Integrating cognitive radio into Internet of Things (IoT) is conducive to reducing spectrum scarcity for large-scale IoT deployment, where a core technology is the design of spectrum access algorithms for effective assignment of spectrum holes. However, due to the partially observable channels and increased number of users in the cognitive radio Internet of Things (CRIoT) network, the secondary users have difficulty avoiding interferences and accessing the spectrum quickly. This study presents a distributed dynamic spectrum access (DSA) algorithm that employs a priority experience replay deep echo state Q-network (PER-DESQN) for CRIoT networks with multiple users and channels. To accelerate the Q-network convergence, we use an echo state network based on the underlying temporal correlation to estimate Q-values. Then, to resolve the Q-value overestimation and improve prediction accuracy, the estimated Q-value and decision action process are trained using a double deep Q-network (DDQN). Moreover, a priority experience replay mechanism that uses the Sum-Tree combined with importance sampling weights is proposed to optimize the DDQN to address the instability of the Q-value resulting from random sampling. As the simulation results demonstrate, the proposed algorithm can make fast and accurate DSA decisions and boost the network channel capacity significantly.

Terahertz (THz) massive multiple-input multiple-output (MIMO) is considered as a key technology for future sixth-generation (6G) wireless communications, in which hybrid precoding facilitates an important trade-off of hardware cost and spectrum efficiency. However, the performance of traditional schemes is limited owing to the beam split effect and the non-convex optimization problem as well as the inter-user interference under imperfect channel state information (CSI) in THz massive multi-user (MU)-MIMO systems. To overcome these challenging problems, we propose an unsupervised convolutional neural network (CNN)-based hybrid precoding scheme with attention mechanism. Specifically, we first adopt the true-time-delay (TTD) structure to mitigate beam splitting. Then, to solve the non-convex optimization problem of TTD hybrid precoding and to further mitigate inter-user interference, we propose a robust hybrid precoding scheme by applying the attention mechanism and CNN, which can be trained to generate an optimal analog precoder targeting at an achievable rate maximization under imperfect CSI. Simulation results show that the proposed algorithm has good robustness and can maintain excellent achievable rate performance in the case of imperfect CSI.

The phrase searchable encryption scheme improves search efficiency and accuracy by searching a set of consecutive keywords from ciphertext. However, most current phrase searchable encryption schemes still don't support dynamic data updates in practical application scenarios such as smart healthcare due to forward security issues. In this article, a dynamic forward secure searchable encryption scheme with phrase search for smart healthcare systems is proposed. On the one hand, we adopt a state chain structure to construct an inverted index, which contains the location of keywords so as to achieve phrase search. On the other hand, we have added a modification operation that modifies the location of keywords directly by traversing the inverted index to achieve indexes update. In this way, the efficiency of data updating can be raised greatly because the update operation of first-delete-then-add is avoided in the previous phrase search scheme. The scheme is proved to satisfy the forward security in the leaked search pattern and access pattern since the server cannot know the updated state of updated files in the state chain index structure which is randomly generated by the client. Experimental results show that the proposed scheme has higher search accuracy and efficiency which can save at least 50ms in search time compared to other related schemes.

In this letter, the correlation structure inherent in the received data is exploited to improve detection performance for sea-surface small floating targets in short observation time. Three detectors are devised resorting to the eigenvalues of covariance matrix that are versatile statistics reflecting the signal correlation. Specifically, the proposed detectors respectively exploit the maximum eigenvalue to arithmetic mean (MAM) of all eigenvalues, the maximum eigenvalue to geometric mean (MGM) of all eigenvalues, and the maximum eigenvalue to minimum eigenvalue (MME) to form test statistics. At the analysis stage, the three-parameter Burr function is exploited to approximate the statistical distributions of the test statistics under null hypothesis and alternative hypothesis. Besides, the analytic expressions of false alarm probability, detection probability, and thresholds of the proposed detectors are derived. Finally, simulation results on real sea clutter show that the proposed detectors provide effective solutions to the problem of target detection in sea clutter.

Proactive eavesdropping was used recently to efficiently intercept a suspicious wireless communication link, by jamming the suspicious destination node. However, While jamming helps weaken the suspicious link, it does not improve the eavesdropping channel from the source node to the legitimate eavesdropper. This work proposes to use intelligent reflecting surfaces (IRS) to enhance proactive eavesdropping, by jointly affecting both the suspicious and eavesdropping channels, and is the first to employ the cooperative passive beamforming in the double-IRS aided monitoring system. By considering the cooperative two single-reflection links and especially the double-reflection link, we jointly design the passive phase shift matrices of double IRSs to optimize the eavesdropping capability. Due to the coupled variables caused by double-IRS cooperation and the intractable unit modulus constraints of IRS elements, the non-convex optimization problem is difficult to solve. We first divide the problem into two subproblems, and apply the idea of minimization majorization to make each subproblem convex. We obtain the solution of reflective coefficient matrix in closed form, and then propose an alternating algorithm with low complexity to obtain the Karush-Kuhn-Tucker (KKT) solution. Finally, simulation results are presented to demonstrate the effectiveness of cooperative passive beamforming design over the traditional single-IRS and jamming assisted eavesdropping schemes.

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called$\alpha $-sum and$\alpha $-product of maximum and minimum eigenvalues ($\alpha $-SMME,$\alpha $-PMME) are further designed, in which$\alpha $is a weight coefficient. Furthermore, the theoretical performance analysis of the$\alpha $-SMME and$\alpha $-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

Due to the proliferation of mobile devices, provisioning of massive connectivity has become a major challenge for future networks. The combination of millimeter wave (mmWave) with non-orthogonal multiple access (NOMA) provides a promising solution to massive connectivity. However, the security issue therein cannot be ignored due to the openness of wireless channels. To overcome the security challenge in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of randomly located eavesdroppers. According to their channel disparity, the NOMA users with stronger channel gains are deemed as SUs for better secrecy performance, while the remaining ones are served as CUs. To further enhance the security, hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs’ requirements. To solve the intractable non-convex problem, we decompose it into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

Considering the security issue in mmWave-NOMA based networks, the nonorthogonal interference can be exploited to improve the security. In this paper, we propose a novel mmWave-NOMA framework where the users are classified as secure users (SUs) and common users (CUs), to satisfy their heterogeneous security service needs with the presence of ran-domly located eavesdroppers. For better secrecy performance, the NOMA users with stronger channel gains are deemed as SUs, and the hybrid precoding for SUs is designed to strengthen the desired signal and reduce interference. In addition, to reduce the complexity and satisfy the diverse demands, user grouping and power allocation are jointly optimized to maximize the sum rate of CUs subject to the SUs' requirements. The non-convex problem is decomposed into two subproblems, i.e., user grouping and power optimization, and a hybrid SU-CU grouping algorithm and a successive convex approximation based algorithm are proposed to solve them, respectively. Finally, simulation results are provided to show the advantages of the proposed scheme.

In this letter, a cascade algorithm combined subband decomposition with an eigenvalue-based detection scheme is proposed to detect moving targets in sea clutter for the radar system with short pulses. Using a discrete Fourier transform-modulated filter bank, on the one hand, subband decomposition can effectively suppress clutter as well as increase the coherent integration time. On the other hand, it transforms the scene where the target spectrum is separated from the clutter spectrum into the scene where the target spectrum overlaps with the clutter spectrum. In the target subband, the noncoherent method using amplitude difference is more favorable for detection due to the nonobvious phase difference caused by the overlap of the target spectrum and the clutter spectrum. Considering that the maximum eigenvalue can reflect the signal intensity and capture the signal correlations well, the maximum eigenvalue of the covariance matrix is adopted to discriminate the target from the clutter. Finally, the simulation results show that the proposed algorithm achieves superior performance.

The user-centric cloud radio access network (C-RAN) is promising for significantly reducing the channel training overhead because only the intra-cluster channel state information (CSI) is required. However, the inter-cluster interference may degrade the network performance. To address this problem, we present a novel framework for the uplink of user-centric C-RANs where the interference cancellation is posed as a system of bi-unknown vectors linear equations. To solve this unusual system of equations, we propose a quasi-least squares (QLS) algorithm and analyze its robustness by exploiting the random matrix theory. We reveal the fact that QLS is very sensitive to the channel estimation error due to involving the inverse of an ill-conditioned matrix. It is well known that truncated singular value decomposition (TSVD) is an effective regularization scheme that can mitigate this ill-conditioning effect. Accordingly, we employ TSVD to further improve the robustness of QLS against the imperfect channel estimation. In addition, since the performance of the TSVD based algorithm strongly depends on the truncation parameter, a parameter-choice method using the constant modulus (CM) feature is also provided. Finally, simulation results are presented to examine the effectiveness and robustness of the proposed method.

The codebook-based techniques are extensively utilized in analog beamforming and combining to overcome high path-loss in millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communications. However, to find the best analog precoder and combiner, a complex search based on predefined codebook is required in conventional schemes, which leads to large time cost. For the purpose of reducing complexity, we propose a new integer coded quantified angles-based particle swarm optimization (IC-PSO) beamforming algorithm. We firstly propose a joint search scheme based on integer coded (IC) quantified angles which transforms the search space into the integer field to simplify the search space. To converge to the optimal solution quickly, an improved particle swarm optimization (PSO) algorithm is further proposed. In this way, the best precoder and combiner can be found with lower complexity. Furthermore, we optimize the inertia weight and acceleration coefficients and process the out-of-bounds particles, which can improve the search ability of the PSO. Theoretical analysis indicates that the proposed IC-PSO beamforming has the lower complexity than some existing methods. Simulation results show that the algorithm has a satisfactory achievable rate which can achieve almost 98% performance of the full-search beamforming.

In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cell-center and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-no-jamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed scheme.

Non-orthogonal multiple access (NOMA) has been proposed as a promising multiple access approach for 5G mobile systems because of its superior spectrum efficiency. However, the privacy between the NOMA users may be compromised due to the transmission of a superposition of all users' signals to successive interference cancellation (SIC) receivers. In this paper, we propose two schemes based on beamforming optimization for NOMA that can enhance the security of a specific private user while guaranteeing the other users' quality of service (QoS). Specifically, in the first scheme, when the transmit antennas are inadequate, we intend to maximize the secrecy rate of the private user, under the constraint that the other users' QoS is satisfied. In the second scheme, the private user's signal is zero-forced at the other users when redundant antennas are available. In this case, the transmission rate of the private user is also maximized while satisfying the QoS of the other users. Due to the non-convexity of optimization in these two schemes, we first convert them into convex forms, and then, an iterative algorithm based on the Concave-Convex Procedure is proposed to obtain their solutions. The extensive simulation results are presented to evaluate the effectiveness of the proposed schemes.

Security is a challenging issue for cognitive radio (CR) networks. Conventionally, interference will degrade the performance of a primary user (PU) when the spectrum is shared with secondary users (SUs). However, when properly designed, SUs can serve as friendly jammers to guarantee the secure transmission of PU. Thus, in this paper, we propose a optimal transceiver design scheme to improve the sum rate of SUs while guaranteeing the secrecy rate of PU. In the scheme, the secondary transceivers are jointly designed to maximize their sum rate while satisfying a threshold on the PU's secrecy rate. Due to the non-convex nature, it is first converted into a convex one and then, an alternating optimization algorithm based on the second-order cone programming is proposed to solve it. Finally, simulation results are presented to verify the effectiveness of the proposed scheme for secure CR networks.

Security is a challenging issue for cognitive radio (CR) to be used in future 5G mobile systems. Conventionally, interference will degrade the performance of a primary user (PU) when the spectrum is shared with secondary users (SUs). However, when properly designed, SUs can serve as friendly jammers to guarantee the secure transmission of PU. Thus, in this paper, we propose two schemes to improve the sum rate of SUs while guaranteeing the secrecy rate of PU. In the first scheme, the secondary transceivers are jointly designed to maximize their sum rate while satisfying a threshold on the PU's secrecy rate. Due to the non-convex nature, it is first converted into a convex one and then, an alternating optimization algorithm based on the second-order cone programming is proposed to solve it. In the second scheme, the principle of interference alignment is employed to eliminate interference from PU and other SUs at each secondary receiver, and the interference from SUs is zero-forced at the primary receiver. Thus, interference-free transmission can be performed by the legitimate CR network, with eavesdropping towards PU disrupted by SUs. The key features and performances of the two proposed schemes are also compared. Finally, simulation results are presented to verify the effectiveness of the two proposed schemes for secure CR networks.

State-of-the-art sensing methods mostly exploit spectrum holes (SHs) in conventional frequency, time, and geography dimensions, which can hardly satisfy the increasing throughput demand of CR networks. Meanwhile, the rapid development of multi-antenna technology makes the terminal obtain the angle recognition capability. Motivated by this, this paper analyzes SHs from the angle/space domain and design a spatial sector based sensing-access scheme. In this case, the SH can be regarded as a kind of particle in spatial-temporal dimension and thus the spatial-temporal uncertainty principle (STUP) is discovered, which reveals an interesting constraint phenomenon between spatial and temporal resolutions. Based on STUP, we propose a novel spatial-temporal resolution tradeoff (STRT) scheme, whose objective is to identify the optimal spatial resolution size to maximize the throughput of CR networks. Different from the conventional temporal domain sensing-throughput tradeoff problem, we study the spatial-temporal cross-dimension optimization, thus fully exploiting SHs' spatial diversity to achieve a better performance. In addition, a fast search algorithm is proposed to track the optimal spatial resolution at an exponential convergence rate. Simulation results verify the efficiency of the proposed tradeoff scheme and search algorithm.

Two kinds of modified codewords for space–time block coded spatial modulation (STBC‐SM) are presented in this study. The first modified codeword introduces a concept of flexible coefficients into the M ‐ary phase shift keying STBC‐SM. A method is proposed to obtain the optimal flexible coefficients. Moreover, the first modified codewords can also be applied to the systems developed from the STBC‐SM. In the second provided codewords, super‐orthogonal space–time trellis codes are applied in STBC‐SM. Therefore, a higher spectral efficiency is obtained compared with the original STBC‐SM. Finally, the simulation results and theoretical analysis demonstrate these performance advantages of the two kinds of modified codewords for STBC‐SM.

The state-of-the-art eigenvalue-based spectrum sensing methods only consider the partial information of eigenvalues, such as the maximum, minimum, and mean values to make detection, which does not make full use of the eigenvalues to catch correlation. In this paper, we focus on all the eigenvalues of sample covariance matrix in multi-antenna cognitive radio networks and propose eigenvalue weighting-based detection schemes. According to the Neyman–Pearson criterion, the globally optimal weighting solution is the likelihood ratio test (LRT). Hence, we analyze and derive the eigenvalue-based LRT (E-LRT). Utilizing the random matrix theory, a simple closed-form expression for the E-LRT is obtained, which is exactly the optimal eigenvalue weighting scheme. Although the E-LRT is optimal, it is infeasible in practice due to its dependence on the knowledge of primary users and noise powers. Hence, we further analyze suboptimal methods and design maximum likelihood estimation-based approximation weighting approach. Under the approach, both semi-blind (only the noise power is known) and totally-blind methods are correspondingly proposed. In addition, the theoretical performance analysis of these proposed methods are provided. Simulation results are presented to verify the efficiency of the proposed algorithms.

For realizing robust target tracking with wireless sensor networks in the circumstance where the propagation parameters of the characteristic signal emitted by the target are unknown, a novel tracking algorithm under the particle filter framework is proposed. We propose a scheme to realize particle weight calculation without the prior knowledge about the propagation parameters of the target's characteristic signal. With the use of the monotonic relationship of the distance and the received signal strength, we define the signal characteristic sequence and particle distance sequence and utilize the modified sequence distance between the signal characteristic sequence and the particle distance sequence as the criterion to calculate the particle weight blindly with simple lightweight operations. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

Due to its ability of realizing device free localization with wireless networks, the radio tomography becomes a promising technique that draws considerable attention. Traditional radio tomography makes use of the received signal strength (RSS) of wireless links to realize location estimation. However, the RSS measurement is particularly sensitive to noise. Inspired by the fact that similar to the RSS, the time-of-flight (TOF) measurement also changes significantly when some objects shadow the wireless link, and the fact that compared with the RSS, the TOF measurement is robust to noise, a novel TOF-based radio tomography is proposed in this paper. With the TOF measurements of the shadowed links as observation information, a modified particle filter algorithm which utilizes the compressive sensing technique to produce the importance distribution of the particle set is proposed, so as to realize localization and tracking with under-sampled measurements by making full use of the space-domain sparse and time-domain gradually changed feature of the location information. The experiments with the 802.15.4a chirp spread spectrum ranging hardware are presented to confirm the proposed scheme.

Abstract Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non‐Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re‐sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. Thequality prediction functionis proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, acentroid shift vectoris calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.

We proposed a new predistortion scheme that result in a last convergence performance by using a gradual quantization on input signal value from rough to fine. We compare the performance of the proposed scheme to conventional methods. Simulation results of the OFDM system on an AWGN channel reveal that the proposed method show improved convergence performance without any loss in accuracy.

In this paper, we propose some schemes of efficient multiple access for multiuser detection in spread spectrum communications. The first scheme is based on the relationship between Walsh code and PN code, and the second and third scheme are based on new scheme called code hopping CDMA (CH-CDMA), but with different signal sets.