Recent years have seen the rapid development of long-range wide area network (LoRaWAN) operating in region-specific sub-GHz frequency bands (e.g., 868 MHz in Europe and 915 MHz in North America). To achieve global deployment, LoRaWAN has been extended to operate in the globally available 2.4 GHz unlicensed band. However, this shift exposes LoRaWAN to significant interference from coexisting Wi-Fi networks, which share the same band and typically transmit at much higher power levels. To address this problem, this paper presents InMAC, an interference-aware medium access control (MAC) protocol designed to improve coexistence between LoRaWAN and Wi-Fi networks. To the best of our knowledge, InMAC is the first MAC protocol specifically tailored to mitigate Wi-Fi interference for 2.4 GHz LoRaWAN. InMAC enhances LoRaWAN communication by probabilistically exploiting the silent time in Wi-Fi traffic, leveraging a Wi-Fi traffic profiling mechanism at LoRaWAN gateways and a packet length adaptation strategy at end devices. In addition to mitigating external interference from Wi-Fi, InMAC also tackles internal interference caused by signal collisions among LoRaWAN end devices. It incorporates a novel channel access mechanism based on Channel Activity Detection, a carrier-sensing technique adapted specifically for LoRaWAN. Experimental results demonstrate that InMAC reduces both external Wi-Fi interference and internal LoRaWAN collisions, achieving up to a 111% throughput boost over 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.

Unmanned aerial vehicle (UAV)-borne radar systems have emerged as a core technology for wide-area, high-efficiency target localization and monitoring. However, traditional UAV-borne radar systems are typically configured with uniform linear scalar sensor arrays, in which mutual coupling effects and limited aperture signiffcantly limit the positioning performance. In this paper, a polarization-assisted UAV-borne radar localization system is developed. This system comprises UAVs outfitted with coprime vector sensor arrays. Furthermore, a tensor-based direction-of-arrival (DOA) estimation algorithm leveraging atomic norm minimization (ANM-Tensor-DOA) and a polarization-assisted cross-localization (PACL) technique are introduced. Specifically, an ANM-based optimization task is established based on the cross-correlation matrices of the polarization components to reconstruct the Hermitian Toeplitz-structured noiseless information matrix and the measurement matrix. Subsequently, an augmented noiseless tensor model is established, allowing DOA to be estimated via tensor decomposition. Then the polarization states are determined via closed-form expressions derived from the measurement matrix. Ultimately, based on the known DOA and polarization information, the target’s location can be determined using the proposed PACL algorithm. Simulation results indicate that, compared to more recent methods, the proposed approach delivers improved parameter estimation performance and high-precision localization capabilities.

Intelligent reflecting surfaces (IRS) enable radar sensing in blocked environments by reconfiguring propagation and creating virtual apertures, which is crucial for non-line-of-sight (NLoS) localization. This work addresses high-accuracy direction-of-arrival (DoA) estimation in semi-passive IRS-assisted sensing. We introduce a virtual-domain lifting that vectorizes the received echoes and induces a structured atomic set, leading to an atomic norm minimization (ANM) formulation. The ANM estimator is formulated as a semidefinite program (SDP) via convex relaxation, and we develop an accelerated proximal gradient (APG) solver that leverages the problem structure and avoids interior-point steps, resulting in substantial computational savings. Compared with spatial-domain and grid-based approaches, the transformed-domain estimator delivers an optimal accuracy-complexity tradeoff. It achieves gridless (ANM-level) high resolution while reducing runtime. Extensive simulations across array sizes, transmit power, and IRS configurations confirm accuracy, robustness to off-grid mismatch, and scalability, demonstrating the practicality of the proposed method for IRS-enabled NLoS sensing in complex environments.

In this paper, channel estimation (CE) for up-link hybrid-field communications involving multiple Internet of Things (IoT) devices assisted by an active intelligent reflecting surface (IRS) is investigated. Firstly, to reduce the complexity of near-field (NF) channel modeling and estimation between IoT devices and active IRS, a sub-blocking strategy for active IRS is proposed. Specifically, the entire active IRS is divided into multiple smaller sub-blocks, so that IoT devices are located in the far-field (FF) region of each sub-block, while also being located in the NF region of the entire active IRS. This strategy significantly simplifies the channel model and reduces the parameter estimation dimension by decoupling the high-dimensional NF channel parameter space into low dimensional FF sub channels. Subsequently, the relationship between channel approximation error and CE error with respect to the number of sub-blocks is derived, and the optimal number of sub-blocks is solved based on the criterion of minimizing the total error. In addition, considering that the amplification capability of active IRS requires power consumption, a closed-form expression for the optimal power allocation factor is derived. To further reduce the pilot overhead, a lightweight CE algorithm based on convolutional autoencoder (CAE) and multi-head attention mechanism, called CAEformer, is designed. The Cramér-Rao lower bound is derived to evaluate the proposed algorithm’s performance. Finally, simulation results demonstrate the proposed CAEformer network significantly outperforms the conventional least square and minimum mean square error scheme in terms of estimation accuracy.

Unmanned Aerial Vehicle (UAV) can enhance communication quality in wireless systems. It is helpful to address the blockage problem in mountainous regions. However, it has high energy consumption. In this paper, we propose a novel blockage model converted blockage region constraints into linear constraint. Then, the joint resource allocation and trajectory design scheme is proposed to maximize energy efficiency (EE). Specifically, we model the blockage effect caused by mountains. The Dinkelbach algorithm is designed to address the fractional objective function for EE. Then, we develop an iterative block coordinate descent (I-BCD) algorithm that alternately optimizes resource allocation and trajectory design subproblems. The resource allocation subproblem employs a dual-loop structure with Lagrangian duality to obtain closed-form solutions for power and bandwidth allocation. Meanwhile, the trajectory optimization uses successive convex approximation (SCA) to transform non-convex constraints into convex optimization problems. Simulation results demonstrate that the proposed scheme improves the EE compared with the benchmark schemes for UAV-assisted communications in mountainous regions.

Massive multiple-input multiple-output (MIMO) array systems are a cornerstone technology for beyond fifth-generation (B5G) and sixth-generation (6G) wireless communications. This paper proposes a novel algorithm for the joint estimation of direction-of-departure (DOD) and direction-of-arrival (DOA) in massive MIMO systems under unknown gain-phase errors. The proposed method first exploits a normalized rotational invariance property to extract the relative amplitude-phase difference vectors between adjacent antenna elements. By incorporating the prior knowledge that the transmitter and receiver phase errors follow a zero-mean distribution, we formulate two decoupled cost functions to enable joint DOD and DOA estimation. We then obtain that the corresponding angular parameters efficiently through low-complexity spectral searches. Notably, the proposed method requires only one well-calibrated transmitter and one well-calibrated receiver, thereby substantially reducing the calibration effort compared with existing approaches. The gain errors are directly estimated from the amplitude-phase difference vectors, while the phase error vectors are reconstructed using the estimated DOD and DOA values. The proposed framework accommodates general conformal transceiver array geometries and effectively mitigates error accumulation in gain-phase calibration. Simulation results verify that the proposed algorithm achieves superior angular estimation accuracy and calibration precision compared with state-of-the-art techniques.

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.

With the rapid development of autonomous unmanned systems, the Internet of unmanned agents (IUAs) has emerged as a prominent research field. Direction of arrival (DOA) estimation enables intelligent base stations (IBSs) to detect the direction of unmanned device, facilitating essential functions such as target localization and tracking, and cooperative navigation, significantly enhancing the environmental perception capabilities and task execution efficiency of IUA systems. However, the traditional DOA estimation algorithms in practical complex electromagnetic mutual coupling environments are computationally intensive, incompatible with IUA’s high real-time requirements. To address these challenges, this article proposes a data-driven (DD) DOA estimation method for unmanned device localization in IUA. The IUA localization system comprises four IBS equipped with uniform linear arrays (ULAs). Device localization is achieved through DOA estimates from these four IBS. A deep learning (DL) is proposed to jointly address two key challenges: 1) IUA’s demand for real-time algorithm performance and 2) mutual coupling effects between IBS sensors. A novel DL architecture is designed to estimate off-grid angle parameters and mutual coupling coefficients. The framework incorporates two learnable modules, one focusing on mutual coupling coefficients and another aimed at precise DOA estimation and associated confidence levels. The target unmanned device position is estimated using the least squares method applied to the DOA measurements from all IBSs. The proposed DL-based algorithm surpasses existing methods while maintaining low computational complexity. Extensive simulations demonstrate the high performance and real-time capabilities of the DD-based solution.

Orthogonal time frequency space (OTFS) modulation, combined with massive multiple-input-multiple-output (MIMO) technology, offers robust performance in high-mobility environments and high-user densities by capturing the full diversity of the wireless channel and effectively utilizing spatial multiplexing. This article introduces an adaptive block sparse backtracking (ABSB) algorithm designed to enhance channel estimation in OTFS with massive MIMO (massive MIMO-OTFS) systems. The proposed ABSB algorithm features dynamic block size adjustment based on the residual signal, improving its adaptability to the varying sparsity structure of the channel. Additionally, the algorithm extends the selection range of related block atoms to increase redundancy, reducing the risk of underfitting. Comprehensive simulation results demonstrate that the ABSB algorithm significantly outperforms traditional pilot-based methods in terms of channel estimation accuracy. It also surpasses the block orthogonal matching pursuit (BOMP) method as well as other classical compressed sensing methods. Specifically, the ABSB algorithm achieves up to a 20% reduction in estimation error compared to some of these traditional methods. The enhanced adaptability and robustness of the ABSB algorithm make it a promising solution for channel estimation in massive MIMO-OTFS systems, paving the way for more reliable and efficient next-generation wireless communications.

In the evolving landscape of wireless communications, extremely large-scale multiple-input-multiple-output (XL-MIMO) systems offer promising enhancements in capacity and spectral efficiency, particularly in near-field scenarios. This article investigates polar-domain channel estimation methods for near-field wideband XL-MIMO systems, proposing a novel approach based on the bilinear pattern detection (BPD) method. We introduce the multicandidate BPD (MBPD) algorithm, which improves detection accuracy by incorporating adaptive weight matrix adjustments and evaluating multiple candidate modes per iteration. Comprehensive simulations validate the superiority of MBPD over traditional BPD in terms of estimation accuracy and robustness. Furthermore, a detailed complexity analysis demonstrates the computational feasibility of the proposed algorithm. The MBPD algorithm greatly improves polar-domain channel estimation, facilitating more efficient implementations of near-field wideband XL-MIMO systems.

As the scale of antenna array increases, centralized methods based on deep learning (DL) encounter significant memory constraints on devices. To address this issue, we propose a distributed DL-based framework for gridless direction-of-arrival (DOA) estimation, which substantially alleviates memory constraints for devices operating in large-scale antenna scenarios. We employ an overlapped subarray selection strategy that partitions the complete array into multiple subarrays, allowing for partial array elements overlapped between adjacent subarrays. This strategy effectively compensates for the loss of cross-correlation information between subarrays, thereby enhancing the precision of DOA estimation. Within this framework, each subarray is paired with an independent subprocessor responsible for compressing the received data and transmitting the results to a fusion center. The fusion center leverages a graph neural network (GNN) to effectively extract DOA estimation information from complex datasets. This framework treats DOA estimation as a regression task, leveraging Toeplitz prior to achieve high-precision gridless DOA estimation through postprocessing. Additionally, we introduce a hybrid data-driven and model-based framework that significantly reduces computation time while ensuring the accuracy of DOA estimation, making it particularly suitable for real-time applications. Simulation results demonstrate that our proposed distributed DL methods achieve DOA estimation accuracy comparable to that of centralized DL methods, while exhibiting lower time complexity and reduced memory requirements for devices.

The scattering environment in offshore regions leads to serious attenuation in wireless transmission, reconfigurable intelligent surfaces (RISs) have emerged as a promising technology for future offshore communication systems. In this paper, we consider an RIS-assisted three-dimensional (3D) offshore communication system. Unlike quality of service (QoS), which does not accurately represent user-centric offshore communication systems, the mean opinion score (MOS) is adopted as a quality of experience (QoE) metric to evaluate device-to-device (D2D) vessel user (DVU) satisfaction. We aim to maximize the sum MOS of DVUs by jointly optimizing power allocation, spectrum reuse, and RIS reflection coefficients, subject to the minimum signal-to-interference-plus-noise ratio (SINR) requirements for unmanned surface vessels (USVs) and the maximum MOS constraints for DVUs. To tackle this non-convex optimization problem, we adopt the block coordinate descent (BCD) method to decompose the original problem into three sub-problems. Specifically, power allocation, spectrum reuse, and RIS reflection coefficients are iteratively optimized using the fractional programming (FP) method, the successive convex approximation (SCA) technique, and the semi-definite relaxation (SDR) algorithm, respectively. Simulation results demonstrate that the proposed RIS-SDR algorithm achieves sum MOS gains of 4%, 17%, 22%, and 46% compared to the baseline schemes, respectively.

Deep Learning (DL) achieves significant performance in estimating the direction of arrival (DOA) in array signal processing. However, many existing DL methods require a large amount of data to train a specialized DL network. To reduce data requirements for training, this paper presents a novel DL-based DOA estimation algorithm for limited training data(LTDDOA-net). The proposed algorithm utilizes the properties of second-order derivatives of the loss function and the ‘learn to learn’ approach to construct a framework that can achieve good performance with minimal data training. Initially, we developed a neural network designed for DOA estimation. This network was subsequently trained using proposed method and loss function on a limited dataset. Ultimately, we validated the practicality and benefits of our approach through simulations and hardware experiment. The results of simulations and hardware experiment have verified the superiority of the proposed approach.

Long-range wide area network (LoRaWAN) has been newly designed to exploit the 2.4 GHz unlicensed band instead of the traditional sub-GHz bands. However, this makes LoRaWAN frequently suffer from wireless communication failures caused by the cross-technology interference (CTI) from coexisting Wi-Fi networks using the same 2.4 GHz band. As a physical-layer solution to this problem, this paper presents CoWiL to combat the CTI from Wi-Fi to LoRa (the physical layer of LoRaWAN) in the 2.4 GHz band for better coexistence between LoRaWAN and Wi-Fi networks. Existing approaches address this problem by sacrificing Wi-Fi transmission performance or assuming that Wi-Fi interferes with only a small portion of LoRa signals. Unlike them, CoWiL does not affect normal Wi-Fi communications and is workable regardless of the degree of the CTI. This is achieved by implementing CoWiL at a LoRa receiver to directly extract LoRa data out of the CTI from Wi-Fi. Specifically, CoWiL exploits the correlation of the signal demodulation results between the preamble and the payload of a LoRa signal. A novel frequency bin mask is generated based on the demodulated preamble and then applied to the following payload for data decoding. Experimental results in various real-world environments show that in comparison with the existing solutions, CoWiL can reduce the packet error rate of LoRa transmissions by up to 96% under the CTI from Wi-Fi.

This article explores the integration of Reconfigurable Intelligent Surfaces (RIS) with Frequency Diversity Array (FDA) radars to advance sixth-generation (6G) communication technologies. The study specifically addresses the challenge of low-cost RIS-assisted FDA radar localization. To solve the joint angle-range estimation problem, we employ the Atomic Norm Minimization (ANM) approach. Traditional semidefinite programming (SDP) methods, often used for this purpose, suffer from high complexity and dependence on interior point methods. To improve efficiency, we propose an iterative solution based on the Alternating Direction Method of Multipliers (ADMM). Our simulation results demonstrate that this ADMM-based method not only enhances parameter estimation accuracy but also maintains low computational complexity, outperforming existing algorithms. This research marks a significant step forward in radar localization technology, effectively combining RIS with FDA radars and introducing a novel, efficient method for precise signal processing.

As the safety problems and economic losses caused by traffic accidents are becoming more and more serious, intelligent transportation system (ITS) came into being. After the outbreak of COVID-19, how to achieve effective traffic scheduling and macro command under less contact has attracted more attention. Therefore, the location estimation of traffic objectives is a key issue. In the developed framework, for the target parameter estimation in traffic, frequency diversity array multiple-input multiple-output (FDA-MIMO) radar is introduced into ITS, and tensor decomposition is used to process transportation big data (TBD) to improve the real-time performance of target location estimation. Unfortunately, spatial colored noise and array gain-phase error will affect the performance of FDA-MIMO radar in ITS. An algorithm that can solve the angle-range estimation problem of FDA-MIMO radar in the co-existence of array gain-phase error and spatial colored noise is proposed. Firstly, the four-dimensional tensor is constructed by using the temporal un-correlation of colored noise. Therefore, the influence of colored noise in ITS is removed. Secondly, the direction matrix containing target information is obtained by parallel factor (PARAFAC) decomposition. For the array gain-phase error, the optimization problem is constructed, and the Lagrange multiplier is employed to calculate the optimal solution. The effect of gain-phase error is eliminated by utilizing the optimal solution and the direction matrices. Finally, the location information of motor vehicle is achieved by calculating the solution of least square (LS) fitting. The developed scheme can achieve the location information of motor vehicles in the co-existence of array gain-phase error and spatial colored noise. Comprehensive numerical experiments illustrate that the developed scheme in ITS can efficiently obtain the location information of motor vehicles.

This paper proposes a heterogeneous carrier-sense multiple access (CSMA) protocol named LoHEC as the first research attempt to improve energy fairness when applying CSMA to long-range wide area network (LoRaWAN). LoHEC is enabled by Channel Activity Detection (CAD), a recently introduced carrier-sensing technique to detect LoRaWAN signals even below the noise floor. The design of LoHEC is inspired by the fact that existing CAD-based CSMA proposals are in a homogeneous manner. In other words, they require LoRaWAN end devices to perform identical CAD regardless of the differences of their used network parameter – spreading factor (SF). This causes energy consumption imbalance among end devices since the consumed energy during CAD is significantly affected by SF. By considering the heterogeneity of LoRaWAN in terms of SF, LoHEC requires end devices to perform different numbers of CAD operations with different CAD intervals during channel access. Particularly, the number of needed CADs and CAD interval are determined based on the CAD energy consumption under different SFs. We conduct extensive experiments regarding LoHEC with a practical LoRaWAN testbed including 60 commercial off-the-shelf end devices. Experimental results show that in comparison with the existing solutions, LoHEC can achieve up to$0.85\times $improvement of the energy fairness on average.

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.

With the advent of the Internet of Things (IoT) era, the multiunmanned aerial vehicle (UAV) networks have attracted great attention in the fields of source detection and localization. However, as the real-time signal processing performance of the UAV is limited by the computing speed and accuracy of the embedded hardware, the effectiveness of source localization is greatly reduced. Aiming at improving the accuracy and computational efficiency of source localization, a Cramer–Rao bound (CRB) weighted multi-UAV network source localization method is proposed based on the deep neural networks (DNNs) and spatial-spectrum fitting (SSF). The proposed source localization system is composed of UAVs equipped with a radar array. The source location can be achieved using the direction of arrival (DOA) of the source signals of UAVs, but the accuracy and real-time performance of the conventional DOA estimation algorithms are not satisfactory, and the data fusion strategy of the conventional cross-location framework needs further improvement. In the proposed method, a DNN-based SSF, denoted as the deep SSF (DeepSSF), is designed to achieve accurate DOA estimation. In the DeepSSF, the DOA estimation performance is guaranteed by the DNN’s strong nonlinear fitting ability and highly parallel structure. In addition, based on the obtained DOA information, the source is located once by every two UAVs. Finally, the source localization is realized based on the weighted CRB according to the principle that the more the DOA distribution deviates from zero, the lower the estimation accuracy. The simulation results verify the efficiency of the proposed method.

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 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.

As an indispensable part of the Internet of Vehicles (IoV), unmanned aerial vehicles (UAVs) can be deployed for target positioning and navigation in the space–air–ground-integrated network (SAGIN) environment. Maritime target positioning is very important for the safe navigation of ships, hydrographic surveys, and marine resource exploration. Traditional methods typically exploit satellites to locate marine targets in the SAGIN environment, and the location accuracy does not satisfy the requirements of modern ocean observation missions. In order to localize the marine target, we develop a system architecture in this article, which contains UAVs integrated with monostatic multiple-input–multiple-output (MIMO) radars. The main thrust is to estimate the direction-of-arrival (DOA) via MIMO radar. Herein, we consider a general scenario that unknown mutual coupling exist and a novel sparse reconstruction algorithm is proposed. The mutual coupling matrix (MCM) is adopted with the help of its special structure, we formulate the data model as a sparse representation form. Then, two novel matrices, a weighted matrix, and a reduced-dimensional matrix are constructed to reduce the computational complexity and enhance the sparsity, respectively. Thereafter, a sparse constraint model is constructed using the concept of optimal weighted subspace fitting (WSF). Finally, the DOA estimation of maritime targets can be achieved by reconstructing the support of a block sparse matrix. Based on the DOA estimation results, multiple UAVs are used to cross-locate marine targets multiple times, and an accurate marine target position is achieved in the SAGIN environment. Numerical results are carried out, which demonstrates the effectiveness of the proposed DOA estimator, and the multi-UAV cooperative localization system can realize accurate target localization.

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.

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.

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.

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.

The gridless one‐bit direction of arrival (DOA) estimator is proposed to estimate electromagnetic (EM) sources on a nested cross‐dipole array, and the multiple measurement vectors (MMV) mode is introduced to improve the reliability of parameter estimation. The gridless method is based on atomic norm minimization, solved by alternating direction multiplier method (ADMM). With gridless method used, sign inconsistency caused by one‐bit measurements and basis mismatches by traditional grid‐based algorithms can be avoided. Furthermore, the reconstructed denoising measurements with fast convergence and stable recovery accuracy are obtained by ADMM. Finally, spatial smoothing root multiple signal classification (SSRMUSIC) and dual polynomial (DP) methods are used, respectively, to estimate the DOAs on the reconstructed denoising measurements. Numerical results show that our method one‐bit ADMM‐SSRMUSIC has a better performance than that of one‐bit SSRMUSIC used directly. At low signal to noise ratio (SNR) and low snapshot, the one‐bit ADMM‐DP has an excellent performance which is even better than that of unquantized MUSIC. In addition, the proposed methods are also suitable for both completely polarized (CP) signals and partially polarized (PP) signals.

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).

A filter bank multicarrier (FBMC) with offset quadrature amplitude modulation (OQAM) (FBMC/OQAM) is considered to be one of the physical layer technologies in future communication systems, and it is also a wireless transmission technology that supports the applications of Internet of Things (IoT). However, efficient channel parameter estimation is one of the difficulties in realization of highly available FBMC systems. In this paper, the Bayesian compressive sensing (BCS) channel estimation approach for FBMC/OQAM systems is investigated and the performance in a multiple-input multiple-output (MIMO) scenario is also analyzed. An iterative fast Bayesian matching pursuit algorithm is proposed for high channel estimation. Bayesian channel estimation is first presented by exploring the prior statistical information of a sparse channel model. It is indicated that the BCS channel estimation scheme can effectively estimate the channel impulse response. Then, a modified FBMP algorithm is proposed by optimizing the iterative termination conditions. The simulation results indicate that the proposed method provides better mean square error (MSE) and bit error rate (BER) performance than conventional compressive sensing methods.

For multi-channel synthetic aperture radar (SAR), the high-coherence between different channel images provide low-rank property. Meanwhile, the ground moving target (GMT) exhibit sparse feature in the image domain. As a result, it is possible to apply robust principal component analysis (RPCA) method for enhanced performance of SAR ground moving target indication (SAR GMTI). In this paper, a joint multi-channel sparsity approach of RPCA is proposed for SAR GMTI by improving the performances of clutter suppression and GMTI. The joint sparsity feature between multi-channel images is modelled from the coherence between multi-channel data, enhancing the sparse signature of moving targets. Compared with the independent-channel sparse approach, the proposed joint sparsity approach is more robust to clutter or noise and has better performance in low signal-to-clutter/noise-ratio (SCNR) by persevering the moving targets. Finally, experimental analysis is implemented to confirm the effectiveness of the proposed algorithm.

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.

For multi-channel synthetic aperture radar of ground moving target imaging (SAR GMTIm), the moving targets in SAR image domain are sparse after clutter suppression, which provides the possibility of using sparse approach. In this paper, a reweighted sparse algorithm of SAR GMTIm is proposed to improve the imaging performance. Intuitively, both the magnitude and interferometric phase can exhibit the moving target signatures by applying displaced phase center antenna (DPCA) technique and along-track interferometry (ATI), respectively. So a hybrid metric of magnitude and interferometric phase is constructed to be as the weights of the reweighted sparse approach. Compared with the unweighted approach, the proposed reweighed approach can effectively improve the sparse imaging performance. Finally, experiments using measured data are performed to confirm the effectiveness of the proposed algorithm.