In covert communications, the position of the warden has a variety of situations, which leads to different scenes that require different covert communication schemes to ensure the security. In response to this situation, in this paper, a beamforming optimization method of covert communications for multi-position warden is proposed. Firstly, we formulate a general optimization problem and optimize it to maximize the covert communication rate of the user based on Dinkelbach’s transform. Subsequently, according to the optimized general optimization problem, we propose three schemes for three scenes corresponding to different fixed warden positions, using appropriate technologies for assistance in each scheme. Specifically, the intelligent reflecting surface (IRS) is used in Scene 1 and the integrated communication and jamming (ICAJ) is used in Scenes 2 and 3, and these technologies can assist the covert communication. Moreover, we propose an alternate optimization (AO) algorithm to solve the optimization problem of Scene 1 for its optimal covert communication performance. Additionally, we also propose an AO algorithm to solve the optimization problems of Scenes 2 and 3 to optimize the active beamforming. Simulation results demonstrate the effectiveness of all three proposed schemes, that outperform their respective benchmark schemes.

With the rapid growth in electromagnetic device quantities, various forms of communication interference have emerged, significantly impacting the accuracy of signal classification. Existing classification algorithms mainly focus on unintentional interference, such as co-channel interference and noise, with limited research on the problem of malicious interference in Multiple Input Multiple Output (MIMO) signal classification. This study proposes an intelligent MIMO signal classification algorithm based on fractional graph feature fusion. Initially, a high-order cumulant tensor model is constructed and regularized tensor decomposition is applied to reconstruct the MIMO signals. Subsequently, a feature extraction model using a fractional wavelet scattering network is designed to effectively capture the distinguishing features of signal constellations. Finally, a collaborative representation classifier based on the Grassmann manifold is utilized to amplify the differences between modulation categories, thereby improving classification performance. Simulation results indicate that the proposed algorithm effectively suppresses common communication interference and successfully classifies MIMO signals. Compared to existing methods, the proposed approach demonstrates significant performance improvements without requiring prior knowledge, such as noise power or channel coefficients.

Multimodal sentiment analysis has emerged as a critical research area. However, existing methods face significant challenges: (1) Unimodal feature extraction techniques often fail to capture the topological structure within data, and do not effectively integrate local and global information, leading to information loss. (2) Traditional multimodal fusion methods, such as concatenation, addition, and multiplication, struggle to model modality differences and inter-modal correlations. In this paper, we propose a novel multi-task hypergraph-attention framework (MTHA) to improve feature discrimination and model performance. Experimental results demonstrate that MTHA outperforms most baseline models in both sentiment classification and regression.

As the electromagnetic environment becomes more complex, a significant number of interferences and malfunctions of authorized equipment can result in anomalies in spectrum usage. Utilizing intelligent spectrum technology to sense and identify anomalies in the electromagnetic space is of great significance for the efficient use of the electromagnetic space. In this paper, a method for intelligent sensing and identification of anomalies in spectrum with alpha‐stable noise is proposed. First, we use a delayed feedback network (DFN) to suppress alpha‐stable noise. Then, we use a long short‐term memory (LSTM) autoencoder‐based attention mechanism to sense anomaly. Finally, we use the deep forest model to identify abnormal spectrum. Simulation results demonstrate that the proposed method effectively suppresses alpha‐stable noise, and it outperforms existing methods in abnormal spectrum sensing and identification.

Precise navigation signals are essential for unmanned aerial vehicles (UAVs) to achieve accurate positioning and task execution. However, global positioning system (GPS) signals are highly vulnerable to spoofing and jamming attacks, posing serious threats to flight safety. Therefore, this paper proposes an intelligent detection algorithm based on federated learning (FL) for identifying spoofing and jamming signals in unmanned aerial vehicle (UAV) navigation. Firstly, the algorithm innovatively integrates the local feature extraction ability of the temporal convolutional network (TCN) and the global dependency modeling advantage of the Transformer network, constructing a TCN-Transformer model to capture the features of navigation data efficiently. Secondly, a distributed construction network is adopted, where weight aggregation and updates are performed via federated learning, enabling global optimization without data sharing. Furthermore, an accuracy-weighted aggregation strategy is introduced, dynamically assigning weights based on the detection performance of each client mode. This encourages contributions of high-quality data and computational resources, thereby enhancing the overall model performance. Simulation results demonstrate that the proposed method outperforms traditional algorithms in detecting navigation spoofing and jamming, offering superior detection accuracy and robustness.

In wireless communications, the communication channel between the transmitter and receiver can be monitored by an eavesdropper. The eavesdropper uses deep learning (DL) to quickly identify the modulation parameters of signals and further disrupt legitimate communications. Since DL has been proven to be vulnerable to adversarial attacks, this paper proposes to attack the eavesdropper’s model by designing adversarial waveforms, preventing the eavesdropper from correctly identifying the modulation schemes used by legitimate users, and thereby preventing the eavesdropper from interfering with normal communications. This paper proposes an attention-based black-box attack method, which uses the prediction of different networks in the ensemble model to assign adversarial attention factors to each network. This greatly improves the transmission attack performance of the designed adversarial examples. In addition, by analysing the influence of the channel on the adversarial waveform, we further design the adversarial waveform that can be transmitted in the channel to improve the practicability of the attack algorithm. Finally, we theoretically derive the bounds of the adversarial risk increase that the attack brings to the target model. Simulation results show that the proposed method can improve the success rate of the attack on the eavesdropper’s modulation detection model, cause the model to misidentify the signal modulation type, and improve the security and reliability of legitimate transceivers in wireless communication systems.

To achieve sustainable communication and sensing, simultaneous wireless information and power transfer (SWIPT) has been introduced into integrated sensing and communication (ISAC). However, this combination brings significant security challenges due to signal multiplexing and spectrum sharing. In this paper, an active intelligent reflecting surface (IRS) assisted secure integrated sensing and SWIPT system is proposed with the power splitting (PS) model adopted. To maximize the harvested power while satisfying the constraints of sidelobe level ratio and secrecy rate, a problem is formulated to jointly optimize the transmit beamforming, artificial noise (AN) vectors, PS ratios, and amplification factors and phase shifts of active IRS, which is difficult to solve due to the coupled variables. To this end, we decompose it into two sub-problems, and propose two alternating optimization (AO) algorithms to solve them. First, an AO algorithm based on semi-definite relaxation (SDR) is developed. Specifically, we develop a two-layer algorithm to obtain the transmit beamforming matrix, AN covariance matrix and PS ratios, and utilize the penalty-based method to design the coefficients of active IRS. To reduce the complexity caused by the high-dimensional matrix operation of SDR, an AO algorithm based on successive convex approximation (SCA) is proposed, which can approximate the original problem as a sequence of convex counterparts via the first-order Taylor expansion. Simulation results show that the SCA-based AO algorithm can achieve the performance close to that of SDR with lower complexity.

With the development of science and technology, the amount of data has experienced explosive growth, and the localization of communication emitters under the backdrop of big data has become a hot topic of research. To solve this problem, we propose a fingerprint matching method based on the nearest neighbor correlation coefficient (MCC-KNN) in this paper. Firstly, we model the existing anomalous data in the process of localizing communication emitters, and we filter out the anomalies using a method based on generalized nuclear norms and Laplace scale mixture. Then, based on the emitter fingerprint after excluding abnormal data, we calculate the correction factor using the initial fingerprint database to create the corresponding fingerprint database. Finally, we utilize the emitter based on the MCC-KNN fingerprint matching method to accomplish communication emitter localization. Simulation results show that the proposed method has superior location accuracy compared to existing methods.

To reduce the energy used in minimum frequency shift keying (MSK) signal reception and enable green communications, we propose a new joint blind estimation method of MSK modulation parameters in the presence of alpha-stable noise for green communications in this paper. Firstly, the generalized second-order cyclic statistics (GSOCS) of the MSK signal is calculated where the received MSK signal is transformed nonlinearly in this calculation that alpha-stable noise in the received signal can be eliminated. Secondly, the specific time delay cross section of the GSOCS is extracted, and the related cyclic frequency set is obtained by using adaptive double threshold detection. Using these values, the modulation frequency interval is estimated using the spacing of adjacent cyclic frequencies in the cyclic frequency set, and the symbol period is estimated according to the modulation index of the MSK signal. The performances of these estimators are evaluated by deriving their corresponding Cramér-Rao lower bound (CRLB). Moreover, the asymptotic properties of modulation frequency interval and symbol period are analyzed. Simulation results demonstrate that the performance of the proposed method is close to its CRLB in the presence of alpha-stable noise, and it has good estimation accuracy. In particularly, the performance of the proposed method is better than the existing techniques in low generalized signal-to-noise ratio (GSNR).

The bistatic interferometric synthetic aperture radar (SAR) technology can break through the baseline limitation of monostatic dual antenna interferometric SAR (InSAR), obtain more flexible baseline configurations, and improve the measurement accuracy of long-wavelength InAR, such as L-band and P-band. Bistatic InSAR can play an important role in obtaining understory terrain height through perspective forest observation and carrying out forest aboveground biomass assessment. Aerospace Information Research Institute, Chinese Academy of Sciences led the design and development of China's first UAV-borne L-band bistatic InSAR system, and conducted flight experiments. This paper briefly introduces the system scheme design, basic structure, and main performance, with a focus on the time and phase synchronization technology based on the bi-directional synchronization chain. The first flight experiment scheme and test results are provided to verify the breakthrough of synchronization technology and the basic performance indicators of the UAV-borne bistatic InSAR system. This provides a foundation for the collaborative research of distributed InSAR synchronization technology on multiple UAV platforms in the future.

The expansion of data coverage and the accuracy of decoding of the narrowband-internet of things (NB-IOT) mainly depend on the quality of channel equalizers. Without using training sequences, blind equalization is an effective method to overcome adverse effects in the internet of things (IoT). The constant modulus algorithm (CMA) has become a favorite blind equalization algorithm due to its least mean square (LMS)-like complexity and desirable robustness property. However, the transmission of high-order quadrature amplitude modulation (QAM) signals in the IoT can degrade its performance and the convergence speed. This paper investigates a family of modified constant modulus algorithms for blind equalization of IoT using high-order QAM. Our theoretical analysis for the first time illustrates that the classical CMA has the problem of artificial error using high-order QAM signals. In order to effectively deal with these issues, a modified constant modulus algorithm (MCMA) is proposed to decrease the modulus matched error, which can efficiently suppress the artificial error and misadjustment at the expense of reduced sample usage rate. Moreover, a generalized form of the MCMA (GMCMA) is developed to improve the sample usage rate and guarantee the desirable equalization performance. Two modified Newton methods (MNMs) for the proposed MCMA and GMCMA are constructed to obtain the optimal equalizer. Theoretical proofs are presented to show the fast convergence speed of the two MNMs. Numerical results show that our methods outperform other methods in terms of equalization performance and convergence speed.

Passive sensing using communication signal waveforms is considered to be a promising technology for target monitoring in Internet-of-Everything. Conventional passive sensing schemes require accurate estimation of the time difference of arrival (TDOA) and frequency difference of arrival (FDOA), which is leading to high complexity but low accuracy. In this paper, a robust passive sensing algorithm using multiple illumination of opportunities is proposed to improve the detection performance while avoiding separate estimation of TDOA and FDOA. The proposed method first combines the linear constrained minimum variance adaptive filter with the wide nulling algorithm to achieve target direction finding while separating the direct wave and suppressing multipath interference. Then, the Linear Canonical Transformation-based Cross Ambiguity Function (LCTCAF) is employed to estimate the distance and radial velocity of the target. Relying on the relationship between distance to time and velocity to Doppler, a Distance-Velocity transformation-based Cross Ambiguity Function (DVCAF) is introduced to characterize the distance and radial velocity of the target. Finally, a spectral peak search scheme is exploited in DVCAF to estimate the time delay and Doppler shift so as to identify the target parameters directly. Its’ Cramer-Rao Low Bound is derived. Simulation results validate that the performance of the proposed algorithm outperforms the conventional estimators based on the cross ambiguity function.

Internet of Things (IoT) based on cognitive radio (CR) exhibits strong dynamic sensing and intelligent decision-making capabilities by effectively utilizing spectrum resources. The federal learning (FL) framework-based modulation recognition (MR) is an essential component, but its use of uninterpretable deep learning (DL) introduces security risks. This article combines traditional signal interference methods and data poisoning in FL to propose a new adversarial attack approach. The poisoning attack in distributed frameworks manipulates the global model by controlling malicious users, which is not only covert but also highly impactful. The carefully designed pseudo-noise in MR is also extremely difficult to detect. The combination of these two techniques can generate a greater security threat. We have further advanced our proposal with the introduction of the new adversarial attack method called “chaotic poisoning attack” to reduce the recognition accuracy of the FL-based MR system. We establish effective attack conditions, and simulation results demonstrate that our method can cause a decrease of approximately 80% in the accuracy of the local model under weak perturbations and a decrease of around 20% in the accuracy of the global model. Compared to white-box attack methods, our method exhibits superior performance and transferability.

Cognitive radio-enabled Internet of Things (CR-IoT) is considered as a promising technology to handle spectrum scarcity for IoT applications. Spectrum sensing enables unlicensed secondary users to exploit spectrum holes under the condition of avoiding interference with primary users in CR-IoT networks. Previous studies often assume that the noise is Gaussian while ignoring the influence of non-Gaussian noise. Moreover, multi-antenna-based spectrum sensing algorithms only consider the partial information of covariance matrix. This paper develops two multi-antenna-based spectrum sensing schemes, using fractional low-order covariance matrices to address the issue of performance degradation in impulsive noise. Specifically, the first scheme, namely, diagonal element weighting detection, exploits the diagonal element weighting of the fractional low-order covariance matrix. The latter scheme is called off-diagonal element weighting detection, which adopts the diagonal matrix weighting strategy that exploits the off-diagonal elements of fractional low-order covariance matrices. The approximate analytical expressions of the false alarm probability and detection probability are derived. These developed schemes do not employ any priori knowledge of the primary user signal. Simulation results indicate that two proposed schemes achieve acceptable performance and are robust to the characteristic exponent of the alpha-stable noise, e.g., these proposed methods could achieve a detection probability of 90% with a false alarm probability of 0.1 at GSNR = -16dB, respectively.

Modulation recognition using deep learning (DL) can efficiently recognize modulated signals in cognitive radio-enabled Internet of Things (IoT). However, it is vulnerable to the attack of adversarial examples designed by attackers, leading to a decrease in its accuracy. Different adversarial techniques can be used for attacks, but these attacks have limited efficiency. This article proposes a double loop iterative method. Different from the traditional attack methods, the new method designs an additional external loop iteration for high efficiency. When generating adversarial examples, the initial conditions of each iteration can be updated as the number of iterations changes, so that the adversarial examples can cross the decision boundary of the model as much as possible. In addition, this article uses knowledge distillation to improve the traditional adversarial training defense, which improves the robustness of the model. Simulation results show that the proposed attack and defense methods have better performance than traditional methods.

Despite its potential for reducing the detection probability at the warden, the effectiveness of covert communication in practical situations is often hindered by harsh wireless signal propagation environments. Fortunately, intelligent reflecting surface (IRS) can establish programmable wireless channels to tackle this issue. In this paper, we propose two IRS-assisted finite-blocklength covert communication schemes to maximize the effective covert throughput (ECT) with equal and unequal transmit prior probabilities, respectively. First, we analyze the warden’s detection performance with its optimal detection threshold derived, which is the worst situation for the covert transmission. We jointly optimize the transmit power, transmission blocklength, prior transmission probability and IRS’s phase shifts to maximize ECT in the common scenario and packet-generation scenario, respectively, which covers a wide range of practical applications. The designed optimal phase shifts not only maximize the signal-to-noise ratio at the receiver, but also introduce uncertainty to the warden for covertness provisioning. The closed-form expressions of solutions indicate that there exists a non-trivial trade-off between ECT and covertness, and adopting unequal transmit prior probabilities is proved to perform better than its counterpart of equal probabilities. Finally, numerical results demonstrate the superior performance achieved by the proposed covert communication schemes.

Signal identification, a vital task of intelligent communication radios, finds its applications in various military and civil communication systems. Previous works on identification for space-time block codes (STBC) of multiple-input multiple-output (MIMO) system employing orthogonal frequency division multiplexing (OFDM) are limited to additive white Gaussian noise. In this paper, we develop a novel automatic identification algorithm to exploit the generalized cross-correntropy function of the received signals to classify STBC-OFDM signals in the presence of Gaussian noise and impulsive interference. This algorithm first introduces the generalized cross-correntropy function to fully utilize the space-time redundancy of STBC-OFDM signals. The strongly-distinguishable discriminating matrix is then constructed by using the generalized cross-correntropy for multiple receive antennas. Finally, a decision tree identification algorithm is employed to identify the STBC-OFDM signals which is extended by the binary hypothesis test. The proposed algorithm avoids the traditionally required pre-processing tasks, such as channel coefficient estimation, noise and interference statistics prediction and modulation type recognition. Numerical results are presented to show that the proposed scheme provides good identification performance by exploiting the generalized cross-correntropy function of STBC-OFDM signals under impulsive interference circumstances.

Air transportation communication jamming recognition model based on deep learning (DL) can quickly and accurately identify and classify communication jamming, to improve the safety and reliability of air traffic. However, due to the vulnerability of deep learning, the jamming recognition model can be easily attacked by the attacker’s carefully designed adversarial examples. Although some defense methods have been proposed, they have strong pertinence to attacks. Thus, new attack methods are needed to improve the defense performance of the model. In this work, we improve the existing attack methods and propose a double level attack method. By constructing the dynamic iterative step size and analyzing the class characteristics of the signals, this method can use the adversarial losses of feature layer and decision layer to generate adversarial examples with stronger attack performance. In order to improve the robustness of the recognition model, we use adversarial examples to train the model, and transfer the knowledge learned from the model to the jamming recognition models in other wireless communication environments by transfer learning. Simulation results show that the proposed attack and defense methods have good performance.

Covert communication assisted by intelligent reflecting surface (IRS) has been widely investigated. Specifically, IRS can reconfigure wireless propagation environment to introduce uncertainty to the warden for covertness provisioning. In this paper, we propose an IRS-assisted finite-blocklength covert communication scheme with unequal transmit prior probabilities (UTPP) resulting from random packet generation at the transmitter. First, we analyze the warden's detection performance with its optimal detection threshold derived, which is the worst case for covert transmission. Then, we jointly optimize the transmit power, the blocklength, the phase shifts of IRS, and the transmit prior probabilities to maximize the effective covert throughput (ECT). Theoretical analysis reveal that UTPP can perform better tradeoff between ECT and covertness than equal transmit prior probabilities. Finally, numerical results demonstrate the superiority of the proposed covert communication scheme with UTPP.

Effective spectrum management control through multi-agent deep reinforcement learning holds promising potential for advancing wireless communication systems. However, backdoor attacks can compromise the integrity and security of multi-agent deep reinforcement learning models, allowing attackers to manipulate their behaviour and cause significant damage to the system. In this paper, we have defined a four-step process for designing a general backdoor in spectrum management based on multi-agent deep reinforcement learning, which involves searching for the most observed channels, determining the backdoor power limit, selecting feasible poisoned channels, and setting up induced rewards. Experimental results demonstrate the effectiveness of this attack, which allows the system to perform spectrum management without triggering the backdoor. However, when the backdoor is triggered, it results in severe communication interruptions. Overall, this paper contributes to the field of secure and reliable spectrum management by providing insights into the impact of backdoor attacks on deep learning-based systems.

Applying deep learning (DL) to modulation recognition can significantly improve the efficiency of communication in cognitive radio (CR) systems, but it may be attacked by adversarial examples. The black-box attack has vital practical significance because it does not need to master the parameters and architecture of the target model. The ensemble attack is an essential black-box attack method, which attacks the model by improving the transferability of adversarial examples. However, the existing ensemble attacks only simply adopt the average method when fusing the outputs of different networks, without fully considering the characteristics of the ensemble model, resulting in poor transferability. This paper proposes an attention-based ensemble attack method, which uses the prediction performance of different networks to assign attention factors to express the influence of these networks, so that the example can pass through the decision boundaries of all networks within a limited number of iterations. Simulation results show that the proposed method can improve the transferability of adversarial examples and effectively attack the black-box modulation recognition model.

Signal classification plays a pivotal role in cognitive radio networks. This problem becomes more challenging for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems employing linear space-time block code (STBC). This paper introduces a novel classification scheme to exploit the generalized cross-correntropy function to classify STBC-OFDM signals in the presence of impulsive interference. This scheme relies on the generalized cross-correntropy statistics of the STBC-OFDM signals to construct the strongly-distinguishable discriminating matrix. The proposed scheme avoids the estimation of channel coefficients, noise and interference statistics, and modulation types. Numerical results are presented to show that the proposed scheme provides acceptable classification performance in the presence of Gaussian noise and impulsive interference.

In the orthogonal frequency division multiplexing (OFDM) systems, inter-carrier interference (ICI) caused by carrier frequency offset (CFO) is considered to be one of the most crucial problems for OFDM over multipath channels, which will bring difficulties in the modulation classification of OFDM. In order to deal with the influence of ICI on subcarrier modulation classification, this paper presents a novel blind modulation classification method of OFDM systems with CFO over multipath channels. The virtual subcarrier signals and the pilot subcarrier signals with CFO are classified by using the second-order moment, and then the modulation subcarriers are classified with the fourth-order cumulants and the sixth-order cumulants. Simulations are conducted to verify the proposed method not only has a good classification performance, but also has a low computational complexity. The proposed method can reduce energy consumption and is beneficial for green radios.

Nowadays, the satellite communication system is subject to various interferences from the outside world. Therefore, to improve the reliability and security of the system, it is necessary to conduct in-depth research on the satellite communication interference, and one of the most important contents is the parameter estimation. In this paper, a novel intelligent estimation method of frequency domain parameters for satellite communication interference with alpha-stable noise is proposed. First, the received interference signal is filtered by myriad filter to suppress non-Gaussian noise. Then, we use the Fourier transform and the spatial pyramid pooling network (Spp-net) to estimate the bandwidth. Finally, we estimate the carrier frequency using the Fourier transform and Alexnet network. Simulation results show that the proposed method performs better than basic network structures under low generalized jamming noise ratio (GJNR) environment.

Cognitive radio-based Internet of Things (CR-IoT) network provides a solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in CR-IoT network, which has been investigated extensively based on deep learning (DL). Despite the unique advantages of DL in spectrum sensing, the black-box and unexplained properties of deep neural networks may lead to many security risks. This article considers the fusion of traditional interference methods and data poisoning which is an attack method on the training data of a machine learning tool. We propose a new adversarial attack for reducing the sensing accuracy in DL-based spectrum sensing systems. We introduce a novel design of jamming waveform whose interference capability is reinforced by data poisoning. Simulation results show that significant performance enhancement and higher mobility can be achieved compared with traditional white-box attack methods.

Distribution test, a method to evaluate the goodness-of-fit, has been applied to automatic modulation clas-sification (AMC) in recent years. It compares the empirical cumulative distribution function (ECDF) of the sample signal with the theoretical cumulative distribution function (TCDF) under each candidate modulation format. Most existing works in MIMO systems assume the additive noise is Gaussian. However, additive noise often exhibits non-Gaussian characteristics in practical systems. The TCDF of each modulation developed for Gaussian noise can not perform well in practical system. To solve this issue, this paper proposes a practical MIMO system model, which assumes that the noise is Cauchy-Gaussian bi-parameter mixture and analyzes the TCDF corresponding to different modulation modes under this noise model. The Kolmogorov-Smirnov (KS) distribution test is used to AMC in flat-fading channel for both Quadrature Amplitude Modulation and Phase Shift Keying. Extensive simulation results demonstrate that, compared with two-sample KS test based classifier, one-sample KS test based classifier can achieve good recognition results under the non-Gaussian noise model.

Unmanned aerial vehicle (UAV) communication is a promising technology that provides swift and flexible on-demand wireless connectivity for devices without infrastructure support. The proliferation of UAV communication equipment is causing the limited spectrum to become crowded. To deal with this issue, spectrum sharing policy (SSP) is introduced to support UAV communication. Spectrum sensing in SSP must be carefully formulated to control interference to the primary users and ground communications. In this paper, we propose spectrum sensing for opportunistic spectrum access in UAV communication to improve the spectrum utilization efficiency. Different from most existing works, we focus on the problem of spectrum sensing with randomly arriving primary signals in the presence of non-Gaussian noise/interference. We propose a novel spectrum sensing scheme to improve the spectrum utilization efficiency in UAV communication. We construct the p-norm decision statistic based on the assumption that the random arrivals of signals follow a Poisson process. Simulation results illustrate the validity and superiority of the proposed scheme when the primary signals are corrupted by additive non-Gaussian noise and are arriving randomly during spectrum sensing in the UAV communication.

High resolution and high quality are now the requirements in synthetic aperture radar (SAR) research. The sliding spotlight mode can obtain high azimuth resolution because of its large azimuth bandwidth. Group sparse penalty can effectively suppress azimuth ambiguities to improve image quality. Generalized mini-max concave (GMC) penalty is a kind of nonconvex penalty, which is widely used in SAR imaging. In this paper, a novel sliding spotlight SAR imaging method based on group sparsity and nonconvex regularization is proposed. Compared with matched filtering method, the proposed method can suppress noise and azimuth ambiguities. Both simulations and Qilu-1(QL-1) real SAR data experiments verify the effectiveness of the proposed method.

Radio frequency fingerprint identification (RFFI) technology identifies the emitter by extracting one or more unintentional features of the signal from the emitter. To solve the problem that the traditional deep learning network is not highly adaptable for the contour features extracted from the signal, this paper proposes a novel RFFI method based on a deformable convolutional network. This network makes the convolution operation more biased towards the useful information content in the feature map with higher energy, and ignores part of the background noise information. The proposed blind identification method requires less information and no training sequences and pilots, Thus, it achieves energy and spectrum efficient radio communications. Simulation verifies that the proposed method can achieve better recognition performance and is beneficial for green radios.

Estimating the location parameters of moving target is an important part of intelligent surveillance for the Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space–air–ground-integrated networks (SAGINs). In this article, a novel passive location parameter estimator using multiple satellites for the moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancelation algorithm to suppress the direct-path interference and multipath interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform FOCCCAF (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer–Rao lower bounds of the proposed location parameter estimators are derived to benchmark the estimator. The simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.

This article investigates nonorthogonal multiple access (NOMA)-based cloud radio access networks (C-RANs), where edge caching is adopted to cut down the crowdedness of the fronthaul links. We aim to maximize the energy efficiency (EE) by jointly optimizing the power allocation, analog, and digital precoding, which turns out to be an intractable nonconvex optimization problem. To tackle this problem, we first select cluster heads using the selecting cluster-head (SCH) algorithm, where the analog precoding matrix can be resolved by means of maximizing the array gains. Then, the device grouping algorithm is proposed to group devices according to the equivalent channel correlations, and thus, the NOMA devices in the same beam are capable of sharing the same digital precoding vector. Finally, the joint digital precoding design and power allocation algorithm is proposed to decompose the resultant optimization problem into two subproblems and solve them iteratively by applying the Taylor expansion operation and the minimum mean square error (MMSE) detection. Simulation results validate that the proposed NOMA-based C-RANs with a hybrid precoding (HP) scheme can achieve higher spectral efficiency and EE than the traditional orthogonal multiple access (OMA)-based approach and two-stage HP scheme.

Identification of transmit-antenna number is of importance in cognitive Internet of Things (IoT) with multiple-input–multiple-output (MIMO). Previous studies on transmit-antenna number detection only consider Gaussian noise and ignore impulsive interference. In the practical wireless communication, impulsive interference may exist due to low-frequency atmospheric noise, multiple access, and electromagnetic disturbance. Such interference can usually be modeled as symmetric alpha stable ($S\alpha S$), which cause the performance degradation of conventional algorithms based on the Gaussian model. In this article, we present a novel scheme to detect the transmit-antenna number for MIMO systems in cognitive IoT, assuming that signals are corrupted by both$S\alpha S$interference and Gaussian noise. We first introduce a new approach to characterize the generalized correlation matrix (GCM), and provide its bound with$S\alpha S$interference. Then, the discriminating feature vector is constructed by utilizing the higher order moments (HOMs) of eigenvalues of the GCM. Finally, an advanced clustering algorithm is employed to detect the transmit-antenna number, using the cluster where the minimum eigenvalue is located. The proposed algorithm avoids the need fora prioriinformation about the transmitted signals, such as coding mode, modulation type, and pilot patterns. Simulation experiments demonstrate the feasibility of the proposed transmit-antenna number detection scheme in MIMO systems with Gaussian noise and$S\alpha S$interference.

The next-generation cellular system operating in millimeter-wave (mm-Wave) frequencies requires different signal processing schemes from the legacy LTE system. Among these, beam management aligns the transmitter-receiver pairs with narrow beams and serves as an essential initial acquisition procedure. This paper introduces a novel angle-based downlink precoding strategy, including beam selection and user scheduling for a full dimension (FD) MIMO-OFDM system. The highly directional mm-Wave channel allows the system to perform user/beam scheduling rather than pure user scheduling during the precoding process. A beam-based receive matrix eliminates the remaining intra-cell interference caused by overlapped beams. The performance analysis proves the superiority of the proposed precoding and scheduling strategies over the conventional technique.

Unmanned aerial vehicles (UAVs) have been used in millimeter-wave (mmWave) networks as relays to assist remote or blocked communication nodes. In this paper, we perform secrecy analysis for UAV-based mmWave relaying networks, where a cooperative jamming scheme is proposed via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Considering the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, closed-form SOP of the network is obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) have been widely utilized to improve the end-to-end performance of wireless communications. In this paper, we perform a cooperative dual-UAV enabled secure data collection scenario and propose two schemes to ensure the security. The worst-case average secrecy rate is first maximized with the propulsion power limitation, where the scheduling, the transmit power, the trajectory and the velocity of UAVs are jointly optimized. To further save the on-board energy and prolong the flight time, we then maximize the secrecy energy efficiency. Based on the Dinkelbach method, we transform the fractional objective function into an integral expression and propose an iterative algorithm to obtain a suboptimal solution. Finally, numerical results are provided to evaluate the effectiveness of the proposed schemes.

The azimuth multichannel SAR is competent to achieve high-resolution and wide-swath (HRWS) imaging. For some spaceborne multichannel SAR systems, the pulse repetition frequency (PRF) of each channel at some beam positions is less than that of uniform sampling, hence leading to the azimuth ambiguities in the recovered images. In this paper, a novel azimuth ambiguity suppression method for multichannel SAR imaging based on$L_{2,q}$regularization$(0 < q\leq 1)$is proposed. First, by analyzing the reasons of azimuth ambiguities in multichannel SAR, we establish the imaging model different from that in single-channel SAR system. Second, we extend the$L_{2,q}$regularization from single-channel SAR system to multichannel SAR system and develop the proposed method. Finally, we demonstrate the effectiveness of the proposed method for non-sparse scenarios. Simulations and Gaofen-3 real data experiments are carried out to verify the validity of proposed method.

In hybrid satellite-terrestrial networks (HSTNs), frequency-selective fading can severely deteriorate the quality of transmitted signal by generating undesired and disordered constellation diagrams due to scatters in the mutipath channels. In this paper, we propose a low-overhead blind constellation identification method to combat frequency-selective fading in HSTNs. Specifically, a pre-equalization method is proposed based on a constant modulus algorithm to restore the contour of the constellation diagram. Moreover, the similarity measure function and the difference measure function are derived using template matching to identify the constellation of M-ary quadrature amplitude modulation (MQAM). The proposed method requires less information and no training sequences and pilots. Simulation results show that the proposed method is able to accurately identify the constellation type of MQAM signal, and these features are extremely useful in HSTNs.

Estimation of communication parameters, a major task of intelligent receivers, has important applications in adaptive wireless systems. Multiple antennas make the identification problem more challenging. In this paper, we focus on the problem of estimating the number of transmit antennas in multiple-input-multiple-output (MIMO) communication systems. A novel estimation algorithm is proposed to determine the number of transmit antennas for MIMO with alpha-stable noise. We first introduce the correlation matrix based on the fractional lower order statistics (FLOS) and provide a particular structure of FLOS-based correlation matrix. Then, the eigenvalues of the FLOS-based correlation matrix are employed to construct a test statistic and the central limit theorem is exploited to obtain the decision threshold. Finally, the transmit-antenna number is estimated using a serial binary hypothesis test. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number estimation algorithm for MIMO with alpha-stable noise.

In this paper, we propose a novel detection algorithm of the number of transmit antennas in multiple-input multiple-output (MIMO) systems, assuming that signals corrupted by non-Gaussian interference and Gaussian noise. We first introduce generalized correlation matrix. Then, the discriminating feature vector is constructed by exploiting the higher-order moment of the eigenvalues. Finally, an advanced clustering algorithm is employed for decision the number of transmit antennas, which is determined by the dimension of the cluster where the minimum eigenvalue is located. The proposed algorithm does not require a priori information about the transmitted signals, such as coding scheme, modulation type, and pilot patterns. Simulation results are demonstrated to evaluate the effectiveness of the proposed transmit-antenna number detection algorithm in MIMO systems with Gaussian Noise and non-Gaussian interference.

With the proliferation of mobile access services and wireless devices, spectrum resources are increasingly becoming scarce. Industrial wireless sensor networks may have to share frequency bands with other systems and suffer from considerable interference. To address that, industrial cognitive radio networks (ICRNs) were developed for effective spectrum sharing, where signal classification is a fundamental and important technology, especially for industrial wireless devices, which need to identify suspicious transmissions. In this article, a novel framework of signal intelligent classification is proposed based on deep learning networks in ICRNs. In the proposed framework, wireless signals will be preprocessed first by Choi-Williams distribution time-frequency analysis and represented by two-dimensional time-frequency images. Then, features of wireless signals are extracted through stack hybrid autoencoders (SHAEs). To accommodate general cases, we design multiple signal classification methods, including unsupervised, semisupervised, and supervised methods, which are processed by Softmax function, semisupervised linear discriminant function, and Fisher discriminant function, respectively. Finally, simulation studies are conducted and the corresponding simulation results show that the proposed framework is able to learn hierarchical features accurately and achieve excellent signal classification performance. Moreover, it can effectively overcome the negative impact caused by feature parameters uncertainty.

Satellite industrial Internet of Things (IIoT) plays an important role in industrial manufactures without requiring the support of terrestrial infrastructures. However, due to the scarcity of spectrum resources, existing satellite frequency bands cannot satisfy the demand of IIoT, which have to explore other available spectrum resources. Cognitive satellite networks are promising technologies and have the potential to alleviate the shortage of spectrum resources and enhance spectrum efficiency by sharing both spectral and spatial degrees of freedom. For effective signal estimations, multiple features of wireless signals are needed at receivers, the transmissions of which may cause considerable overhead. To mitigate the overhead, part of parameters, such as modulation order, constellation type, and signal to noise ratio (SNR), could be obtained at receivers through signal estimation rather than transmissions from transmitters to receivers. In this article, a grid method is utilized to process the constellation map to obtain its equivalent probability density function. Then, binary feature matrix of the probability density function is employed to construct a cost function to estimate the modulation order and constellation type for multiple quadrature amplitude modulation (MQAM) signal. Finally, an improved M2M∞method is adopted to realize the SNR estimation of MQAM. Simulation results show that the proposed method is able to accurately estimate the modulation order, constellation type, and SNR of MQAM signal, and these features are extremely useful in satellite-based IIoT.

In industrial sensor networks, complex industrial environments may be encountered leading to a mix of signals of different types. Complicated interference caused by mixed signals on industrial equipments may significantly degrade the classification rate of signals, which may result in a long training time in order to extract features. In addition, with limited channel resources, it is difficult to make the global optimal decision in industrial distributed wireless sensor networks. To address this problem, a signal classification method using feature fusion is proposed for industrial Internet of Things in this article. In the proposed method, the received signals of nodes are processed by frequency reduction and sampling pretreatment, based on which intelligent representations of signals are obtained. Using federated learning, the data samples are trained with the feature fusion network. Moreover, the trained deep learning network is used on each sensor node to classify signals, the results of which will be transmitted to aggregation center. In the aggregation center, the improved evidence theory method is used to aggregate the recognition results of each sensor node to achieve the final classification. Simulation shows that the proposed method has excellent classification performances. Notably, it is not required for the proposed method to transmit signals from nodes to the aggregation center, which could effectively protect the privacy of industrial information.

Employing unmanned aerial vehicles (UAVs) in millimeter-wave (mmWave) networks as relays has emerged as an appealing solution to assist remote or blocked communication nodes. In this case, the network security becomes a great challenge due to the presence of malicious eavesdroppers. In this paper, we perform a secrecy analysis for a UAV-based mmWave relaying network. We first investigate the relaying scheme without jamming where the UAV decodes and forwards the information from the source to the destination with malicious eavesdropping. Furthermore, to enhance the secrecy performance, we propose a cooperative jamming scheme via utilizing the destination and an external UAV to cooperatively disrupt the eavesdroppers at the two stages of relaying, respectively. Using the probability of line-of-sight (LoS) between the UAV and ground nodes, the three-dimensional (3D) antenna gain, and the Nakagami-m small-scale fading model, the secrecy outage probability (SOP) of the two schemes with and without jamming is analyzed. Closed-form expressions for the SOP of the two schemes are obtained by employing the Gauss-Chebyshev quadrature. Simulation results are presented to validate the theoretical expressions of SOP and to show the effectiveness of the proposed schemes.

In this paper, a downlink (DL) precoding scheme is introduced for time-division-duplex (TDD) multi-cell multi-user 3D massive MIMO/full-dimension MIMO (FD-MIMO) systems. A pre-beamformer based on uplink direction-of-arrival (DoA) is incorporated with interference alignment (IA) scheme to provide more degrees of freedom for each cell. We analyze the feasible conditions of applying IA schemes and provide the corresponding precoding schemes for different inter-cell interference scenarios. Simulation results show that the introduced IA-DoA-based multi-cell multi-user precoding and power allocation scheme significantly outperform the existing IA-based precoding strategies for FD-MIMO systems.

There is inevitable polarization angle deviation between the target echo signal and the direct path signal of illuminator of opportunity (IO) in passive radar. In order to investigate the potential performance loss in target detection induced by the random deviation, small unmanned aerial vehicle (UAV) detection experiments with digital television terrestrial multimedia broadcasting‐ (DTMB‐) based passive radar are conducted in this paper. Experimental results show that the polarization angles of the clutter signal and target echo signal are inconsistent. When the polarization diversity technology is used to suppress the clutter signal, the processing performance of the target echo signal may be reduced. On the premise that clutter is effectively suppressed by the processing algorithm, polarization synthesis can maximize the target echo signal processing gain. The effectiveness of the target localization algorithm combining time difference of arrival (TDOA) and direction of arrival (DOA) is also verified with polarization diversity reception in this paper.

The emerging cognitive radio-based Internet-of-Things (CR-IoT) network provides a novel paradigm solution for IoT devices to efficiently utilize spectrum resources. Spectrum sensing is a critical problem in the CR-IoT network which has been investigated extensively under the Gaussian noise/interference. Since most of the interference in an IoT network is non-Gaussian, in this article, we introduce a novel spectrum sensing method for CR-IoT with additive Gaussian mixture noise/interference. The introduced method maps the observation signal matrix from the original input space to a high-dimensional feature space by a nonlinear Gaussian kernel function and then constructs a kernelized test statistic in the feature space. The approximate analytical expressions of the false alarm and detection probability of the proposed scheme are derived under Gaussian mixture noise, and the decision threshold can be determined according to false alarm probability. The simulation results show that the introduced multiple-input-multiple-output (MIMO) spectrum sensing method achieves good performance under Gaussian mixture noise/interference and significantly outperforms existing detectors.

Blind estimation of parameters for M-ary frequency-shift-keying (M-FSK) signals is great of importance in intelligent receivers. Many existing algorithms have assumed white Gaussian noise. However, their performance severely degrades when grossly corrupted data, i.e., outliers, exist. This article solves this issue by developing a novel approach for parameter estimation of M-FSK signals in the presence of alpha-stable noise. Specifically, the proposed method exploits the generalized first- and second-order cyclostationarity of M-FSK signals with alpha-stable noise, which results in closed-form solutions for unknown parameters in both time and frequency domains. As a merit, it is computationally efficient and thus can be used for signal preprocessing, symbol timing estimation, signal and noise power estimation. Furthermore, substantial theoretical analysis on the performance of the proposed approach is provided. Simulations demonstrate that the proposed method is robust to alpha-stable noise and that it outperforms the state-of-the-art algorithms in many challenging scenarios.

Underlay cognitive radio (CR) holds the promise to address spectrum scarcity and let industrial wireless sensor networks obtain spectrum extension from shared frequency band resources. However, underlay CR devices should be capable of properly adjusting wireless transmission parameters according to the sensing of wireless environments. To realize the goal, in this article, two different signal-to-noise ratio (SNR) estimation methods are proposed for time-frequency overlapped signal estimations in the underlay CR-based industrial Internet of Things (IoT). In the first method, normalized higher order cumulant equations and the theoretical value of normalized higher order cumulants are adopted to estimate the SNR of component signals and the SNR of received signals. In the second one, the power of each component signals and the received signals is estimated based on the second-order time-varying moments. For the performance analysis, the Cramer-Rao lower bound of the SNR estimation for the time-frequency overlapped signals is derived. Simulation results show that the proposed method based on normalized higher order cumulants not only can effectively estimate the SNR of the time-frequency overlapped signals, but also has the strong robustness to the spectrum overlapped rate and the hybrid power ratio. The proposed method with second-order time-varying moments is able to accurately estimate the SNR of the time-frequency overlapped signals effectively, especially in the low-SNR region. These features are extremely useful in the industrial IoT, which usually operate in low-SNR regimes.

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Sparse signal processing theory has been applied to SAR imaging. The estimation of sparsity is crucial for sparse SAR imaging. But the true value of sparsity is unknown. Adaptive parameter estimation for sparse SAR imaging can achieved by the automatic regularization parameter estimating methods. However, these methods are deduced based on measurement matrix, which will cause huge computational and memory costs. Also, the adaptive estimated sparsity is often greater than the true value due the noise and sidelobes. In this paper, we propose improved adaptive parameter estimation method for sparse SAR imaging. The complex-image-based sparse SAR imaging is adopted to pre-estimate the parameter. Then, azimuth-range decouple operators are introduced into parameter estimation method. Simulation and real data experimental results show the effectiveness of the proposed method.