and the forwarding latency are effectively reduced compared with previous work. In space efficiency, it only required 10% memory space compared with the original SVRF/fractional‐N SVRF, decreased 10% memory usage compared with pure VOPBC and nearly improved 1 to 4 orders of magnitude of packet processing time compared with the original SVRF and the fractional‐N SVRF respectively. [ABSTRACT FROM AUTHOR]

In order to solve the security problems caused by malicious nodes in wireless sensor networks, a TS‐BRS reputation model based on time series analysis is proposed in this paper. By using the time series analysis method, the matching analysis of two time series is carried out to reduce the interference of channel conflicts on the reputation evaluation model and improve the accuracy of model recognition. In order to improve the adaptability of the evaluation model, the adaptive maintenance function μ is introduced into the update of credit value, which aggravates the influence of node behaviour on credit value at the present stage. The simulation results show that the new reputation evaluation model can effectively improve the detection rate and detection speed of malicious nodes in the network. After the introduction of maintenance function, the reputation value of the captured malicious nodes in the network has a faster convergence speed. [ABSTRACT FROM AUTHOR]

In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human‐like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in‐depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security. [ABSTRACT FROM AUTHOR]

PageRank talent mining in power system is an effective means for enterprises to recruit talents, which can correctly recommend talents in practical applications. At present, the mining evaluation index system is not perfect, and the consistency coefficient between the evaluation results and the actual situation is low in practical applications. Therefore, PageRank talent mining algorithm in power system based on cognitive load and dilated convolutional neural network (DPCNN) is proposed. The cognitive load and DPCNN are used to establish a talent capability evaluation system, calculate the index weight value, construct the PageRank talent capability evaluation model of the power system according to the corresponding weight of the index, determine the membership range of the index, calculate the comprehensive score of the appraiser's ability, and determine the ability level of the appraiser, thus realizing the PageRank talent mining algorithm of the power system. The experimental results show that the algorithm has high accuracy and objectivity, good encryption effect, cannot crack the attack node, the prediction error and the prediction relative error are closest to the standard value, the maximum error is 0.51, the maximum relative error is 0.82, and can achieve the accurate prediction of talent demand. [ABSTRACT FROM AUTHOR]

In present times, Radio‐Frequency Identification (RFID) systems have seen a significant rise in their usage. There has been an increasing interest in developing even lighter RFID protocols suitable for resource‐constrained environments. Ensuring security and privacy remain critical challenges in RFID‐based systems. Recently proposed lightweight authentication schemes, namely LRSAS+ and LRARP+, are ideally suited for constrained devices. However, this article investigates these schemes and reveals certain vulnerabilities: LRSAS+ is susceptible to tag impersonation, desynchronization, and traceability attacks, while LRARP+ can fall prey to traceability and secret disclosure attacks. An enhanced version of these authentication systems is proposed that tackles their inherent weaknesses by leveraging the χper$\chi per$ function. To verify the security of the proposed scheme, a formal analysis is conducted using Gong–Needham–Yahalom logic (GNY logic) and an automated security protocol verification tool, ProVerif. The improved scheme's effectiveness is also compared with multiple contemporary lightweight systems. The results indicate that the enhanced scheme not only meets the security requirements for lightweight authentication schemes but also achieves this with minimal computational overhead. [ABSTRACT FROM AUTHOR]

Automatic modulation classification models based on deep learning models are at risk of being interfered by adversarial attacks. In an adversarial attack, the attacker causes the classification model to misclassify the received signal by adding carefully crafted adversarial interference to the transmitted signal. Based on the requirements of efficient computing and edge deployment, a lightweight automatic modulation classification model is proposed. Considering that the lightweight automatic modulation classification model is more susceptible to interference from adversarial attacks and that adversarial training of the lightweight auto‐modulation classification model fails to achieve the desired results, an adversarial attack defense system for the lightweight automatic modulation classification model is further proposed, which can enhance the robustness when subjected to adversarial attacks. The defense method aims to transfer the adversarial robustness from a trained large automatic modulation classification model to a lightweight model through the technique of adversarial robust distillation. The proposed method exhibits better adversarial robustness than current defense techniques in feature fusion based automatic modulation classification models in white box attack scenarios. [ABSTRACT FROM AUTHOR]

The challenge of ensuring reliability for high‐efficiency technology, multiple‐input‐multiple‐output orthogonal‐frequency‐division‐multiplexing (MIMO‐OFDM), in wireless frequency‐selective fading environments persists. In this article, the concept of spreading a symbol's energy is proposed as a viable solution to enhance transmission reliability for MIMO‐OFDM systems. And an energy‐spreading‐transform (EST)‐based MIMO‐OFDM transceiver is developed. Following the Inverse Fast Fourier Transform (IFFT) performed by the conventional MIMO‐OFDM transmitter, an orthogonal transformation called the EST is introduced. This transform spreads the energy of a symbol across the entire frequency domain and all time slots. The EST is coupled with the improved iterative detection algorithm named EST‐partial decision (PD)‐iterative‐interference‐cancellation (EST‐PD‐IIC) to maximize and leverage the potential diversity gain. Numerical simulation results demonstrate that the proposed scheme approaches the performance bound when signal‐to‐noise ratio (SNR) is about 21dB for 16‐ary quadrature amplitude modulation (16‐QAM). Complexity analysis illustrates that the computational complexity of the evolved EST‐PD‐IIC algorithm is lower than that of the famous vertically Bell laboratory layered space‐time detector (V‐BLAST) when the antenna array size is greater than 3×3$3\times 3$. In summary, the proposed scheme is practical for providing high‐quality communication in multi‐path fading environments and can even enable a reliable communication without channel encoding when Eb/N0 exceeds a threshold in 5G‐Advanced. [ABSTRACT FROM AUTHOR]

The Network Function Virtualization (NFV) technology has emerged to turn hardware‐based network functions into software‐based virtual entities. In NFV‐enabled multicast services, the data flow should be passed through a series of Virtual Network Functions (VNFs) before reaching destinations. In the problem of NFV‐enabled multicast service embedding, not only the VNFs must be deployed, but also the traffic forwarding topology must be constructed. As the exact solution of this problem suffers from high computational complexity, the heuristic or approximation approaches are used in practice. However, as a way to control the trade off between complexity and efficiency, using multistage solutions is offered in literature. In this article, some two‐stage approaches for embedding NFV‐enabled multicast services are introduced, and they are compared through analysis and simulation. Specifically, a new low‐complexity approach based on the Auxiliary VNF Graph (AVG) is proposed to determine the sub‐optimal locations for VNF placement and to construct the NFV‐based multicast routing tree. The comparative analysis shows that the proposal has acceptable performance while reducing the computational complexity significantly. [ABSTRACT FROM AUTHOR]

Within the evolving landscape of fifth‐generation (5G) wireless networks, the introduction of network‐slicing protocols has become pivotal, enabling the accommodation of diverse application needs while fortifying defences against potential security breaches. This study endeavours to construct a comprehensive network‐slicing model integrated with an attack detection system within the 5G framework. Leveraging software‐defined networking (SDN) along with deep learning techniques, this approach seeks to fortify security measures while optimizing network performance. This undertaking introduces network slicing predicated on SDN with the OpenFlow protocol and Ryu control technology, complemented by a neural network model for attack detection using deep learning methodologies. Additionally, the proposed convolutional neural networks‐long short‐term memory approach demonstrates superiority over conventional ML algorithms, signifying its potential for real‐time attack detection. Evaluation of the proposed system using a 5G dataset showcases an impressive accuracy of 99%, surpassing previous studies, and affirming the efficacy of the approach. Moreover, network slicing significantly enhances quality of service by segmenting services based on bandwidth. Future research will concentrate on real‐world implementation, encompassing diverse dataset evaluations, and assessing the model's adaptability across varied scenarios. [ABSTRACT FROM AUTHOR]

Chaotic time series prediction is a prediction method based on chaos theory, and has important theoretical and application value. At present, most prediction methods only pursue digital fitting and do not consider the directional trend. In addition, using the single model will not achieve better prediction results. Therefore, a chaotic time series combined prediction model for improving trend lagging (ITL) is proposed. An improved dual‐stage attention‐based long short‐term memory model with the improved training objective fuction is designed to solve the trend lagging problem. Then, an auto regressive moving average model with the sliding window is established to mine other characteristics of the time series except nonlinear characteristic. Finally, the idea of optimization algorithm is introduced to construct a time series combined prediction model with high accuracy based on the above two models, so as to perform the chaotic time series prediction from multiple perspectives. Multiple datasets are selected as experimental datasets, and the proposed method is compared with common prediction methods. The results show that the proposed method can achieve single‐step prediction with high accuracy and effectively improve the lagging of chaotic time series prediction. This research can provide theoretical support for the complex chaotic time series prediction. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of specialized sensor nodes that perform sensing services for Internet of Things devices with limited battery power. As the replacement or recharging of batteries is not possible, energy consumption becomes the most important design issue in WSNs. The energy‐efficient routing protocol gets the most priority in such energy‐constrained networks. The utilization of clustering‐based routing protocols like stable election protocol (SEP) has gained much attention as the network's lifetime is significantly improved due to the clustering of sensor nodes. Moreover, the inclusion of a secondary CH (SCH) is beneficial when the member node with the most remaining energy performs data aggregation and reduces the energy burden of the CH. This article characterizes SEP and Prolong SEP (P‐SEP) protocols and then extends them by incorporating SCH, which ensures balanced energy consumption. The performance of the proposed Extended SEP and Extended P‐SEP protocols has been analysed regarding stability period, network lifetime, energy usage and throughput. The simulation results demonstrate that the proposed protocols outperform state‐of‐the‐art protocols like P‐SEP, Modified SEP (M‐SEP) and SEP. In particular, the stability period of Extended SEP and Extended P‐SEP has improved up to 42% and 89%, respectively, compared to M‐SEP. [ABSTRACT FROM AUTHOR]

Spread spectrum technology has been widely employed for positioning and communicating with autonomous underwater vehicles (AUVs), but conventional spread spectrum sequences lack confidentiality and reliability in UWA channel. Considering the limitations of conventional sequences and the characteristics of underwater acoustic (UWA) channel, a composite chaotic orthogonal sequence (CCOS) based on the UWA channel is proposed. The confidentiality of the CCOS is superior to that of the m‐sequence, while the autocorrelation performance of the CCOS is superior to that of the orthogonal sequence. Moreover, the CCOS can compensate for the imbalance of logistic chaotic sequence when assigned certain initial values. Acquisition is a crucial component of accessing the spread spectrum signal; therefore, the acquisition performance indicates the applicability of the CCOS. The source–target model is established to simulate communication with an underwater moving target. To simulate the acquisition process, a parallel algorithm based on fast Fourier transform is adopted, and the entire simulation process is completed based on the BELLHOP ray acoustic model. Through data processing, the Doppler shift error is less than half of the frequency‐search element. Furthermore, the acquisition probabilities of the CCOS with different numbers of bits are over 90%, which demonstrates the reliability of the CCOS. [ABSTRACT FROM AUTHOR]

Backscatter communications, which originated from World War II, have been widely applied in the logistics domain, and recently attract emerging interest from both academic and industrial circles. Here, the backscatter communication systems equipped with retrodirective arrays that can re‐transmit the impinging signals back toward the direction of incidence are studied so as to reduce the power loss of the signals. Specifically, the authors consider the tag is equipped with retrodirective arrays to improve reliability and enhance communication range. The probability density function of channel coefficients is then derived. Next, a channel estimator based on Bayesian theory is proposed to acquire the modulus values of channel parameters and calculate its Bayesian Cramer–Rao Lower Bound. Finally, simulation results are provided to corroborate these theoretical studies. [ABSTRACT FROM AUTHOR]

Here, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of 1NT2+1NR2$\sqrt {\frac{1}{{{N_T}^2}} + \frac{1}{{{N_R}^2}}}$, where NT${N_T}$ and NR${N_R}$ are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications. [ABSTRACT FROM AUTHOR]

Data management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly. [ABSTRACT FROM AUTHOR]

Reactive jammers select jamming strategies according to the users' responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others' strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes. [ABSTRACT FROM AUTHOR]

The purpose of this work is to explore the decrease of total used power in cell‐free millimetre‐wave (mm‐Wave) massive multiple‐input multiple‐output (MIMO) systems, which can be regarded an essential technology for future wireless generations to improve system performance. One of the most important strategies for reducing total power consumption is to activate and deactivate radio frequency (RF) chains at each access point (AP) in the coverage region. Nonetheless, the optimization issue for this methodology is NP‐hard, and an exhaustive search method may be used to determine the ideal number of RF chains at each AP in the cell‐free network. Unfortunately, the exhaustive searching approach is prohibitively complicated, indicating that it is unworkable when there are a significant number of APs in the service region. Furthermore, present RF chain selection approaches prioritize decreasing consumed power and complexity at the price of system performance in terms of total possible rate. This research solves this issue by introducing a unique RF chain selection approach that combines Hungarian and genetic algorithms. The fact that the genetic algorithm (GA) can readily determine the ideal number of active RF chains, yet this technique generally entails significant complexity in large‐scale cell‐free networks, prompted this notion. As a result, the Hungarian method is used early in the GA to overcome the complexity problem while still retaining system performance. In addition, the suggested system employs a semi‐centralized hybrid beamforming architecture in which all analogue combiners for all APs are operated at a central processing unit using channel state information. In addition, each AP has a fully linked phase shifters network and restricted RF chains connecting to its antennas. Finally, simulation findings reveal that, when compared to state‐of‐the‐art techniques, the suggested approach achieves the maximum attainable rate and overall energy efficiency with a tolerable computational complexity. [ABSTRACT FROM AUTHOR]

Due to its open source and large user base, Android has emerged as the most popular operating system. Android's popularity and openness have made it a prime target for malicious attackers. Permissions have received great attention from researchers because of their effectiveness in restricting applications' access to sensitive resources. However, existing malware detection methods based on permissions are easily bypassed by inter‐application resource access. To address these issues, we combine inter‐application resource access‐related intent features with permission features. Besides, we designed a customized convolutional neural network using two squeeze‐and‐excitation blocks to learn the inherent relationships between multi‐type features. The two basic SE blocks perform squeezing operations based on average pooling and max pooling, respectively, to compute channel‐wise attention from multiple perspectives. We designed a series of experiments based on real‐world samples to evaluate the efficacy of the proposed framework. Empirical results demonstrate that our framework outperforms state‐of‐the‐art methods, achieving an accuracy of 96.29%, precision of 97.52%, recall of 94.63%, F1‐score of 96.06% and MCC of 92.60%. These promising experimental results consistently demonstrate that AMERDroid is an effective approach for Android malware detection. [ABSTRACT FROM AUTHOR]