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As a prominent candidate to cope with both device integration and miniaturization, multifunctional metadevices have attracted increasing interest recently. However, suffering from several demerits, conventional multifunctional devices that could not meet practical demands. In this paper, we elaborate on a single-layer transmissive all-dielectric meta-lens platform that can flexibly manipulate beams by pluggable phase elements over a broad millimeter-wave regime. The pluggable element of the meta-lens platform is able to provide a full 2π transmission phase coverage together with a high transmission coefficient. The elements are encoded into 36 phase modules as a phase library. The designed phase modules can be re-assembled that span 2π uniformly to reconstruct distinct functions, due to their dynamic pluggable feature. To verify its flexible assembly and efficient phase modulation, the meta-lens platform with different arrangements of phase modules is assembled and demonstrated in the experiment, which is in good agreement with theoretical and simulation analyses. Owing to the properties of flexible assembly, broad bandwidth, and high efficiency, the proposed meta-lens platform paves the way for potential applications in millimeter-wave regimes such as beamforming, computer-generated hologram, and other complex demands that involve phase manipulations.

In this paper, we propose a covert wireless uplink transmission strategy in space-air-ground integrated vehicular networks, where the source vehicle transmits its own message over the channel that being used by the host communication system, to avoid being detected by the warden. It is obvious that the data transmission efficiency of the covert communication system is limited due to the co-channel interference. To improve the data transmission efficiency, we consider that the covert communication system adopts improper Gaussian signaling (IGS). We formulate a joint transmit power and IGS factor optimization problem to minimize the outage probability of the covert communication system. The minimum error detection probability of the warden is first analyzed with noise uncertainty, which is used to measure the system covertness. Under the constraints of the quality of service (QoS) of host communication system and the covertness requirement, the optimal transmit power is first derived with proper Gaussian signaling (PGS) scheme. Then, with the approximate outage probability derived under IGS scheme, the optimization problem is solved by jointly designing the transmit power and IGS factor. Finally, we provide extensive numerical results to validate the proposed covert transmission strategy, and demonstrate that the IGS scheme is beneficial in improving the data transmission efficiency in terms of outage probability compared to PGS scheme.

Covert wireless communications aim to hide the existence of transmission behavior from watchful adversaries to enhance security. In this paper, we propose a spectrum mask based covert communication strategy in Internet of Things (IoT) networks, where the overt channels are leveraged to enhance the covertness. Specifically, the legitimate IoT transmitter superimposes its own message on the overt channel to avoid being detected by the warden. We assume that proper Gaussian signaling (PGS) is adopted at the overt channel, and improper Gaussian signaling (IGS) is adopted at the legitimate transmitter to improve the covert transmission performance. To maximize the covert rate of the legitimate IoT system, a joint transmit power and IGS factor optimization problem is formulated under the constraints of covertness requirement. The metric of minimum error detection probability, that represents the worst-case for the legitimate transmitter, is utilized to measure the covertness. By exploiting the piece-wise monotonic properties of the objective function and the constraints, we derive the optimal transmit power and IGS factor pairs in both the IGS and PGS schemes. Finally, extensive numerical results are presented to demonstrate that the IGS scheme can improve the covert rate compared to the PGS scheme under a given covertness constraint.

With unprecedented progress on Internet of Things (IoT), spectrum scarcity becomes even severe with the explosive growth of wireless smart devices. To deal with spectrum scarcity issues, cognitive radio (CR)-enabled IoT has been emerged as a promising solution, which allows IoT devices reusing the underutilized spectrum bands. In this article, we investigate spectrum sensing in CR-IoT, in which full-duplex CR-IoT node can perform spectrum sensing and data transmission concurrently for reducing sensing delay. Two sensing methods are proposed based on multiple high-order cumulants for excavating rich information of the non-Gaussian transmitted signals. Specifically, for the scenarios with a single sensing antenna, we first propose a multiple high-order cumulants-based sensing method (MCS) derived from the likelihood ratio test, which is assumed to be near optimum. The test statistics are derived, respectively, in two cases, i.e., the case only performing sensing and the case performing sensing and transmission simultaneously. Interestingly, the derived two test statistics have same expression, while the corresponding sensing thresholds are different from each other. For the scenarios with multiple sensing antennas, we propose a multiantenna-assisted multiple high-order cumulants-based sensing method (MMCS), which can provide a tradeoff between the computational complexity and sensing performance. We conduct the hypothesis test with Hotelling’s ${T^{2}}$ -statistic and derive the corresponding sensing threshold. Theoretical performance evaluated by detection probability and computational complexity of the proposed methods are analyzed. Additionally, extensive simulations are provided, which show both the proposed methods can counter the adverse effects of noise uncertainty, and MCS has superiority over MMCS in terms of sensing accuracy.

Automatic modulation classification (AMC) is becoming increasingly important for its fundamental role in dynamic spectrum access, which can support 5G wireless communications to refarm the spectrum resource with low utilization. In order to achieve a better classification performance, several AMC methods based on prototype and variant of convolutional neural networks (CNNs) have been proposed. However, most existing AMC methods based on CNNs only use monomodal information from either time domain or frequency domain. The complementary processing gain, which can be obatianed by fusing multimodal information from multiple transformation domain together, is neglected. To address the issue, we exploit a waveform-spectrum multimodal fusion (WSMF) method to realize AMC based on deep residual networks (Resnet). After extracting features from multimodal information using Resnet, we adopt a feature fusion strategy to merge multimodal features of signals to obtain more discriminating features. Simulation results demonstrate the superior performance of our proposed WSMF method compared with traditional CNNs based AMC method using single modality information. Our proposed method can distinguish among sixteen modulation signals, and it works well even for higher-order digital modulation types like 256QAM and 1024QAM.

In cognitive radio networks, spectrum sensing under circumstances of dynamically varying noise and lacking prior information is a key challenge to the conventional spectrum sensing algorithms. Since the necessary information is rather difficult to obtain practically, most existing spectrum sensing methods are fettered in applications. Motivated by these, in this paper, a Frequency domain Goodness of Fit Test (FGoF) based spectrum sensing method is proposed. The FGoF makes full use of underlying information in Guard- Bands and the advantages of GoF test works for any distribution. Analytical and simulated results show that the FGoF is a robust spectrum sensing method in cognitive radio with the inherent advantages of invulnerability to dynamically varying noise.

In this letter, we consider the varying detection environments to address the problem of detecting small targets within sea clutter. We first extract three simple yet practically discriminative features from the returned signals in the time and frequency domains and then fuse them into a 3-D feature space. Based on the constructed space, we then adopt and elegantly modify the support vector machine (SVM) to design a learning-based detector that enfolds the false alarm rate (FAR). Most importantly, our proposed detector can flexibly control the FAR by simply adjusting two introduced parameters, which facilitates to regulate detector's sensitivity to the outliers incurred by the sea spikes and to fairly evaluate the performance of different detection algorithms. Experimental results demonstrate that our proposed detector significantly improves the detection probability over several existing classical detectors in both low signal to clutter ratio (SCR) (up to 58%) and low FAR (up to 40%) cases., Comment: 5 pages, 4 figures, 1 table, submitted to IEEE Geoscience and Remote Sensing Letters (GRSL)

An automatic modulation classification has a very broad application in wireless communications. Recently, deep learning has been used to solve this problem and achieved superior performance. In most cases, the input size is fixed in convolutional neural network (CNN)-based modulation classification. However, the duration of the actual radio signal burst is variable. When the signal length is greater than the CNN input length, how to make full use of the complete signal burst to improve the classification accuracy is a problem needs to be considered. In this paper, three fusion methods are proposed to solve this problem, such as voting-based fusion, confidence-based fusion, and feature-based fusion. The simulation experiments are done to analyze the performance of these methods. The results show that the three fusion methods perform better than the non-fusion method. The performance of the two fusion methods based on confidence and feature is very close, which is better than that of the voting-based fusion.

Multi-functional metamaterial absorbers have attracted considerable attention for applications in the microwave frequency regime. In this paper, we report the design, fabrication, and characterization of frequency-selective absorbers, which exhibit substantial absorption property within a pre-defined frequency band, while at the same time behaving as a highly transparent screen in another targeted frequency band. The proposed designs consist of a symmetrically patterned indium tin oxide film acting as an absorbing layer, two dielectric substrates, and a cross-slot metal sheet frequency selective surface playing the role of a transmitting layer. In order to validate the functionalities of the designed absorbers, equivalent circuit models, full-wave numerical simulations and measurements are presented. The measured results, in good agreement with the numerical ones, show that the proposed designs realize 80% broadband absorption over the desired frequency range and possess a transparent window in a higher or lower frequency band for a wide range of incidence angles up to 60

This study presents a new machine learning (support vector machine (SVM))-based cooperative spectrum sensing (CSS) model, which utilises the methods of user grouping, to reduce cooperation overhead and effectively improve detection performance. Cognitive radio users were properly grouped before the cooperative sensing process using energy data samples and an SVM model. The resulting user group which participates in cooperative sensing procedures is safe, less redundant, or the optimised user group. Three grouping algorithms are presented in this study. The first grouping algorithm divides normal and abnormal users (malicious and severely fading users) into two groups. The second grouping algorithm distinguishes redundant and non-redundant users. The third grouping algorithm establishes an optimisation model with the objective of minimising average correlation within subsets. All users are then divided into a specific number of optimised groups, only one of which is required for cooperative sensing in each time. The performances of the three algorithms were quantified in terms of the average training time, classification speed and classification accuracy. Experimental results showed the proposed algorithms achieved their intended function and outperformed a conventional machine learning-based CSS model (proposed by Karaputugala et al. ) in terms of security, energy consumption, and sensing efficiency.

Cooperative spectrum sensing has been shown have various advantages including spectrum utilization and robustness in Cognitive Radio (CR) networks. Security concerns are raised for cooperative spectrum sensing due to its vulnerabilities to the potential attacks. In this paper, we first analyze the performance limits of cooperative spectrum sensing under Byzantine attacks where malicious users send tampered data to the fusion center (FC) leading to increased probability of incorrect sensing results. We also propose a novel and easy cooperative spectrum sensing scheme to counter Byzantine attacks in malicious CR networks. In this approach, the FC removes the anomalous value from the data fusion process which most likely from the malicious users. Simulation results show that our proposed scheme is more robust against Byzantine attacks than traditional scheme.

Rooted in the compressed sensing theory, sub-Nyquist spectrum sensing (SNSS) has been considered as a promising approach to dealing with difficulties and limitations of conventional wideband spectrum sensing in cognitive radio (CR) networks. Most existing SNSS methods require some prior knowledge of the monitored frequency bands, such as the spectrum occupancy/sparsity level and/or the noise power, to determine a termination condition used by an underlying iterative signal recovery process. However, such prior knowledge may be difficult to acquire in practical CR scenarios. To address this problem, we propose a blind SNSS algorithm, referred to as the residual energy ratio based detector (RERD), which bypasses the need for the above-mentioned prior knowledge and performs spectrum sensing in a more autonomous way. The RERD algorithm, which is based on the modulated wideband converter (MWC) sub-Nyquist sampling framework, employs energy ratios of adjacent channels of the MWC as test statistics. We derive closed-form expressions of the decision threshold and the false alarm probability following the Neyman–Pearson criterion. Simulation results show that, without requiring the aforementioned prior knowledge, the RERD algorithm can accurately determine the support of a multiband signal contaminated by background noise in a wide range of signal-to-noise ratio. Moreover, the RERD algorithm is shown to be robust to a range of sparsity orders and different number of sampling channels.

Built on compressed sensing theories, sub-Nyquist spectrum sensing (SNSS) has emerged as a promising solution to the wideband spectrum sensing problem. However, most of the existing SNSS methods do not distinguish if primary users (PUs) are present or absent in the concerned spectrum band and directly pursue support recovery of the PUs. This may lead to a high false alarm rate and a waste of computational cost. To address the issue, we propose a predecision algorithm, referred to as the pairwise channel energy ratio (PCER) detector, to determine the presence or absence of PUs prior to signal support recovery. The proposed detector is based on the popular modulated wideband converter (MWC) framework for SNSS, which has several advantages over other SNSS approaches. The PCER test statistic is constructed from compressed samples obtained by the MWC. The decision threshold and the detection probability are derived in closed form following the Neyman–Pearson criterion. Numerical results are presented to verify the theoretical calculation. The proposed PCER detection method is shown to be able to detect the existence of PUs in a wide range of signal-to-noise ratio, while being robust to noise uncertainty and does not need the prior knowledge of the PU signals. Additionally, our results show that the use of the PCER detector leads to a significant improvement of the correct support recovery rate of the PU signals.

To boost the performance of binary pulse amplitude modulation at low signal-to-noise ratio, the parameter-tuned stochastic resonance (SR) is introduced into digital communications system. In this study, an analytical framework is developed for evaluating the system performance by approximating the probability density function of the Ornstein–Uhlebeck noise based on the central limit theorem. Expression for the bit error rate of the bistable SR system with coherent receiver is derived. Theoretical and numerical results are presented to verify the analysis that the noise can improve the performance of the SR system with non-coherent receiver. Also, it is shown that the performance of the bistable SR system with coherent receiver is superior to that with non-coherent receiver, and the background noise is not favourable to signal processing in the coherent receiver.

Spectrum sensing is a key technology for cognitive radios. We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification. We normalize the received signal power to overcome the effects of noise power uncertainty. We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals. We also use transfer learning strategies to improve the performance for real-world signals. Extensive experiments are conducted to evaluate the performance of this method. The simulation results show that the proposed method performs better than two traditional spectrum sensing methods, i.e., maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method. In addition, the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals. Furthermore, the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning. Finally, experiments under colored noise show that our proposed method has superior detection performance under colored noise, while the traditional methods have a significant performance degradation, which further validate the superiority of our method., Comment: Submitted to China Communications

Spectrum sensing in the presence of non-Gaussian noise is a challenging task for cognitive radio networks (CRNs). Among all spectrum sensing approach, energy detection (ED) is the dominantly used one and becomes the object of comparison of others. Motivated by these, in this paper, we provide a performance analysis of ED in generalized Gaussian noise (GGN). We make full use of the stochastic characteristics of generalized Gaussian distribution and the central limit theorem (CLT) to derive the probabilities of detection and false alarm. By using the Berry-Esseen theorem, the convergence of generalized Gaussian distribution to Gaussian distribution is further given. Based on the related Berry-Esseen bound, the number of samples required to ensure a given approximation error was derived. Besides, a performance analysis about the influence of noise uncertainty is proposed, which shows that the SNR Wall of ED in GGN is the same as that in Gaussian noise. For cooperative spectrum sensing with ED in GGN, we provide targeted approach for different cases. Simulation results validate our analysis, and show that the proposed fusion rule can improve ED in CRNs.

Spectrum sensing, as a way for a secondary user (SU) to detect the on/off status of the primary user (PU), has been well studied in the area of cognitive radio (CR) for the past ten years. It is noticed that most exiting techniques only assumed that the PU has constant transmit power, although in practice, the PU could possibly operate under more than one discrete power levels, as can be seen from many practical standards, e.g., 802.11, Global System for Mobile Communications, and Long-Term Evolution. In this paper, we investigate this new multiple primary transmit power (MPTP) scenario and propose the corresponding detection/recognition framework based on multiple-antenna configuration at the SU. Although the primary target in MPTP is still to detect the on/off status of the PU, a secondary target appears in order to identify PU's transmit power level once the PU is “detected” in the first place. To achieve these two targets, an eigenvalue-based approach is applied due to its robustness and least requirement on the knowledge of channels. To obtain the closed-form threshold expressions and the performance analysis, we adopt nonasymptotic Gaussian and Gamma approximation for the distribution of the eigenvalues of the sample covariance matrix. Simulation results are provided to verify the proposed studies.

Cooperative spectrum sensing, proposed to improve the performance of spectrum sensing in cognitive radio systems where there are multiple secondary users who can cooperatively detect the presence of one primary user, is receiving significant attention. However, few cooperative sensing algorithms take the correlation among the received primary user signals into account. A feasibly efficient cooperative spectrum sensing scheme based on Cholesky decomposition of the correlation matrix of the received signals is proposed. The ratio of the maximum eigenvalue to the minimum eigenvalue of the matrix obtained by Cholesky decomposition is used to construct the test statistic. Analytical approximations for the false alarm probability and decision threshold are derived using a moment matching method. The new scheme is in the category of blind cooperative spectrum sensing schemes requiring neither information about the primary user signal nor the channel nor the noise power. The new scheme can work better than the existing eigenvalue-based cooperative spectrum sensing methods in some conditions, and it has lower complexity.