The integration of sixth-generation (6G) networks with the Industrial Internet of Things (IIoT) promises unprecedented connectivity and intelligence for industrial applications. However, the massive scale of device deployment and dynamic spectrum requirements in 6G-IIoT systems pose significant challenges for reliable and trustworthy resource allocation. This paper proposes a multi-objective optimization approach that leverages cognitive radio technology and energy harvesting capabilities to ensure trustworthy resource allocation while maximizing data transmission and minimizing energy consumption. The framework incorporates imperfect spectrum sensing and interference constraints to guarantee reliable coexistence between primary and secondary networks, aligning with the reliability requirements of 6G-IIoT applications. This paper proposes a constraint repair strategy-based multi-objective artificial hummingbird algorithm (CPS-MOAHA) that effectively handles the complex constraints and provides Pareto optimal solutions. Simulation results demonstrate the superiority of the proposed algorithm in achieving better trade-offs between data transmission and energy efficiency compared to existing approaches.

The rapid development of drone technology has spurred significant interest in multi-UAV collaborative systems, particularly for complex tasks like cooperative target jamming. However, realizing their full potential is hindered by significant challenges, primarily stemming from uncertain three-dimensional (3-D) target positions and operational time constraints. These factors complicate crucial aspects like path planning and efficient task allocation, ultimately jeopardizing jamming mission success. Furthermore, the specific complexities introduced by uncertain 3-D target positions are often overlooked in existing cooperative jamming strategies. To address these issues, we propose cooperative multi-agent jamming techniques using reinforcement learning (RL) to maximize interference effectiveness against designated targets under target position uncertainty. Our methodology is based on a task framework that unifies the models of target position uncertainty, 3-D probabilistic perception for high-fidelity UAV sensing, and directional antenna interference to achieve optimal jamming. Within this framework, we formalize the task as a Markov Decision Process (MDP) and employ reinforcement learning to optimize collaborative jamming policies under target positions uncertainty. The proposed RL algorithm, by utilizing both individual and collaborator rewards, adaptively balances exploration and exploitation across different mission stages. This balance is achieved by adjusting the amplitude of noise used for action selection. We conducted simulation experiments with various UAV, target and no-fly zone configurations to validate the effectiveness of our proposed method, demonstrating its scalability and strong joint task performance in achieving jamming objectives.

Mobile Edge Computing (MEC) systems integrated with Reconfigurable Intelligent Surfaces (RIS) can significantly enhance task offloading efficiency and overall system performance in complex urban environments by enabling high-quality, energy-efficient communication. This paper focuses on urban scenarios with obstacles such as trees and high-rise buildings, and investigates the joint optimization of task offloading decisions, resource allocation, and RIS control in multi-user MEC systems. In the proposed framework, RIS panels are deployed on building exteriors to assist communication, while terminal devices (TDs) can either process tasks locally or offload them to MEC servers. The primary objective is to minimize the total system delay and energy consumption while ensuring task success rates and meeting both energy and latency constraints. To accurately model the environment, differentiated channel models are adopted: Rayleigh fading for occluded user-MEC links and Rician fading for unobstructed RIS-user and RIS-MEC links. A comprehensive joint optimization problem is formulated, encompassing task offloading decisions, user-server association, bandwidth allocation, computing resource scheduling, and RIS phase shift configuration. To solve this multidimensional optimization problem, we propose an MCEG-PSO algorithm-Particle Swarm Optimization enhanced with crossover, mutation, and evolution-ary game theory mechanisms. Simulation results demonstrate that the proposed algorithm effectively improves wireless link quality, reduces delay and energy consumption, and enhances task offloading efficiency in complex urban environments.

In multi-UAV wireless communication networks, limited spectrum resources and environmental interference jointly pose significant challenges. To address the limitations of spectrum resources and the uncertainty of interference distribution in complex interference environments, this paper proposes an intelligent cooperative scheduling approach based on local sensing assistance. Each UAV autonomously senses the local spectrum state-including channel availability and interference intensity-and collaboratively makes resource selection and allocation decisions through multi-agent coordination. Considering the uncertainty of spectrum dynamics and the coupling of inter-agent interference, we construct a sensing-driven spectrum scheduling model and introduce a Multi-Agent Dueling Double Deep Q-Network (MAD3QN) to enable decentralized spectrum sharing and conflict avoidance without relying on centralized control. The proposed method emphasizes robust cooperative scheduling mechanisms under adversarial interference conditions, improving communication efficiency and task resilience in dynamic environments. Simulation results demonstrate that our method outperforms existing schemes in terms of spectrum utilization, system throughput, and anti-interference capability, validating its effectiveness for efficient cooperative communication in dynamic spectrum environments.

Unmanned aerial vehicles (UAVs) are increasingly deployed in next-generation communication networks due to their flexibility, low cost, and ability to provide rapid connectivity in dynamic environments. In such scenarios, efficient path planning is essential to ensure reliable communication links, minimize energy consumption, and improve overall mission performance. Traditional reinforcement learning approaches, such as the multi-agent deep deterministic policy gradient (MADDPG), have demonstrated strong performance in multi-UAV coordination and decision making. However, their high computational complexity and large-scale neural architectures restrict their deployment on resource-constrained UAV platforms with limited onboard processing and energy budgets. To address this challenge, we propose a lightweight path planning framework that incorporates knowledge distillation into the actor-critic structure of MAD-D PG. In the proposed method, a large teacher network is first trained to learn optimal strategies in complex communication environments. The knowledge learned by the teacher is then transferred to a compact student network, which significantly reduces the number of parameters and inference latency. This design enables the UAVs to achieve near real-time decision making while maintaining high planning accuracy. Extensive simulation experiments validate the effectiveness of the proposed approach. Results show that the distilled model achieves comparable or even improved performance compared to the original MADDPG framework, while reducing computational overhead and convergence time. These advantages make the proposed method highly suitable for real-time UAV communication scenarios, especially in dynamic and resource-limited environments. The study provides a promising direction for integrating lightweight deep reinforcement learning with UAV communication and path planning systems.

In this work, we introduce an MLP-Mixer-based unrolling UAMPSBL approach for near/far-field structured channel estimation in terahertz extremely large-scale antenna arrays (ELAA) systems. The MLP-Mixer-based unrolling UAMPSBL approach can effectively alleviate the diverge problem of the original UAMP-SBL algorithm. Moreover, the MLP-Mixer-based unrolling UAMPSBL approach has a more simplified structure and suite for block-sparse structures compared to the CNN model. Simulation results show that the proposed MLP-Mixer-based unrolling UAMPSBL approach significantly outperforms the benchmark algorithms for near/far-field channel estimation problem.

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this article, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy autonomous aerial vehicles (AAVs) to interfere with the targets. First, we established a AAV swarm task scheduling optimization model after considering constraints, such as target threat range, priority of reconnaissance and interference tasks, interference task time, and AAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a nondominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve AAV resource scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms.

Sparsity-based joint active user detection and channel estimation (JADCE) algorithms are crucial in grant-free massive machine-type communication (mMTC) systems. The conventional compressed sensing algorithms are tailored for noncoherent communication systems, where the correlation between any two measurements is as minimal as possible. However, existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems, especially with a small number of pilot subcarriers. To tackle this challenge, we formulate JADCE as a joint sparse signal recovery problem, leveraging the block-type row-sparse structure of millimeter-wave (mmWave) channels in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Then, we propose an efficient difference-of-convex function algorithm (DCA) based JADCE algorithm with multiple measurement vector (MMV) frameworks, promoting the row-sparsity of the channel matrix. To mitigate the computational complexity further, we introduce a fast DCA-based JADCE algorithm via a proximal operator, which allows a low-complexity alternating direction multiplier method (ADMM) to resolve the optimization problem directly. Finally, simulation results demonstrate that the two proposed difference-of-convex (DC) algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.

Unmanned aerial vehicle (UAV) swarm shows great potential as an effective tool for target tracking through completing complex tasks by collaboration of heterogeneous UAVs. However, UAV swarm scheduling faces challenges with poor quality communication and obstacles, especially in communication denial environment with multiple obstacles. To overcome these challenges, first, this paper proposes a scheduling slot model which divides the scheduling process into multiple time slots, allowing UAVs to communicate in communication slots while predicting instead of communication in communication denial slots. In communication denial slots, this model utilizes route fitting and two-stage Kalman filtering for UAV location prediction and optimizes UAV scheduling to align with predicted positions. In enabled slots, this model corrects position deviations to obtain precise UAV locations manually. Then, we propose an obstacle avoidance strategy to facilitate swarm scheduling for target searching under communication constraints. The obstacle avoidance strategy simplifies obstacles as regular hexagons and facilitates the determination of UAV avoidance routes by introducing intermediary points. Finally, to optimize UAV scheduling strategy, we propose a region co-evolution algorithm (RCEA), which emphasizes the collaboration among diverse individuals or populations. RCEA adopts area evaluation and Pareto strategy to enhance scheduling efficiency with following three steps. RCEA divides the overall scheduling region into multiple sub-regions, generates the foundational solution pool through the implementation of the area evaluation or Pareto strategy, and then proceeds to execute the region cooperation process base on the foundational solution pool. Simulation experiments are conducted to validate the performance of human-UAV interaction scheduling model with proposed scheduling methods and obstacle avoidance strategy. The simulation results demonstrate that RCEA outperforms other scheduling algorithms for UAV swarm in communication denial environment with multiple obstacles. Note to Practitioners—This paper addresses challenges inherent in real-world application scenarios, and the proposed algorithm has the potential to bring many benefits to practitioners. Firstly, the scheduling slot model can be applied not only to UAV swarm for target searching but can also be extended to other swarm devices for complex tasks with collaboration relying on communication support while facing poor quality communication or obstacles. Secondly, the proposed RCEA focuses on collaboration and region partitioning, the algorithm demonstrates remarkable scalability, effectively tackling challenges across diverse scales and complexities. Thirdly, the experimental scenarios can serve as a validation dataset for other peer researchers, and although the simulation experiment is based on a 2D movement model, this study still offers theoretical support applicable to a 3D movement model.

In the academic research field, identifying suitable collaboration partners and selecting appropriate journals for publication remain significant challenges for researchers. Existing academic recommendation systems often fail to provide personalized, accurate, and efficient recommendations. To address these issues, this article proposes an innovative academic recommendation system that incorporates multisimilarity features. By constructing an academic collaboration network and optimizing the transfer probability matrix to reflect scholars’ relationships, the system captures scholars’ collaborative tendencies and potential connections. A key innovation of this work is the proposed Muls-IRWR algorithm, which improves traditional random walk with restart (RWR) by integrating various similarity measures. Using a subset of the DBLP citation data, we develop our academic collaboration network to calculate precise scholar similarities. Experimental results demonstrate that our system significantly outperforms existing models in terms of recommendation accuracy and efficiency, highlighting its practical value and potential for use in real-world academic applications.

In existing graph data, the connection relationships often exhibit uniform weights, leading to the model aggregating neighboring nodes with equal weights across various connection types. However, this uniform aggregation of diverse information diminishes the discriminability of node representations, contributing significantly to the over-smoothing issue in models. In this paper, we propose the Flexible Graph Neural Diffusion (FGND) model, incorporating latent class representation to address the misalignment between graph topology and node features. In particular, we combine latent class representation learning with the inherent graph topology to reconstruct the diffusion matrix during the graph diffusion process. We introduce the sim metric to quantify the degree of mismatch between graph topology and node features. By flexibly adjusting the dependency level on node features through the hyperparameter, we accommodate diverse adjacency relationships. The effective filtering of noise in the topology also allows the model to capture higher order information, significantly alleviating the over-smoothing problem. Meanwhile, we model the graphical diffusion process as a set of differential equations and employ advanced partial differential equation tools to obtain more accurate solutions. Empirical evaluations on five benchmarks reveal that our FGND model outperforms existing popular GNN methods in terms of both overall performance and stability under data perturbations. Meanwhile, our model exhibits superior performance in comparison to models tailored for heterogeneous graphs and those designed to address oversmoothing issues.

Unmanned aerial vehicles (UAVs) can be deployed in the areas where traditional network infrastructure is insufficient or absent because of flexibility and collaboration. The deployment of edge intelligence on UAVs in UAV-assisted mobile-edge networks significantly enhance data processing efficiency, which is a critical factor for applications requiring delay-sensitive data processing in the areas mentioned above. However, the limited energy capacity poses a challenge when running complex deployment algorithms. Therefore, lightweight network model is essential for deployment algorithms, as it significantly reduces energy and time consumption. In this article, we propose a cooperative framework-based knowledge distillation to compressed deep neural network (DNN). The subnetworks collaborate to train interactive node parameters, resulting in the optimal evolution of the student network. Then, we introduce a novel result-driven model training approach for simulation data sets. To further enhance efficiency and significantly reduce overall latency, we meticulously refine the internal architecture of the knowledge distillation algorithm. We incorporate a collaborative evolution mechanism into the core of the algorithm, utilizing multinetwork and subnetwork learning to facilitate knowledge transfer, and incorporate some optimization mechanisms into the framework. Finally, we perform a series of experiments to acquiredata sets and conduct algorithm simulation analysis to evaluate the proposed method. The results demonstrate that our work achieves good research results.

In this letter, we present a novel strategy for designing zero-redundancy arrays exploiting the concept of the sum-difference coarray. This approach aims to eliminate redundant lags between the difference and sum coarrays, resulting in a more efficient array design. Consequently, we introduce two new sparse array configurations, named Zero-Redundancy Sparse Array (ZRSA-I and ZRSA-II), for direction-of-arrival (DOA) estimation. These arrays have been proven to possess the zero-redundancy property and offer more uniform degrees of freedom (uDOFs) compared to many existing sparse arrays. Furthermore, we propose the Sum-Difference Translation Invariance Criteria (SDTIC) to mitigate the mutual coupling (MC) effects in the arrays. Theoretical propositions and simulation results confirm the exceptional performance of the proposed ZRSAs, highlighting their potential for enhancing angle resolution and estimation accuracy.

Compared with the traditional transportation data, the transportation big data (TBD) is under the background of “Internet + traffic.” It is a great challenge for analyzing and processing TBD because of its complex and unstructured characteristics, such as sequence, strong relevance, accuracy, and closed loop. This Special Issue provides high-quality and up-to-date technology related to the application of SP and AI into TBD and their applications in the COVID-19 scenario and beyond and serves as a forum for researchers all over the world to discuss their works and recent advancements in the field, especially for defensing COVID-19 in public transportation.

Network embedding aims to represent nodes with low dimensional vectors while preserving structural information. It has been recently shown that many popular network embedding methods can be transformed into matrix factorization problems. In this paper, we propose the unifying framework “Z-NetMF,” which generalizes random walk samplers to Z-Laplacian graph filters, leading to embedding algorithms with interpretable parameters. In particular, by controlling biases in the time domain, we propose the Z-NetMF-t algorithm, making it possible to scale contributions of random walks of different length. Inspired by node2vec, we design the Z-NetMF-g algorithm, capturing the random walk biases in the graph domain. Moreover, we evaluate the effect of the bias parameters based on node classification and link prediction tasks. The results show that our algorithms, especially the combined model Z-NetMF-gt with biases in both domains, outperform the state-of-art methods while providing interpretable insights at the same time. Finally, we discuss future directions of the Z-NetMF framework.

This paper considers a joint active user detection and channel estimation (JADCE) problem in the grant-free massive machine-type communications (mMTC) circumstances. Specifically, we exploit the millimeter-wave (mmWave) channel in the uplink with the continuous angular domains based on massive multi-input multi-output systems. The sporadic communication nature of the mMTC scenario and the inherent angular domain sparsity of the mmWave channel make the space-angle domain sparsity of the mmWave channel even more serious. Hence, the JADCE problem is formulated as a convex optimization problem under the gridless reweighted atomic norm minimization (RAM) framework in a multiple measurement vector settings (MMV), which can enhance the sparsity in the continuous anular domains. Moreover, RAM has nature of Semidefinite programming (SDP) which can be computed by CVX solver. Meanwhile, to reduce the computational complexity of CVX, we design a fast alternating direction method of multipliers approach to settle the SDP formulation. The simulation results demonstrate that our proposed method has achieved excellent estimation performance and a substantial reduction in computational complexity compared with conventional JADCE methods.

Recent years have witnessed significant advances in graph contrastive learning (GCL), while most GCL models use graph neural networks as encoders based on supervised learning. In this work, we propose a novel graph learning model called GraphTL, which explores self-supervised teaching and learning of representations on graphs. One critical objective of GCL is to retain original graph information. For this purpose, we design an encoder based on the idea of unsupervised dimensionality reduction of locally linear embedding (LLE). Specifically, we map one iteration of the LLE to one layer of the network. To guide the encoder to better retain the original graph information, we propose an unbalanced contrastive model consisting of two views, which are the learning view and the teaching view, respectively. Furthermore, we consider the nodes that are identical in muti-views as positive node pairs, and design the node similarity scorer so that the model can select positive samples of a target node. Extensive experiments have been conducted over multiple datasets to evaluate the performance of GraphTL in comparison with baseline models. Results demonstrate that GraphTL can reduce distances between similar nodes while preserving network topological and feature information, yielding better performance in node classification.

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN’s capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets.

Smart city enters the new 3.0 era, and Internet of Things (IoT) perform as the urban neural network in smart city. In the industrial areas of smart city, the IoT focuses on industrial applications, such as logistics and environmental monitoring. In this work, an auxiliary position analysis framework composed of IoT and distributed mobile-edge computing (MEC) is proposed to analyze the position of vehicles in the industrial areas of smart city. In the proposed framework, IoT are deployed by multiple unmanned aerial vehicles (UAVs) equipped with uniform linear array (ULA), which receives the signal emitted by vehicles for obtaining the direction of arrival (DOA), and the distributed MEC provides computing and synchronization services to auxiliary positioning. The DOA estimation is a key issue for auxiliary positioning in the proposed framework. To realize DOA estimation with unknown mutual coupling (MC) existing in IoT nodes, a novel block sparse Bayesian learning (SBL) algorithm is developed. In the developed algorithm, the unknown MC existing between sensors in each IoT nodes is first fused with the signal by parameterizing steering vector. Then, a block SBL (BSBL) procedure is presented to perform DOA estimation by using the inherent block sparse structure in the equivalent signal obtained after fusion. Benefiting from the fusion of MC and signal, the developed DOA estimation algorithm does not require a separate estimation of the unknown MC and also does not cause the loss of the array aperture. Based on the DOA estimation information, the position of vehicles in the industrial environment is effectively analyzed through weighted multiple cross-locations. Synthetic data set simulation is carried out to verify that the vehicle positions in smart city can be efficiently analyzed and estimated based on the presented framework and algorithm.

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications.

The essence of connection in vehicle network is the social relationship between people, and thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can be formed. The information collected by VSNs can be used for context prediction of autonomous vehicles. Multivariate relations are common in square connected relations caused by geographic characteristics in VSNs. They can effectively reflect the high-order structural features of the network dataset. It is necessary to exploit the multivariate relations of VSNs to improve the performance of context prediction. However, The representation of entity-relationes in the network often adopts a binary form, and the existing graph learning methods rely on the neighborhood information of nodes to achieve the aggregation or diffusion of information. Using this to represent multivariate relations will result in partial omissions or even complete loss of valuable information, which ultimately affects the learning effect of learning methods. In order to better understand the social behavior of the VSNs, this paper uses the network motif to implement the representation of the multivariate relations in the network, and proposes the graPh learnIng with moTif mAtrix (PITA) method. This method can be used as a preprocessing step for the measurement strategy of the relations in VSNs and the graph learning, which can mine the information in VSNs and improve the accuracy of the original graph learning method by the multivariate relation information. We performed experiments on 6 network datasets. The experimental results show that in the node classification task, the baseline method modified by the PITA method has a higher classification accuracy than the original method.

In this paper, a fast optimization-based off-grid channel estimation method is proposed for millimeter wave (mmWave) or sub-terahertz(THz) cellular systems. Different from most existing works concerned with compressed sensing (CS) or off-grid methods, we proposed a novel first-order convex off-grid channel estimation approach to relieve the resolution loss from the angle quantization. Moreover, our proposed method is approximate inverse-free without costly matrix inversion. The numerical results demonstrate the effectiveness of the proposed FOG method, which achieves superior performance compared to CS-based approach and existing off-grid algorithm in terms of computational complexity and estimation accuracy.

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.

It is challenging to acquire accurate channel knowledge for sufficient beamforming gain because of the large number of antennas. In this paper, a deep learning aided channel estimation algorithm is proposed for mmWave cellular systems, in which both the base station (BS) and mobile station (MS) gain directional beamforming by employing large antenna arrays. We consider the off-grid (OG) mmWave channel estimation and propose a deep network architecture for solving this problem, which is different from most existing works concerning the compressed sensing (CS)-based channel estimation. Firstly, the off-grid channel model is used to relieve the basis mismatch issue by exploiting first-order Taylor-series approximation of the array manifold taken on a fixed grid of both BS and MS. Secondly, a new formulation of this off-grid channel estimation problem is solved by using a low computational complexity alternating direction method of multipliers (ADMM)-based algorithm, dubbed ADMM-OG algorithm. Thirdly, the idea of algorithm unrolling guides us to design a deep network architecture ADMM-OGChannelNet corresponding to the ADMM-OG algorithm for the mmWave channel estimation. Based on the interpretability of this deep network architecture, the optimal parameters of this network can be learned without tuning in a hand-crafted way. The Cramér-Rao bound (CRB) of the off-grid channel model is derived for performance comparison as well. Finally, from the simulation results, we can verify that the ADMM-OGChannelNet has better estimation accuracy and relatively low computational complexity compared with the state-of-the-art algorithms.

Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

Scientific collaboration has become a universal phenomenon in recent years. Meanwhile, scholars tend to hunt for surprising collaborators for broadening their horizons. Serendipity initially denotes the fortunate discovery. Although a lot of literature is available on the topic of serendipity, little research has investigated serendipity in scientific collaborations. The objective of this paper is to identify serendipitous scientific collaborators of target scholars based on their collaboration data. First, we induce the definition of serendipitous scientific collaborators by three components, which are relevance, unexpectedness, and value, respectively. They are quantified as three intuitive indices corresponding to the network proximity, topic diversity, and collaborator influence, respectively. Second, we propose a classification model, called RUVMod, to classify all collaborators based on the analysis of three indices in definition. The serendipitous collaborator has lower network proximity, higher topic diversity and higher influence than his/her target scholar relatively. Finally, we cluster all collaborators via Self Organizing Maps and identify the serendipitous collaborator class according to the classes divided in our RUVMod. We apply our definition to the scientific collaborators extracted from DBLP dataset. The evaluation from the serendipity-based metrics suggests that RUVMod is effective in identifying serendipitous scientific collaborators.

Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor large-scale forest areas, and they are weak in predicting the specific areas of forest fires. In this article, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial Internet of Things (IIoTs), in which the data collected by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective is to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning-based cooperative particle swarm optimization (LCPSO) algorithm with a Markov random field (MRF)-based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into subsolution spaces according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple subsolution spaces cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.

The papers in this special section focus on advanced signal processing and artificial intelligence (AI) technologies for industrial Big Data (IBD) powered by Industry 4.0. Modern industry has evolved from the traditional manufacturing industry to digital and intelligent industry. Huge amount of complex real-time data are generated from the thousands of industrial sensors in physical and man-made environments. Industrial big data (IBD) afford us an unprecedented opportunity to obtain an in-depth understanding of Internet of Things and facilitate data-driven approaches for industrial optimization and scheduling. The papers in this section collect the latest ideas and research on advanced signal processing and artificial intelligence (AI) technologies for IBD.

Industrial advancement has introduced a significant impact on ecological balance and natural resources; thus, pollution monitoring using smart sensors in the industrial Internet of Things (IIoT) has recently attracted growing interests in the era of Industry 4.0. However, the effective detection and location of polluted areas remains a major challenge for collecting and processing a massive amount of sensor data in the IIoT especially in far sea, danger zone, and mountain zone, where there is no communication infrastructure. In this article, we establish a satellite-terrestrial framework to detect and locate industrial pollution areas by integrating the satellite with the IIoT, and the massive amount of sensor data can be delivered to the satellite via a ground base station. Local attribute detection inspired by recent advances in graph signal processing provides a promising way for solving this problem. A subgraph can be formed by grouping the vertices with identical attributes, and these vertices can be easily separated from other vertices based on local attribute detection. In this article, new methods based on local attribute detection are proposed to detect and locate pollution areas. First, a stable wavelet statistic (SWS) is proposed by modeling the classical wavelet basis as a graph-based wavelet basis. To improve the generalization ability of the SWS, a new cluster center discovery method is proposed to minimize the distance between any vertex and the remaining vertices of the same cluster. Second, a smooth scan statistic is proposed by introducing a new constraint to simplify the problem formulation of the likelihood ratio test. The effectiveness of the two graph-based statistical methods is evaluated using real datasets for detecting and locating industrial pollution.

Unmanned aerial vehicle (UAV) swarms-enabled mobile edge computing system can be deployed in critical industrial zones for monitoring. Meanwhile, its malicious use may bring great threat to the security, and the accurate detection, and localization are important. UAV swarms show characteristics of the high density, small radar cross section, far range, and time-varying motion, and have posed formidable challenges to the accurate detection and localization. In this article, the accurate detection and localization of UAV swarms are investigated, and an effective method is proposed based on the Dechirp-keystone transform, and frequency-selective reweighted trace minimization. It inherits high robustness of the coherent long-time integration technique and superresolution of the gridless sparse technique. Mathematical analyzes and numerical simulations validate its superiorities in accurate detection and localization of UAV swarms.

The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.

In this paper, a novel system architecture including a massive multi-input multi-output (MIMO) or a reconfigurable intelligent surface (RIS) and multiple autonomous vehicles is considered in vehicle location systems. The location parameters of autonomous vehicles can be estimated based on the deep unfolding technique, which is a recent advance of deep learning. Traditional vehicle location methods such as the global position system (GPS) can only locate the target vehicles with relatively low accuracy. The super resolution cannot be achieved when two vehicles are too close, which means that the safety incidents exist when autonomous vehicles are deployed in future intelligent transportation systems (ITS). Different from the existing massive MIMO or RIS equipped with a regular array such as uniform rectangular array (URA) and uniform circular array (UCA), we exploit a massive MIMO or a RIS equipped with a conformal array extended from traditional regular array. First, the rotation from the global coordinate system to the local coordinate system is achieved based on geometric algebra. Second, 2D-DOA estimation of autonomous vehicles is modeled as a novel block sparse recovery problem. Third, the deep network architecture SBLNet is implemented to learn the nonlinear characteristic from the DOAs of autonomous vehicles and the data received by massive MIMOs or RISs. The 2D-DOA and polarization parameters can be estimated based on SBLNet with relatively low computational complexity. Simulation results demonstrate that SBLNet performs better than the state-of-the-art methods in terms of estimation accuracy and successful probability. The SBLNet is also suitable for the practical scenario considering fast moving autonomous vehicles, while, the traditional block sparse recovery methods fail in this complex scenario.

Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.

In this article, a multiple signal classification (MUSIC) based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signals in three-dimensional (3-D) millimeter wave polarized massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signals or the DOA for non-circular signals by using spectrum search. By contrast, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. Moreover, only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, the DOA estimation can be achieved based on the derivation principle. Then the closed-form expression of the polarization estimation can be obtained based on the chain rule of the derivation w.r.t. the polarization parameters. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC has much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when large-scale/massive MIMO systems are employed.

An advanced source number estimation (SNE) algorithm based on both fuzzy C‐means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one‐dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal‐to‐noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.

Phase retrieval problem is an nonlinear inverse problem of recovering real- or complex-valued signal from quadratic measurements, which arises in various applications. The best-known algorithms for solving this problem are non-convex methods starting with spectral initializers that provide an initial point within a local basin sufficiently close to the target signal. This paper introduces a simple method, called weighted null vector initialization (WNI), which can be used to compute accurate initialization vectors for solving non-convex phase retrieval. The introduced WNI method is more robust against measurement noise and outperforms the best spectral initializer, and null initializer. Simulation results are provided to illustrate the effectiveness of the proposed method.

The recently developed technique of wireless power transfer (WPT) provides a promising way to charge the wireless sensor networks (WSNs) of cognitive industrial Internet of Things (IoT) deployed in areas that are difficult for humans to access. Previous work has focused on the power allocation strategy at the wireless node level. However, the priority among different modes in an identical wireless node has not been taken into consideration, and different modes equipped with different types of batteries accomplish different tasks in an identical wireless node. One challenging scenario is rechargeable WSNs with a large number of wireless nodes. In this article, we aim to optimize the power allocation strategy in priority constraint WPT systems with a large number of wireless nodes. Traditional WPT systems consist of a rechargeable WSN and a mobile charger, which are deployed for charging wireless nodes in a wireless manner. However, the constructed WPT system consists of a rechargeable WSN and multiple mobile chargers with adequate power, which can charge wireless nodes simultaneously. Each solution of the power allocation strategy can be represented as one disjunctive graph, and the critical path (CP) in the disjunctive graph is the core factor in determining the final maximum cost. Thus, we propose a decomposition strategy that can identify the interacting variables based on the CP by exploiting the perturbation technique. Then, the decomposed subcomponents are cooperatively evolved by adopting a cooperative evolutionary algorithm (CEA). The proposed CP-based grouping strategy combined with CEA is named CPCEA. Three state-of-the-art methods are tested and compared with CPCEA, and three scales of datasets are considered. The experimental results demonstrate the validity of CPCEA.

In this paper, a sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to jointly extract the angle and range estimates of targets. Firstly, the angle estimates of targets are calculated by transmitting a pulse with a zero frequency increment and employing the improved l 1 -SVD method. Subsequently, the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment. Specifically, after obtaining the angle estimates of targets, we perform dimensionality reduction processing on the overcomplete dictionary to achieve the automatically paired range and angle in range estimation. Grid partition will bring a heavy computational burden. Therefore, we adopt an iterative grid refinement method to alleviate the above limitation on parameter estimation and propose a new iteration criterion to improve the error between real parameters and their estimates to get a trade-off between the high-precision grid and the atomic correlation. Finally, the proposed algorithm is evaluated by providing the results of the Cramér-Rao lower bound (CRLB) and numerical root mean square error (RMSE).

In this paper, a novel off-grid channel model is considered for millimeter-wave (mmWave) cellular systems, where both the BS and MS employ large antenna arrays for directional beamforming. Accurate acquisition of channel knowledge for sufficient beamforming gain is challenging due to the large number of antennas and low coherence time. Different from most existing studies that are concerned with compressed sensing (CS)-based channel estimators, a novel convex formulation without semidefinite programming (SDP) relaxation is proposed for the off-grid/super-resolution channel estimation and then a low complexity solver based on alternating direction method of multipliers (ADMM) is developed. The simulation results demonstrate that the proposed off-grid method has better estimation accuracy and lower computational complexity compared with other state- of-the-art algorithms.

Local administrations and governments aim at leveraging wireless communications and Internet of Things (IoT) technologies to manage the city infrastructures and enhance the public services in an efficient and sustainable manner. Furthermore, they strive to adopt smart and cost-effective mobile applications to deal with major urbanization problems, such as natural disasters, pollution, and traffic congestion. Mobile crowdsourcing (MCS) is known as a key emerging paradigm for enabling smart cities, which integrates the wisdom of dynamic crowds with mobile devices to provide decentralized ubiquitous services and applications. Using MCS solutions, residents (i.e., mobile carriers) play the role of active workers who generate a wealth of crowdsourced data to significantly promote the development of smart cities. In this paper, we present an overview of state-of-the-art technologies and applications of MCS in smart cities. First, we provide an overview of MCS in smart cities and highlight its major characteristics. Second, we introduce the general architecture of MCS and its enabling technologies. Third, we study novel applications of MCS in smart cities. Finally, we discuss several open problems and future research challenges in the context of MCS in smart cities.

As a promising research area in Internet of Things (IoT), Internet of Vehicles (IoV) has attracted much attention in wireless communication and network. In general, vehicle localization can be achieved by the global positioning systems (GPSs). However, in some special scenarios, such as cloud cover, tunnels or some places where the GPS signals are weak, GPS cannot perform well. The continuous and accurate localization services cannot be guaranteed. In order to improve the accuracy of vehicle localization, an assistant vehicle localization method based on direction-of-arrival (DOA) estimation is proposed in this paper. The assistant vehicle localization system is composed of three base stations (BSs) equipped with a multiple input multiple output (MIMO) array. The locations of vehicles can be estimated if the positions of the three BSs and the DOAs of vehicles estimated by the BSs are known. However, the DOA estimated accuracy maybe degrade dramatically when the electromagnetic environment is complex. In the proposed method, a sparse Bayesian learning (SBL)-based robust DOA estimation approach is first proposed to achieve the off-grid DOA estimation of the target vehicles under the condition of nonuniform noise, where the covariance matrix of nonuniform noise is estimated by a least squares (LSs) procedure, and a grid refinement procedure implemented by finding the roots of a polynomial is performed to refine the grid points to reduce the off-grid error. Then, according to the DOA estimation results, the target vehicle is cross-located once by each two BSs in the localization system. Finally, robust localization can be realized based on the results of three-time cross-location. Plenty of simulation results demonstrate the effectiveness and superiority of the proposed method.

The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades.

In recent years, many schemes have been proposed to disseminate Emergency Warning Messages (EWMs) in VANETs. However, various problems such as broadcast storm, hidden terminal and connectivity issues still persist to reduce the efficacy of these systems. In this paper, we propose a novel Social Utility-based Dissemination Scheme (SUDS) for Emergency Warning Messages in Vehicular Social Networks (VSNs). We utilize social properties of nodes such as centrality, interests and friendships to mitigate broadcast storm and hidden terminal problems. Furthermore, we employ the hybrid architecture of VSNs to solve connectivity and contact duration-related issues in sparse and high mobility environments. For this purpose, we devise a dual-strategy-based mechanism, where vehicles communicate with each other in a distributed or centralized manner according to the required situation. In order to evaluate the performance of proposed scheme, we have conducted extensive experiments for a highway scenario under varying vehicular density, vehicular speed and distance, in comparison with the state-of-the-art dissemination schemes. Simulation results have demonstrated the superiority of SUDS over the compared protocols in terms of delivery ratio, transmission delay and total number of transmissions. We have also demonstrated the positive effects of hybrid architecture of VSNs on various network parameters.

In the new era of integrated computing with intelligent devices and system, moving aerial targets can be tracked flexibly. The estimation performance of traditional matched filter-based methods would deteriorate dramatically for multiple targets tracking, since the weak target is masked by the strong target or the strong sidelobes. In order to solve the problems mentioned above, this paper aims at developing a joint range-Doppler-angle estimation solution for an intelligent tracking system with a commercial frequency modulation radio station (noncooperative illuminator of opportunity) and a uniform linear array. First, a gridless sparse method is proposed for simultaneous angle-range-Doppler estimation with atomic norm minimization. Based on the integrated computing, multiple workstations or servers of the data process center in the intelligent tracking system can cooperate with each other to accelerate the data process. Then a suboptimal method, which estimates three parameters in a sequential way, is proposed based on grid sparse method. The range-Doppler of each target is iteratively estimated by exploiting the joint sparsity in multiple surveillance antennas. A simple beamforming method is used to estimate the angles in turn by exploiting the angle information in the joint sparse coefficients. Simulation result and real test show that the proposed solution can effectively detect weak targets in an iterative manner.

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low‐pass component of network traffic that describes the long‐range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low‐pass component. Otherwise, for the remaining high‐pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

In this paper, an improved direction-of-arrival (DOA) estimation algorithm for circular and non-circular signals is proposed. Most state-of-the-art algorithms only deal with the DOA estimation problem for the maximal non-circularity rated and circular signals. However, common non-circularity rated signals are not taken into consideration. The proposed algorithm can estimates not only the maximal non-circularity rated and circular signals, but also the common non-circularity rated signals. Based on the property of the non-circularity phase and rate, the incident signals can be divided into three types as mentioned above, which can be estimated separately. The interrelationship among these signals can be reduced significantly, which means the resolution performance among different types of signals is improved. Simulation results illustrate the effectiveness of the proposed method.

With the development of wireless communication, industrial wireless sensor networks (IWSNs) plays an important role in monitoring and control systems. In this paper, we extend the application of IWSNs into High Frequency Surface-Wave Radar (HFSWR) system. The traditional antenna is replaced by mobile IWSNs. In combination of the application precondition of super-directivity in HF band and circular topology of IWSNs, a super- directivity synthesis method is presented for designing super-directivity array. In this method, the dominance of external noise is ensured by constraining the Ratio of External to Internal Noise (REIN) of the array, and the desired side lobe level is achieved by implementing linear constraint. By using this method, the highest directivity will be achieved in certain conditions. Using the designed super directive circular array as sub-arrays, the compact receive antenna array is constructed, the purpose of miniaturization is achieved. Simulation verifies that the proposed method is correct and effective, the validity of the proposed method has been proved.

In this paper, an improved localization method named three-uniform-linear-array localization is proposed for patient track systems. Three receivers adopting a smart antenna technique cooperate with each other to locate the patients using the angulation positioning method. In order to be able to track patients in environment with high patient density, a high-resolution direction-of-arrival (DOA) estimation algorithm for the coexistence of noncircular and circular signals is proposed. First, the maximal and common noncircularity rated signals are preliminarily estimated. Second, based on the noise space block matrix, the DOAs of these signals are re-estimated with high accuracy. Then, the covariance matrix of the maximal and common noncircularity rated signals is reconstructed. The contributions of these signals are eliminated after performing a subtraction operation on the covariance matrix of the received data and only those of circular signals remain. Finally, the DOAs of circular signals are obtained. Results of simulations and real tests demonstrate the effectiveness and performance of the proposed algorithm.