This paper addresses the multistatic localization problem with the receiving sensors distributed on a rigid body (RB) whose position and orientation are not known. We propose to jointly estimate the unknown object position together with the RB position and orientation, simply called joint object and rigid body localization (JORBL), using time delay (TD) measurements from both the direct-propagation and indirect-reflection paths between the transmitters and sensors. Two scenarios are considered, one with synchronization between the sensors and transmitters and the other without. We begin by investigating the benefits of having the direct-path TD measurements compared to using the indirect-path TDs only for JORBL, in terms of estimation accuracy and minimum number of transmitters and sensors needed, through analyzing the Cramér-Rao lower bound (CRLB). Afterward, in the presence of synchronization, we propose a JORBL method by formulating a non-convex constrained weighted least squares problem and applying semidefinite relaxation to obtain the solution by semidefinite programming. The proposed method is then extended to the case without synchronization. The mean squared error analysis demonstrates that the proposed methods can achieve the CRLB accuracy for small Gaussian noise, which is further confirmed by numerical simulations.

A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have emerged as promising technologies for enhancing wireless coverage and connectivity due to their mobility and reconfigurability. In this paper, we investigate a multi-user system assisted by multiple UAV-mounted RISs, where each UAV carries a RIS to enhance the communication link between the base station (BS) and users. We aim to maximize the system sum-rate by jointly optimizing the BS beamforming, the phase shifts of RISs, and the location of UAVs. To address this problem, we develop an alternating optimization algorithm based on the fractional programming framework. The original problem is decomposed into three subproblems to obtain the optimal BS beamforming, RIS phase shift, and the UAV location. Numerical results demonstrate that the proposed algorithm effectively achieves the sum-rate performance.

Low-cost and low-power consumptions have become the trend for the future communication systems, where one-bit quantization is a promising candidate. However, due to the low resolutions, the performance loss of the one-bit system is serious. To alleviate this issue, we propose leveraging the fluid reconfigurable intelligent surface (fRIS) to compensate for the loss in downlink communication systems with one-bit digital-to-analog converters (DACs). Specifically, we formulate a symbol estimation error minimization problem by jointly optimizing the symbol estimator, the one-bit transmit signal, the fRIS phase shifts, and the element positions. To handle the resultant problem, an alternating algorithm is developed, where four subproblems are solved iteratively by using semidefinite-relaxation, discrete optimization, and quadratic programming. In optimizing fRIS element positions, the positions affect both the incident and reflective channels, leading to the high-order complex objective functions. We show that these terms can be simplified by using the characteristics of channels. Numerical results validate that the introduction of fRIS can alleviate the performance loss caused by one-bit quantization.

This paper investigates the rigid body localization (RBL) problem in 3-D using hybrid time-of-arrival (TOA) and angle-of-arrival (AOA) measurements without synchronization among the sensors on the rigid body and with the anchors, where relays at unknown positions are exploited to improve the RBL performance by generating additional measurements in the relay propagation paths and enhance the localization geometry by having the relays as pseudo receivers. The large number of unknown parameters in the localization system leads to a very challenging problem. We propose a two-step method that jointly estimates the rotation angles and position of the rigid body, the relay positions, and the synchronization offsets. In the first step, a non-convex constrained least squares problem (CWLS) is formulated based on the transformed measurement models, and it is solved by semidefinite programming (SDP) after semidefinite relaxation (SDR). The CWLS solution is suboptimal due to the loss of useful information incurred by the measurement model transformations. In the second step, a refinement procedure is followed to compensate for the performance loss by solving a linear weighted least squares problem. Apart from the solution method, the performance gain from the relays is confirmed by a theoretical analysis with the Cramér-Rao lower bound (CRLB). Moreover, the mean square error performance of the proposed two-step method is shown to reach the CRLB under the small Gaussian noise condition. Simulation results validate the theoretical studies and demonstrate accurate RBL of the proposed method.

In practical underwater environments, the uncertain and varying propagation speed of an acoustic signal makes time-based localization of an acoustic source challenging. In this paper, we utilize the isogradient sound speed profile (SSP) model with random model parameters to address the underwater source localization problem using time-of-arrival (TOA) measurements. To better reflect the real-world environments, the SSP we used is indeterminate where its model parameters have unknown random deviations from the nominal values. We propose a two-step method for locating an underwater acoustic source for this highly nonlinear challenging problem. In the first step, we propose an approximation to the TOA model that takes the sound speed dependence on the source depth into consideration, and formulate a non-convex constrained weighted least squares (CWLS) problem to obtain an initial source position estimate. In the second step, we expand the original TOA model by the second-order Taylor series at the initial estimate, and then create a different non-convex CWLS problem to obtain a refined solution. Both CWLS problems are solved by applying the semidefinite relaxation technique. Moreover, the proposed method is extended to the case where the source is not time synchronized with the sensors. Furthermore, we derive the Cramér-Rao lower bound (CRLB) for this particular localization problem and show by mean squared error (MSE) analysis that the proposed method is capable of achieving the CRLB performance under small Gaussian errors in the measurements and SSP model parameters. Simulation results confirm the effectiveness of the proposed method in achieving good localization performance.

Active reconfigurable intelligent surface (A-RIS) aided integrated sensing and communications (ISAC) system has been considered as a promising paradigm to improve spectrum efficiency. However, massive energy-hungry radio frequency (RF) chains hinder its large-scale deployment. To address this issue, an A-RIS-aided ISAC system with antenna selection (AS) is proposed in this work, where a target is sensed while multiple communication users are served with specifically selected antennas. Specifically, a cuckoo search-based scheme is first utilized to select the antennas associated with high-gain channels. Subsequently, with the properly selected antennas, the weighted sum-rate (WSR) of the system is optimized under the condition of radar probing power level, power budget for the A-RIS and transmitter. To solve the highly non-convex optimization problem, we develop an efficient algorithm based on weighted minimum mean square error (WMMSE) and fractional programming (FP). Simulation results show that the proposed AS scheme and the algorithm are effective, which reduces the number of RF chains without significant performance degradation.

Mobile edge computing (MEC) has emerged as a cutting-edge technique that brings computation and storage resources closer to the edge of the mobile network. However, MEC is vulnerable to be attacked by malicious users. To improve the security of computation tasks and enhance user connectivity, we design a deep reinforcement learning (DRL) network for reconfigurable intelligence surface (RIS)-aided MEC system. Specifically, we jointly optimize the phase shifts at the RIS, tasks offloaded by users and task assignment to maximize the secrecy offloading capacity and minimize energy consumption under different delay requirements of users. Furthermore, a multi-agent twin delayed deep deterministic policy gradient (TD3)-based algorithm is exploited to tackle the non-convex optimization problem. Numerical results validate the feasibility and applicability of our proposed scheme, demonstrating that the proposed scheme significantly improves the security and energy performance of the system compared to the baseline DRL algorithm.

The family of regularization by denoising (RED) methods introduce denoising operator as the regularization term to perform compressed sensing (CS) reconstruction, which shows higher flexibility and scalability. However, traditional RED framework has strict requirements on several properties of denoiser, making it hard to design the specific denoiser and limits the quality of reconstructed images. Although some relaxation for denoisers can be made by incorporating the fixed point projection during the iteration process, the involved parameters have great impact on the effectiveness and efficiency of the algorithm, which is non-trivial to set them properly. In this paper, we propose an innovative Deep Unfolding Network framework termed FP-DUN based on the iterative process of Regularization by Denoising via Fixed-Point Projection (RED-PRO). In FP-DUN, fix-point projection module is implemented with learnable weights of neural networks, where an effective denoiser based on dual attention mechanism (DAM) is developed to capture the details of the reconstructed image. Additionally, we propose a new loss function based on fixed point constraints, which is able to overcome the over-smoothness caused by multi-stage denoising and maintain the structural details to progressively improve the reconstruction quality. By training the DUN model, the parameters for the process of fix point projection and denoiser are learned automatically. Extensive experimental results comparing with state-of-the-art CS algorithms and traditional RED-PRO approach validate the effectiveness of FP-DUN, especially on some images with complex details.

Due to the limited resources available on small synthetic aperture radar (SAR) platforms, such as unmanned aerial vehicles (UAVs), one-bit SAR imaging has emerged as a promising technique, particularly with the rapid development of low-altitude economy. One-bit SAR is capable of leveraging a very simple and cheap one-bit analog-to-digital converter (ADC) to complete the same sensing task as those in the conventional SAR using high-resolution ADC. Nevertheless, the one-bit quantization incurs some intractable problems, such as signal amplitude distortion and the emergence of unwanted interference, which seriously degrade the SAR image quality. To tackle these problems, this work proposes a one-bit SAR imaging strategy that devises a quantization threshold fixed in fast-time but fluctuating in slow-time. Specifically, within the one-bit quantization procedure for each echo pulse, the threshold is fixed, significantly simplifying the SAR system. On the other hand, the slow-time fluctuation of the threshold enables the SAR to reassign spectrum energies, effectively suppressing unwanted interference. The frequency of the threshold and the corresponding pulse repetition frequency (PRF) of the SAR system are elaborately designed. In addition, the fluctuation range of the threshold, which determines the fidelity of SAR images directly, is designed as well, and a closed-form expression for the threshold fluctuation range is determined. The effectiveness of the proposed scheme is demonstrated through the simulated and real-data experiments, confirming that high-quality one-bit SAR images can be achieved.

The Global Navigation Satellite System (GNSS) receiver has become indispensable in navigation applications due to its affordability and reliable accuracy. Despite its widespread use, GNSS is vulnerable to manipulation by malicious entities within the inherently insecure wireless landscape. This paper proposes innovative GNSS signal authentication strategies that employing multiple antennas to counteract spoofing attacks. Leveraging a multi-antenna framework enhances the distinguishability of spoofing signals over traditional single-antenna configurations. Importantly, the proposed approaches capitalize on spatial characteristics, which are independent of the GNSS signal’s intrinsic features, to accurately identify spoofing attacks. Specifically, we propose a Direction-of-Arrival (DOA)-based authentication scheme to address scenarios where authentic signals are blocked during spoofing attacks. Additionally, for situations where both authentic and spoofing signals coexist at the receiver, we propose a Spatial Filter (SF)-based authentication scheme. Through rigorous theoretical analysis and comprehensive simulations, we not only validate the proposed schemes but also demonstrate their robustness and effectiveness in enhancing GNSS signal security. The simulation results, aligning closely with theoretical predictions, underscore the superiority of the proposed schemes in safeguarding against spoofing threats.

This work is aimed at the requirements of high real-time moving target detection and localization for near-field Synthetic Aperture Radar (SAR). It addresses the challenges posed by strong spatial variation in imaging and severe coupling of Doppler parameters between the platform and the target. This work proposes a near-field Displaced Phase Center Antenna (DPCA) for clutter suppression based on instantaneous range-Doppler imaging algorithms, as well as joint dynamic target parameter estimation method using multiple channels and moments configuration. Simulation results verifies the effectiveness of the proposed approach.

The cross-range scaling technique plays a crucial role in inverse synthetic aperture radar (ISAR) imaging. It enables the determination of the absolute size of the target by transforming the ISAR image from the range-Doppler domain to the homogeneous range cross-range domain. This work devises an accurate cross-range scaling approach based on the one-bit data extracted from ISAR echoes. The proposed approach provides a novel strategy to improve the accuracy of the phase parameter estimation for cross-range scaling. It amplifies the phase of the echo via extracting one-bit data. By leveraging the amplified feature carried by the generated harmonics, the proposed approach can achieve higher accuracy while maintaining the search interval. Additionally, the proposal exhibits low computational burden and is easy to implement. The effectiveness is validated through experiments conducted on simulated and real measured data.

We address the unified near-field and far-field time-difference-of-arrival source localization problem, in which the signal propagation speed is unknown, and the prior knowledge that whether the source is in the near-field or far-field is unavailable. Using the unified near-field and far-field model obtained by expressing the source position in the modified polar representation (MPR), two different methods, the semidefinite relaxation (SDR) method that achieves noise resilience and the two-step closed-from solution method that accomplishes low complexity, are proposed to jointly estimate the MPR coordinates and the propagation speed. For the SDR method, we express the propagation speed as the sum of a pre-selected constant and the residual to form a non-convex constrained weighted least squares problem, which is then relaxed into a convex semidefinite program by applying SDR. The two-step method obtains a coarse estimate in Step 1 by solving the quadratic programming problem without considering the relations among the variables, and then refines the coarse estimate in Step 2 by estimating the correction to compensate the error resulting from ignoring the relations. Theoretical mean square error analysis confirms that the proposed methods can reach the Cramer-Rao Lower Bound performance, which is also validated by using both simulated and real data.

Reconfigurable intelligent surfaces (RISs) are deemed as a potential technique for the future of the Internet of Things (IoT) due to their capability of smartly reconfiguring the wireless propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. Existing channel estimation methods usually ignored the different channel characteristics of direct channel and reflected channels. In fact, the reflected channel can be smartly configured by adjusting the phase shifts of the RIS, which is different from the direct channel due to the different path loss exponents between the transmitter and receiver. Therefore, it is necessary to further develop a RIS-assisted channel estimation to determine the direct and reflected channels, respectively. In this paper, we study a RIS-assisted channel estimation that jointly exploits the properties of the direct and the reflected channel to provide more accurate CSI. The direct channel is estimated using weighted$\ell_1$norm minimization, while the reflected channel is modeled based upon the robust$\ell_{1,\tau}$norm minimization to sequentially estimate the channel parameters. Moreover, by combining the gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide a feasible solution. Simulation results demonstrate that an RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.

This paper addresses the localization problem in unique coordinates of a far-field object. Using moving transceivers as relays for the sensor signals in reaching the object, we utilize the range measurements from the sensors through the transceivers to the object to determine its position. The transceivers have no self-localization capability such that their motion parameters are unknown. Moreover, neither transceiver relay times nor object reflection delay are known, causing additional unknown range offsets. We propose an effective three-step method for this localization problem. The first step eliminates the object position and range offsets by formulating a constrained weighted least squares (CWLS) problem and estimates only the transceiver motion parameters. Using the preliminary estimate of the motion parameters, the second step formulates another CWLS problem to obtain the object position estimate, which is used to determine the range offsets by a linear WLS estimator. Finally, a refinement procedure follows in the third step by formulating a different CWLS problem to compensate for the performance loss. To solve the non-convex CWLS problems, we introduce semidefinite relaxation to transform them into convex semidefinite programs. Both mean square error analysis and simulation results show that the refined CWLS solution is able to achieve the Cramer–Rao lower bound performance when the SNR is not very low.

In comparison with upper-layer authentication mechanisms, the tag-based Physical-Layer Authentication (PLA) attracts many research interests because of high security and low complexity. This paper mainly concerns two problems in prior tag-based PLA schemes, where the first one is extra overhead and vulnerability due to the reason that the parameter is broadcasted and the other one is the problem of setting the parameter empirically. Therefore, two new tag-based PLA schemes are proposed to address the above limitations. Specifically, a blind tag-based PLA scheme (BTP) is presented to achieve accurate authentication without knowing the tag parameter of the legitimate transmitter, which not only saves the communication overhead but also improves security. Then, an adaptive blind tag-based PLA scheme (ABTP) is further proposed, which adaptively sets the tag parameter according to the wireless channel state to achieve a better balance among robustness, security, and compatibility. Rigorous theoretical analyses are provided for the two proposed schemes and the prior schemes’ performance comparisons are given. The accuracy of the theoretical analyses is verified through simulation results. At last, the advantages and disadvantages of the two proposed schemes are discussed, and suggestions are given according to different scenarios.

This paper addresses the rigid body localization (RBL) problem using an unsynchronized sensor network, where clock offsets between sensors and anchors are present. We first investigate RBL using unsynchronized time-of-arrival (TOA) measurements to determine the necessary and sufficient conditions for this problem by analyzing the rank of the Fisher Information Matrix. When the condition is fulfilled, a constrained weighted least squares (CWLS) problem is formulated to jointly estimate the position and rotation angles of the rigid body together with the clock offsets. Besides the non-convex nature, the CWLS problem contains nonlinear constraints resulting from the orthogonality property of the rotation matrix. To solve this difficult problem, we propose to apply semidefinite relaxation (SDR) to relax it as a convex semidefinite program. Furthermore, we investigate the RBL problem using hybrid TOA and angle-of-arrival (AOA) measurements. The Cramer-Rao Lower Bound (CRLB) analysis shows better performance of RBL by hybrid TOA-AOA than by AOA only when more than one anchors are used. The CWLS problem is extended to include the AOAs and the associated SDR method is derived. Finally, mean square error analysis shows that the proposed methods are able to achieve the CRLB performance under small Gaussian noise. Numerical results confirm the theoretical analyses and validate the performance of the proposed methods.

This paper addresses the critical challenge of securing two-way Time-of-Arrival (ToA) localization in Wireless Sensor Networks (WSNs) against cooperative distance-enlargement attacks. Existing schemes often rely on heuristic test statistics, are limited to specific attack strategies e.g., Amplify and Forward (AF), or require collaboration among multiple anchor nodes, which hinders their effectiveness against sophisticated adversaries and often incurs high communication overhead. We propose two novel detection schemes based on signal characteristics that overcome these limitations, offering both high accuracy and low complexity. The first scheme, termed the Efficient Detection Scheme of Distance-Enlargement Attacks using Log-Likelihood Ratio (EDDEA-LLR), leverages the Neyman-Pearson (NP) lemma to derive an optimal test statistic for scenarios involving AF attacks. Recognizing the limitations of existing schemes and the EDDEA-LLR scheme in countering Decode and Forward (DF) attacks, we introduce the Efficient Detection Scheme of Distance-Enlargement Attacks by Embedding a Secret Tag (EDDEA-EST). This scheme exploits embedded secret tags within the challenge signal to effectively detect DF attacks. Importantly, the proposed schemes do not require collaboration among multiple anchor nodes, eliminating the need for additional communication and featuring low computational complexity. We provide a rigorous theoretical analysis, deriving closed-form expressions for the detection performance of both schemes. Through extensive simulations, we demonstrate the superiority of the proposed schemes over existing methods in terms of detection accuracy, robustness against both AF and DF attacks, and comprehensive performance considering communication overhead.

Filter pruning has attracted increasing attention in recent years for its capacity in compressing and accelerating convolutional neural networks. Various data-independent criteria, including norm-based and relationship-based ones, were proposed to prune the most unimportant filters. However, these state-of-the-art criteria fail to fully consider the dissimilarity of filters, and thus might lead to performance degradation. In this paper, we first analyze the limitation of relationship-based criteria with examples, and then introduce a new data-independent criterion, Weighted Hybrid Criterion (WHC), to tackle the problems of both norm-based and relationship-based criteria. By taking the magnitude of each filter and the linear dependence between filters into consideration, WHC can robustly recognize the most redundant filters, which can be safely pruned without introducing severe performance degradation to networks. Extensive pruning experiments in a simple one-shot manner demonstrate the effectiveness of the proposed WHC. In particular, WHC can prune ResNet-50 on ImageNet with more than 42% of floating point operations reduced without any performance loss in top-5 accuracy.

This paper addresses the challenging problem of multistatic localization of a stationary object with a set of synchronized receivers, when the transmitter position is unknown and the synchronization with the transmitter is unavailable. Using the time delay measurements from the direct and indirect paths, we propose to jointly estimate the object and transmitter positions together with the clock offset. To accomplish the joint estimation, we first formulate a non-convex constrained weighted least squares (CWLS) minimization problem, where the approximations involved could introduce a large amount of estimation bias. We then extend the formulation to arrive at a bias-reduced CWLS (BR-CWLS) problem that has the ability of reducing the bias. The BR-CWLS problem, which is non-convex, is handled by applying the semidefinite relaxation technique to reach a convex semidefinite program that can be directly solved by a software package. Simulation results demonstrate the good performance of the proposed method in achieving the Cramer-Rao lower bound performance and reducing the bias.

Integrated dual functional radar and communication (DFRC) has been regarded as one of the most promising technologies. However, beamforming design in DFRC system is a challenge due to its nonconvexity. Inspired by the studies of active reconfigurable intelligent surface (RIS), we propose a joint transmitter and receiver design with adaptive beamforming for an active RIS aided DFRC system, where the active RIS is utilized to assist the DFRC to simultaneously detect targets and serve multiple users. Specifically, the WMMSE design criterion is exploited to design the transmit waveform of the DFRC by maximizing the sum-rate (SR) of users. Then, the Minorize-Maximization (MM) framework is derived to jointly optimize the amplification and phase shift coefficients of the active RIS. Simulation results show that when the active RIS is introduced, the SR of the system will improve greatly as compared to the scheme with passive RIS.

Spatial Modulation (SM) is a promising low-complexity modulation scheme for Multiple-Input Multiple-Output (MIMO) systems. In this paper, we address the problem of authenticating the transmitter device in the SM. We propose an authentication approach for an SM system by using Physical-Layer Authentication (PLA) mechanisms because the PLA has the following advantages: high security and low complexity. Based on the features of an SM system, we propose two PLA schemes:PLA with Superimposed Authentication Tag(PLA-SAT) andPLA with Superimposed Imaginary authentication Tag(PLA-SIT). We provide performance analyses of our schemes over fading channels in terms of robustness, compatibility, and security. Moreover, we derive their closed-form expressions under both perfect and imperfect channel estimates, including the Probability of Detection (PD), Probability of False Alarm (PFA), and Average Error Probability (AEP). Although the two proposed schemes have the same robustness and security, the PLA-SIT scheme has better compatibility than the PLA-SAT scheme. Our schemes were implemented and extensive performance comparisons through simulations were conducted. We observe that the simulation results of the two proposed schemes perfectly match their corresponding theoretical analyses. The authentication accuracy of the two proposed schemes is close to one when the received SNR is greater than 20 dB and the security performances of the two proposed schemes improve as the variance of estimation errors increases.

One-bit Synthetic Aperture Radar (SAR) imaging, which collects echoes into one-bit quantized samples, is a highly promising technique for the faster SAR images acquisition and simplified implementations in numerous applications. Harmonics, accompanied with one-bit sampling operation, are one of the fundamental factors that trigger severe aliasing artifacts problem in frequency domain. For one-bit SAR imaging, rather than sampling the echoes with extremely high rate to alleviate the effects of harmonics, a Convolutional De-Quantization Network, termed as CDQOB-net, is proposed to establish de-aliasing in a transformed imagery domain. The proposed method starts with a fresh interpretation on the regular range-Doppler imaging, which is also referred to as the 2-dimensional (2D) transformed domain of SAR echo. Then, in the light of deep learning framework for image restoration, an alternative method designed in the perspective of harmonics is specifically analyzed, wherein the degradation caused by harmonics in transformed domain are sufficiently estimated. Consequently, as a data-driven method, our two-stage CDQOB-net is proposed and trained to handle aliasing harmonics and multiple distortions, which can significantly improve the quality of one-bit SAR imaging. The proposed method is easy to implement and computationally efficient. Extensive experiments on simulated and real SAR data also demonstrate its validation and effectiveness.

It is a challenge for the state-of-the-art non-synchronous measurements beamforming method to localize multiple broa-dband sources due to the difficulty in selecting an appropriate operating frequency without any prior information about the target signals. In this paper, we propose a tensor singular value decomposition based non-synchronous measurements method for broadband multiple sound source localization. By adopting the proposed method, the working frequency range of the microphone array is no longer limited by the array geometry. While the proposed tensor completion approach via alternating direction method of multipliers algorithm could provide a sound map with distinct global view of three different speech signal sources with high accuracy in both simulation and experimental validations.

This paper addresses the multistatic localization of a moving object in position and velocity using time delay (TD) and Doppler frequency shift (DFS) measurements, where the position of the transmitter is unknown and has not yet been synchronized with the receivers. Based on the TD and DFS measurements from the direct and indirect paths, we formulate a non-convex weighted least squares (WLS) minimization problem, and then apply semidefinite relaxation (SDR) to relax the WLS problem into a convex semidefinite program. Simulation results show that the proposed SDR method is able to achieve the Cramer-Rao lower bound accuracy under mild Gaussian noise condition and outperforms the existing method.

Abstract This paper proposes a transceiver coexistence design of multiple‐input multiple‐output (MIMO) radar and MIMO communication under Gaussian model uncertainty. To tackle the mutual interference and the model uncertainty problems, the transceivers of the radar system and the communication system are jointly designed to minimise the total transmit power of the radar system and the communication system, while the signal‐to‐interference‐plus‐noise‐ratios (SINRs) or effective SINR (ESINR) of the radar system and the mean square error (MSE) of the communication system are guaranteed with small outage probability. Safe approximations are proposed for the probabilistic constraints, and a converged iterative algorithm is proposed for the joint transceiver design. Owing to the MSE formulation, the convex relaxation for the communication transmitter is proved to be tight. Furthermore, the tight communication relaxation provides an effective way to check whether the radar error is majorly due to numeric error or the possible rank‐one relaxation error. Extensive simulation results show that the proposed iterative algorithm converges, the possible radar rank‐one relaxation error is trivial with respect to the numeric error, and the quality of service in terms of SINR, ESINR, MSE and bit error rate (BER) in the proposed coexistence system are robust against model uncertainty with the cost of using extra transmit power.

Vehicular communications have witnessed remarkable achievements throughout the last few years on the way of providing reliable, safe, and affordable travel experience. Such achievements have not only skyrocketed the demand to the current services of vehicular communications, but also exploited many new services. To meet different Quality-of-Service (QoS) requirements of vehicular communications services, we assume the adoption of the hybrid OMA/NOMA-enabled access scheme as well as deploying intelligent reflecting surface (IRS) in such highly dynamic environments. We focus on maximizing the total system sum rate via jointly optimizing the transmit power allocation, the IRS’s phase shift, and the vehicle active beamforming under different QoS levels, in terms of reliability and rate constraints of vehicles. To tackle such an optimization problem, we divide the original problem into three subproblems, and propose an efficient iterative suboptimal algorithm. Specifically, the successive convex approximation (SCA) approach is applied to turn this original nonconvex problem into separate power allocation, IRS phase shift, and active transmit beamforming optimization subproblems. Simulation results demonstrate that the proposed algorithm converges fast, and the proposed design can enhance the total system sum rate through enhancing the rate within cell-edge zones regardless of the vehicle speeds.

This paper addresses two issues of a contact-tracing system with edge-based wireless communication techniques: to locate close contacts and to provide confidentiality-preserving communications. In this paper, we propose the Confidentiality-Preserving Contact-Tracing (CPCT) system with multiple wireless edge-based nodes. The CPCT system consists of three stages: registration, authentication, and confidentiality-preserving communication stages. We propose two Physical-Layer Authentication (PLA) schemes for the authentication stage of the CPCT system: the Coordinate-based Location PLA (CLP) scheme using the estimated coordinates and the Time-of-Arrival (ToA) based Location PLA (TLP) scheme using the estimated ToAs. We propose a distributed encryption scheme for the confidentiality-preserving communication stage of the CPCT system named the Distributed Channel Impulse Response (CIR)-based Encryption (DCE) scheme. We provide the theoretical analysis of the proposed schemes and derive their closed-form expressions. We implement the proposed schemes and conduct extensive performance comparisons through simulations. We observe that the theoretical results perfectly match the corresponding simulation results. Moreover, the proposed PLA schemes provide higher authentication performance than the prior PLA scheme, while the proposed encryption scheme provides higher confidentiality performance than the prior encryption scheme.

The high fidelity and fraudulence of deceptive jamming can severely mislead synthetic aperture radar (SAR), which poses a big challenge in SAR imaging. In this letter, an imagery domain target reconstruction approach is proposed to suppress deceptive jamming for single-channel SAR. Specifically, the distinction between the echo signals of true targets and deceptive jamming in the azimuth phase is ascertained first. Then, the imaging process encountering deceptive jamming is formulated as a linear model, and the target reconstruction problem is converted to a linear inverse problem with the dictionary containing the explored phase characteristics. Finally, considering the sparsity of man-made targets, the imageries of the true and false targets can be reconstructed simultaneously through solving this sparse signal recovery problem. Theoretical analysis and experimental results showcase the superiority of the proposed method.

This paper concerns the problem of defending against spoofing attacks without a secret key. We address the problem using the Physical-Layer-Authentication (PLA) because of its high security, low overhead, and high compatibility. However, many PLA schemes have the following limitations: quantization errors, local optimums, and performance loss due to the change in the communication environment. In this paper, two phase-noise-based PLA schemes are proposed to address the limitations of the prior schemes. We denote the first scheme as the Multiple Phase Noises PLA (MPP) scheme, which realizes the PLA by using multiple phase noise innovations. Note that since the MPP scheme avoids using any quantization algorithm, it outperforms the prior schemes on the authentication performance. We denote the second scheme as the Enhanced Multiple Phase Noises PLA (EMPP) scheme, which introduces an artificial random phase to the transmitted symbols at the transmitter to further improve the authentication performance. The theoretical analyses of the proposed schemes over fading channels are provided, where the closed-form expressions are derived. Theoretical comparisons between the proposed schemes and prior schemes are provided. The theoretical analyses and simulation results demonstrated the superiority of the proposed schemes. In comparison with the prior schemes, the MPP scheme achieves 13% authentication-performance gain without demodulation-performance loss, while the EMPP scheme achieves 42% authentication-performance gain with merely 16% demodulation-performance loss.

This paper concerns the problem of improving compatibility in the tag-based Physical-Layer Authentication (PLA) without sacrificing robustness of the PLA schemes. The prior PLA schemes for improving compatibility often sacrifice robustness or introduce extra communication overhead and security vulnerabilities. The main objective of our approach is to reduce the modification ratio of the source message given the tag length via an encoding approach. Based on a tag-encoding function, we propose two encoded tag-based schemes for the scenario of a single block and the scenario of multiple blocks, respectively, which are named as the Encoded Tag-based PLA scheme for Single Block (ET-SB) and the Encoded Tag-based PLA scheme for Multiple Blocks (ET-MB), respectively. We theoretically analyze the performance of the proposed schemes over fading channels and derive the closed-form expressions of the performance analyses. We implement the proposed schemes and conduct extensive performance comparisons through simulations. Our simulation results show that the closed-form expressions of the theoretical results of the proposed schemes perfectly match the corresponding simulation results. When the SNR is 10 dB, the compatibility of the ET-SB and ET-MB schemes improves to 15.63% and 23.45%, respectively, compared to the prior scheme.

This paper addresses the issue of detecting the use of information hiding at anti-copying 2D barcodes. Prior hidden information detection schemes have their roots in either heuristic-based or Machine Learning (ML). However, prior heuristics-based schemes lack a rigorous theoretical analysis. Prior ML-based information schemes lack robustness because a printed 2D barcode is very much environmentally dependent. Thus, an information hiding detection scheme trained in one environment often does not work well in another environment. In this paper, we propose two hidden information detection schemes for existing anti-copying 2D barcodes. The first scheme directly uses the pixel distance to detect the use of an information hiding scheme in a 2D barcode, referred to as the Pixel Distance Based Detection (PDBD) scheme. The second scheme first calculates the variance of raw signal and the covariance between the recovered and raw signals, and then based on the variance results, detects the use of information hiding scheme in a 2D barcode, referred to as the Pixel Variance Based Detection (PVBD) scheme. Moreover, we design advanced Illegitimately-Copying (IC) attacks to evaluate the security of two existing anti-copying 2D barcodes. We conduct extensive performance comparisons among the proposed schemes and prior schemes under different capturing devices,e.g., different scanners or camera phones. Our experimental results show that the PVBD scheme can correctly detect the existence of hidden information at both the 2LQR code and the LCAC 2D barcode. Moreover, the successful attacking probability of the proposed IC attacks achieves 0.6538 for the 2LQR code and 1 for the LCAC 2D barcode.

This paper presents a 1-bit quantization based on frequency shifted dechirping echo for vehicle-mounted synthetic aperture radar (SAR). By processing the dechirp echo with predesigned frequency shift and setting a proper sampling rate, the high-order harmonics caused by 1-bit quantization are effectively separated from the original echo. In this way, the 1-bit echoes can be range-compressed by FFT, and then a back projection (BP) algorithm can be used for imaging. The scheme is applied to image the real-measured data obtained a vehicle-mounted SAR and the effectiveness of the method is verified.

This work addresses the problem of network pruning and proposes a novel joint training method based on a multiobjective optimization model. Most of the state-of-the-art pruning methods rely on user experience for selecting the sparsity ratio of the weight matrices or tensors, and thus suffer from severe performance reduction with inappropriate user-defined parameters. Moreover, networks might be inferior due to the inefficient connecting architecture search, especially when it is highly sparse. It is revealed in this work that the network model might maintain sparse characteristic in the early stage of the backpropagation (BP) training process, and evolutionary computation-based algorithms can accurately discover the connecting architecture with satisfying network performance. In particular, we establish a multiobjective sparse model for network pruning and propose an efficient approach that combines BP training and two modified multiobjective evolutionary algorithms (MOEAs). The BP algorithm converges quickly, and the two MOEAs can search for the optimal sparse structure and refine the weights, respectively. Experiments are also included to prove the benefits of the proposed algorithm. We show that the proposed method can obtain a desired Pareto front (PF), leading to a better pruning result comparing to the state-of-the-art methods, especially when the network structure is highly sparse.

This work proposes a method to acquire the geometry of a room in terms of the size and shape by exploiting the signal propagation times of the single bound reflections (SBRs). The information of room geometry is crucial for improving localization and assisting robot navigation under complex indoor environments. The data association problem for the SBR time measurements from the same reflection surface is addressed by the minimal measurement estimator (MME) together with the density-based clustering method. A best linear unbiased estimator (BLUE) is derived to integrate the MMEs of the same surface for the room geometry estimation. Analysis shows and simulation validates that the proposed method achieves the Cramèr-Rao Lower Bound (CRLB) performance for Gaussian data model.

It has been proven that the imaging quality of 1-bit quantized synthetic aperture radar (SAR) data can be improved by using an imaging scheme of a single-frequency threshold (SFT). In this article, such a quantization model is specialized to a fixed threshold application by setting the frequency of the threshold to zero. Using multiple fixed thresholds, the conventional quantization schemes are also assimilated into this model. In this way, the performance degradation caused by low-precision quantization is analyzed in terms of harmonics, and the SFT quantization (SFTQ) strategy is addressed to handle the issue of performance degradation. The proposed quantization scheme provides significant imaging quality improvements, especially when the quantization level is low, and its advantage in computational complexity is also analyzed. Simulations on different SAR scenes show that the SFTQ, compared with the conventional quantizers, is able to guarantee 97% information in the SAR imagery while consuming less than 1/6 hardware for pulse compression. Therefore, the proposed low-precision SFTQ is a promising approach to SAR system miniaturization.

This paper concerns the problem of authenticating the transmitter without a secret key. In comparison with traditional cryptographic-based authentication mechanisms, the Physical-Layer Authentication (PLA) has the following advantages: high security, low complexity, and high compatibility, since it exploits intrinsic and unique features of the physical layer to authenticate the transmitter rather than using a secret key. The prior channel-based PLA schemes use a quantization algorithm to deal with multiple channel-based features for simplicity. However, there are two main limitations in the prior schemes: performance loss due to quantization error and the difficulty of obtaining the optimal thresholds in closed-form. In this paper, we propose two multiple Channel Impulse Response (CIR) based PLA schemes to effectively overcome the aforementioned limitations of the prior schemes. The first scheme uses multiple CIRs to realize the PLA, which is named as the Multiple CIRs PLA (MCP) scheme. The MCP scheme has better authentication performance than the prior schemes, since it avoids to use a quantization algorithm. The second scheme further improves the authentication performance by exploiting the channel correlation coefficient, which is named as the Enhanced Multiple CIRs PLA (EMCP) scheme. We provide rigorous performance analysis of two proposed schemes. We implemented the proposed schemes and conducted extensive performance comparisons through simulations. Our experimental results show that the closed-form expressions of the theoretical results of the proposed schemes perfectly match the corresponding simulation results. The EMCP scheme has the best authentication performance and the MCP scheme is the second one, whereas the prior scheme is the worst one. As the SNR or the channel correlation coefficient declines, the performance gap among various schemes gradually increases.

This paper addresses the problem of authenticating the transmitter device in wirelessly powered communications networks (WPCNs). We proposed a physical-layer authentication scheme for a WPCN. In comparison with upper-layer authentication schemes, the proposed scheme has low complexity, low power consumption, low overhead, and high security. For comprehensively analyzing the performance of the proposed scheme by considering the requirements of transmission delay, security, and reliability together, we put forward an analytical framework by considering the probabilities of transmission, security outage, connection outage, and joint security-connection outage. Based on the proposed analytical framework, we further introduced a new systematic metric by calculating the achievable throughput of all users with considering the performance of transmission-delay, security, and reliability together. We defined this new systematic metric as the overall transmission efficiency (OTE) of a WPCN, which can effectively quantize the average efficiency of message transmission in the WPCN with physical layer authentication. We analyzed the events represented by these probability factors over random fading channels and explicitly derive their closed-form expressions. For defending against jamming attacks, we further proposed another metric to estimate which user has the high probability of suffering from jamming attacks. The new metric represents that the adversary has the largest attacking gain with the minimum cost. We implemented our scheme and conducted extensive performance comparisons through simulations. Our experimental results show that the proposed scheme accurately detects an impersonating attack and drops its contribution to the sum of long-term throughput.

In this study, the authors address antenna selection (AS)‐aided massive multi‐user multiple‐input‐multiple‐output (MU‐MIMO) system based on maximum signal‐to‐noise ratio, where imperfect channel state information (CSI), time‐varying channel and antenna spatial correlation are considered. More explicitly, a computationally simple training‐based channel estimator is firstly employed for obtaining the imperfect down‐link CSI. Channel quantisation (CQ) is subsequently introduced by the feedback link which is used for feeding back so‐obtained CSI to base station. Time varying coefficient is utilised to characterise channel time variety and antenna spatial correlation is modelled by Kronecker model. Furthermore, the authors derive closed‐form ergodic channel capacity of the AS‐aided MU‐MIMO system with channel estimation error, CQ error and delay feedback, which has not yet been addressed in the state‐of‐the‐art approaches. Simulation results are provided to demonstrate the tightness of their theoretical computations of the ergodic channel capacity.

This letter develops a novel approach to determinethepose of a rigid body in an asynchronous network using the range measurements. While the sensors on the rigid body share one clock, the synchronization between distant anchors is often unavailable. The pose parameters of the rigid body are estimated in the presence of clock offsets by applying the semi-definite relaxation (SDR) technique. Then, the clock offsets are obtained by utilizing the rigid body pose information acquired in the previous step. Perturbation analysis is conducted to examine the performance of the proposed method theoretically. Simulations validate that the proposed method achieves the Cramer-Rao lower bound (CRLB) performance at low noise levels.

Sensing matrix can be designed with low coherence with the measurement matrix to improve the sparse signal recovery performance of greedy algorithms. However, most of the sensing matrix design algorithms are computationally expensive due to large number of iterations. This paper proposes an iteration-free sensing matrix design algorithm for multiple measurement vectors (MMV) compressive sensing. Specifi-cally, sensing matrix is designed in the sense of the local cumulative cross-coherence (LCCC) of the sensing matrix with respect to the measurement matrix when the number of M-MV is sufficient and the sparse signals are of full rank. Experiment results verify the effectiveness of the proposed algorithm in terms of improving the sparse signal recovery performance of greedy algorithms.

This paper proposes a novel 1-bit SAR imaging scheme with the assistance of a single-frequency threshold. Such a threshold is able to maintain the amplitude information lost in the 1-bit quantization. Moreover, the influence of high order harmonics is also suppressed due to the spectrum shifting effect brought by the threshold and its high order harmonics. The 1-bit SAR imaging quality is thus improved while the system simplification brought by 1-bit sampling, which is gained by replacing a conventional multiplier with a logic gate, can still be retained. Strategy for SAR parameter selection is discussed to guarantee the performance. Experiment based on the RADARSAT-2 data verifies the validity of the proposed scheme.

In Internet of Things, with large amounts of sensor data gathered in fusion center, it is important to detect a public hypothesis, but at the same time it is crucial to prevent a private hypothesis being detected. In order to achieve this goal, a multilayer nonlinear processing procedure is proposed to distort the sensor's data before it is sent to the fusion center. In particular, each sensor applies linear and nonlinear distortions to balance the public hypothesis test and the privacy distortion. Mirror descent methodology is reformulated to optimize the distortion matrices so as to ensure that the regularized empirical risk of detecting the private hypothesis is above a given privacy threshold, while minimizing the regularized empirical risk of detecting the public hypothesis. Experiments on empirical datasets demonstrate that the proposed approach achieves a good tradeoff between the error rates of the public and private hypotheses.

This letter addresses the problem of signal detection in additive correlated noise whose covariance matrix is Toeplitz. Particularly, we design a novel detection approach in the framework of generalized likelihood ratio test, in which the maximum likelihood (ML) estimate of the Toeplitz covariance matrix is needed. Since there are no closed-form expressions for this ML estimate, we resort to the inverse iterative algorithm. The proposed detector surpasses existing methods in detection power and enjoys the constant false-alarm rate property. Besides, accurate asymptotic null and non-null distributions of the test statistic are derived. Numerical results are presented to validate our theoretical findings.

This paper addresses a novel SAR imaging scheme based on 1-bit sampling assisted with a single-frequency threshold. The 1-bit sampling approach is able to considerably reduce the quantization cost. However, when the sampling technique simplifies the SAR system by reducing the word length of each sample to only 1 bit, amplitude information of the SAR echo is lost and high-order harmonics are introduced, degrading the SAR imaging quality. The strategy of single-frequency threshold is able to linearly maintain the amplitude information and shifts the spectra of the harmonics away from the imaging component caused by the intermodulation. Hence, the imaging quality using 1-bit sampled data can be guaranteed. By selecting different SAR parameter groups according to a comprehensive consideration on spectrum aliasing, filter mismatching, and radio frequency interfering, a good tradeoff can be achieved between imaging quality and system simplification. Examples of ideal scatterers and a real measured scene are provided for quantitative analysis. Real measured RADARSAT-1 data are also imaged using the proposed scheme to validate its effectiveness.

We consider the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to the majority of existing NOMA schemes using continuous Gaussian distributed inputs, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, whose sizes are not necessarily the same. By adjusting the scaling factors (i.e., instantaneous transmitting powers) of both users, we aim to maximize the minimum Euclidean distance of the received sum-constellation for a maximum likelihood (ML) receiver. The formulated problem is a mixed continuous-discrete optimization problem and in general it is nontrivial to resolve. By carefully examining the structure of the objective function, we discover that Farey sequence can be employed to tackle the formulated problem. However, the existing Farey sequence is not applicable when the constellation sizes of the two users are different. To address this challenge, we define a new type of Farey sequence, termed punched Farey sequence. Based on this new definition and its properties, we manage to attain a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all the subintervals. Finally, computer simulations are carried out to verify our theoretical analysis, and to demonstrate the advantages of the proposed NOMA over known orthogonal and non-orthogonal designs.

This paper proposes a novel approach to sparse recovery assisted direction-of-arrival (SR-DOA) estimation. By exploiting the sparsity inherent in the spatial spectrum, the DOA estimation is formulated as a sparse nonnegative least squares problem. Meanwhile, in order to enhance the estimation accuracy, the devised method is able to suppress the additive Gaussian noise but at the expense of a few degrees-of- freedom, and mitigate the sampling errors by exploiting its asymptotic distribution. Subsequently, the sparse Bayesian learning with nonnegative Laplace prior is utilized to yield the DOA estimation. The performances of the proposed SR-DOA estimator along with other two existing approaches are investigated and compared. Numerical results show that the proposed SR-DOA algorithm is superior to the state-of-the-art methods in terms of the estimation accuracy.

Multiple-input multiple-output (MIMO) radar systems have a wide range of applications not only in military fields but also in civilian areas. This wide applicability of MIMO radars is due to their improved spatial resolution and flexibility of designing their transmit beampatterns. Motivated by this and the increasing interest in obtaining the optimum beampattern through designing the covariance matrix of the transmit waveform, this paper presents two new algorithms for designing the beampattern of MIMO radar systems through optimizing the covariance matrix for the transmitting waveform. The first algorithm is based on the iteratively reweighted least squares of the deviation errors between the designed and desired beampatterns. The second one depends on the least absolute shrinkage selection operator (LASSO), which shrinks some deviation errors elements and sets others to zero, hence achieving a good trade-off between the sparsity and errors of the devised beampattern. Numerical simulations are carried out to prove the superiority of our proposed algorithms in case of both symmetric and non-symmetric beampatterns.

This letter addresses the problem of tensor completion. The properties of the tensor tubal rank (TTR) and tensor Kronecker rank are first discussed, and then a novel generalized tubal Kronecker decomposition together with a new tensor rank referred to as generalized tensor tubal rank (GTTR) are defined. It is shown that the GTTR is suitable for revealing both the Kronecker and tubal structures of a tensor. The general tensor completion idea is then presented following the procedure of alternate projection between tensor rank minimization and Frobenius-norm optimization. Furthermore, the GTTR minimization is relaxed to the problem of generalized tensor nuclear norm (TNN) minimization, and two solutions are derived. The first one is based on the idea of combining all generalized TNNs as a weighted sum, while the second one employs the alternate cancelation scheme. Experiments are also carried out using both simulated data and real datasets for comparison of the proposed and the state-of-the-art approaches.

This paper considers the problem of reconstructing true targets in a single-channel synthetic aperture radar (SAR) imaging system, which has been disturbed by deceptive jammings. Since the deceptive jammings are usually confined to the main lobe of an SAR antenna, their time-frequency distributions are different from those of the true echoes. This enables us to utilize a dynamic synthetic aperture (DSA) scheme to extract the characteristics of the true and false targets. Dictionaries about the true and false targets are constructed by taking interactions between scatterers into account. Then a sparsity-driven optimization problem is solved to reconstruct the true and false targets separately with super-resolution. Moreover, the deceptively jammed SAR data are divided into different areas to handle various scenarios efficiently, and strategies for DSA selection are addressed as well. Simulations are provided to verify the effectiveness of the proposed algorithm.

Usually, it is very difficult to determine the exact distribution for a test statistic. In this paper, asymptotic distributions of locally most powerful invariant test for independence of complex Gaussian vectors are developed. In particular, its cumulative distribution function (CDF) under the null hypothesis is approximated by a function of chi-squared CDFs. Moreover, the CDF corresponding to the non-null distribution is expressed in terms of non-central chi-squared CDFs for close hypothesis, and Gaussian CDF as well as its derivatives for far hypothesis. The results turn out to be very accurate in terms of fitting their empirical counterparts. Closed-form expression for the detection threshold is also provided. Numerical results are presented to validate our theoretical findings.

This paper focuses on the non-orthogonal multiple access (NOMA) design for a classical two-user multiple access channel (MAC) with finite-alphabet inputs. In contrast to most of the existing NOMA designs using continuous Gaussian input distributions, we consider practical quadrature amplitude modulation (QAM) constellations at both transmitters, the sizes of which are assumed to be not necessarily identical. We propose maximizing the minimum Euclidean distance of the received sum constellation with a maximum likelihood (ML) detector by adjusting the scaling factors (i.e., instantaneous transmitted powers and phases) of both users. The formulated problem is a mixed continuous-discrete optimization problem, which is nontrivial to resolve in general. By carefully observing the structure of the objective function, we define a new type of Farey sequence, termed punched Farey sequence to tackle the formulated problem. Based on this, we manage to achieve a closed-form optimal solution to the original problem by first dividing the entire feasible region into a finite number of Farey intervals and then taking the maximum over all possible intervals. The resulting sum constellation is proved to be a regular QAM constellation of a larger size, and hence, a simple quantization receiver can be implemented as the ML detector for the demodulation. Moreover, the superiority of NOMA over time-division multiple access in terms of minimum Euclidean distance is rigorously proved. We subsequently address how to extend our design framework intended for the two-user MAC to systems with multiple users and multiple antennas. Finally, simulation results are provided to verify our theoretical analysis and demonstrate the merits of the proposed NOMA over existing orthogonal and non-orthogonal designs.

In this paper, we consider the design of multi-users space-time modulation (MUSTM) for an uplink MIMO system with one base station equipped with the massive number of antennas and N single-antenna users, where it is assumed that only large scale channel coefficients are available at both the transmitter and the receiver. For such a system, a novel concept called uniquely factorable (UF) MUSTM is introduced. Then, using our recently developed framework on uniquely decomposable constellation group with energy-efficient quadrature amplitude modulation (QAM), and properly and timely assigning each sub-constellation to each user at each time slot, we develop a machinery method for systematically designing a family of invertible UF-MUSTM with flexible data rates in order to assure the reliable estimation of the transmitted signal as well as of the channel for the massive MIMO system. In addition, a simple cross-correlation receiver is proposed to efficiently and effectively detect such UF-MUSTM. Its pair-wise error probability (PEP) is derived, showing that our proposed invertible UF-MUSRM enables full receiver diversity. Furthermore, the optimal closed-form power allocation and the optimal user constellation assignment are found to maximize the worst-case coding gain under a peak power constraint on each user and each time slot.

This paper focuses on the design of non-orthogonal multiple access in a classical two-transmitter two-receiver Z-channel, wherein one transmitter sends information to its intended receiver from the direct link while the other transmitter sends information to both receivers from the direct and cross links. Unlike most existing designs using (continuous) Gaussian input distribution, we consider the practical finite-alphabet (i.e., discrete) inputs by assuming that the widely used quadrature amplitude modulation constellations are adopted by both transmitters. To balance the error performance of two receivers, we apply the max-min fairness design criterion in this paper. More specifically, we propose to jointly optimize the scaling factors at both transmitters, which control the minimum Euclidean distance of transmitting constellations, to maximize the smaller minimum Euclidean distance of two resulting constellations at the receivers, subject to an individual average power constraint at each transmitter. The formulated problem is a mixed continuous-discrete optimization problem and is thus intractable in general. By resorting to the Farey sequence, we manage to attain the closed-form expression for the optimal solution to the formulated problem. This is achieved by dividing the overall feasible region of the original optimization problem into a finite number of sub-intervals and deriving the optimal solution in each sub-interval. Through carefully observing the structure of the optimal solutions in all sub-intervals, we obtain compact and closed-form expressions for the optimal solutions to the original problem in three possible scenarios defined by the relative strength of the cross link. Simulation studies are provided to validate our analysis and demonstrate the merits of the proposed design over existing orthogonal or non-orthogonal schemes.

This letter concentrates on the problem of spectral compressed sensing in impulsive noise, which aims to recover a spectrally sparse signal from its contaminated and undersampled measurements. We propose a robust formulation for joint sparse signal and frequency recovery, which includes the generalized ℓpnorm(02approach via majorizing the original objective function by a quadratic surrogate function. Simulation results illustrate that the proposed approach attains a significant performance improvement over the existing methods under impulsive noise.

Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

This paper considers the sparse recovery problem of multiple measurement vector (MMV) model corrupted in impulsive noise. To ensure outlier-robust sparse recovery, we formulate an MMV problem that includes the generalized ℓp-norm (12,0joint sparsity-promoting regularizer. The joint sparse penalty, however, is non-continuous and hence non-differentiable, which inevitably raises difficulty in optimization when using a gradient-based method. To address this, we build a smooth approximation for the ℓ2,0-based sparse metric via the log-sum based sparse-encouraging surrogate function. Then, we propose a block successive upper-bound minimization algorithm for the smooth MMV problem by solving a series of subproblems based on the block coordinate descent (BCD) method. Furthermore, local convergence of the proposed algorithm to a stationary point of the smooth problem is proved. Experiments demonstrate its efficiency and robust recovery performance for suppressing impulsive noise.

Phase retrieval has recently attracted renewed interest. It is revisited here through a new approach based on nonconvex quadratically constrained quadratic programming (QCQP). A least-squares (LS) formulation is adopted, and a recently developed non-convex QCQP approximation technique called feasible point pursuit (FPP) is tailored to obtain a new LS-FPP phase retrieval algorithm. The Cramér-Rao bound (CRB) is also derived for phase retrieval under additive white Gaussian noise. We demonstrate through simulations that the LS-FPP method outperforms the prior art and its mean square error approaches the CRB.

In this paper, parameter estimation for multi-dimensional sinusoids in additive impulsive noise is addressed. Our underlying idea is to minimize the ℓp-norm of the residual error tensor, where 12-norm minimization. In doing so, we can utilize the tensorial structure of the received data and then apply iteratively reweighted tensor singular value decomposition, referred to as IR-t-SVD, to recover the subspace or the signal tensor. After the recovery step, standard subspace techniques can be applied for parameter estimation. Based on the numerical results, IR-t-SVD outperforms several state-of-the-art methods in terms of mean square frequency error under α-stable noise.

John's test, which is also known as the locally most invariant test for sphericity of Gaussian variables, is one of the most frequently used methods in multichannel signal detection. The application of John's test requires closed-form and accurate formula to set threshold according to a prescribed false alarm rate. Asymptotic expansion is a powerful method in deriving the threshold expressions of detectors for large samples. However, the existing asymptotic analysis of John's test in the real-valued Gaussian case is not accurate, causing the obtained false alarm rate to deviate from the preset value. This work first corrects a miscalculation in the existing results. Then this accurate approach is extended to the complex-valued case. In this scenario our result is as accurate as the state-of-the-art scheme but enjoys higher computational efficiency.

Besides noise and potential targets, there usually exists jamming, which can significantly degrade detection performance of a detector. In this letter, we analyze the statistical performance of the adaptive orthogonal rejection detector (AORD), recently proposed for the case of completely unknown jamming. We derive closed-form expressions for the probabilities of detection and false alarm and show how the jamming affects the detection performance. The theoretical results are verified by Monte Carlo (MC) simulations.

Joint estimation of the directions-of-arrival (DOAs) and frequencies of multiple signals is addressed in this paper. By constructing a set of joint diagonalization matrices, two cost functions that do not require a priori information of the source number are devised for DOA and frequency estimation in a separate manner. This enables us to estimate DOAs and frequencies via two one-dimensional search steps in their corresponding spatial and frequency domains. Thus, the tremendous two-dimensional search required in the standard approaches can be avoided. Simulation results demonstrate the effectiveness of the proposed approach.

Motion compensation (MOCOMP) is a key procedure in inverse synthetic aperture radar (ISAR) imaging because the accuracy of estimated parameter has a strong influence on the imaging quality. Generally, the backscattered signal of a moving target is sampled in fast time dimension, which can be approximated as the combination of multiple Chirp signals with a proper Chirp rate. Compared with the Fourier transform, the fractional Fourier transform (FrFT) performs better compression property due to its unique energy focus ability to Chirp signals. An improved Cross-correlation method based on FrFT for parameter estimation is presented in this paper. It employs the correlation between range profiles compressed by FrFT to enhance the quality of parameter estimation for ISAR applications. The method takes good balance between accuracy and complexity, and is robust to noise. Simulation results show that the proposed method outperforms the conventional Cross-correlation Method in terms of ISAR translational MOCOMP.

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

From the co-array perspective, sparse spatial sampling can significantly increase the degrees-of-freedom (DOFs), enabling us to perform underdetermined direction-of-arrival (DOA) estimation. By leveraging the increased DOFs from the sparse spatial sampling, we develop a new underdetermined DOA estimation method for wideband signals, named wideband sparse spectrum fitting (W-SpSF) estimator. In W-SpSF, we formulate a sparse reconstruction problem that includes a quadratic$({\ell_2})$weighted covariance fitting term added to a sparsity-promoting$({\ell _{2, 1}})$regularizer. Meanwhile, the optimal regularization parameter of W-SpSF is studied to ensure robust sparse recovery. Numerical results enabled nested arrays demonstrate that the W-SpSF estimator outperforms the spatial smoothing based MUSIC algorithm and works well in nonuniform noise environment.

We consider the problem of sparse signal recovery from one-bit measurements. Due to the noise present in the acquisition and transmission process, some quantized bits may be flipped to their opposite states. These bit-flip errors, also referred to as the sign-flip errors, may result in severe performance degradation. To address this issue, we introduce a robust Bayesian compressed sensing framework to account for sign flip errors. Specifically, sign-flip errors are considered as a result of a sparse noise-corrupted model in which original (unquantized) observations are corrupted by sparse (impulse) noise. A Gaussian-inverse Gamma hierarchical prior is assigned to the noise vector to promote sparsity. Based on the modified hierarchical model, we develop a variational expectation-maximization (EM) algorithm to identify the sign-flip errors and recover the sparse signal simultaneously. Numerical results are provided to illustrate the effectiveness and superiority of the proposed method.

In this work, the volume-based method for spectrum sensing is analyzed, which is able to provide the desirable properties of constant false-alarm rate, robustness against deviation from independent and identically distributed (IID) noise and being free of noise uncertainty. By computing the first and second moments for the signal-absence and signal-presence hypotheses together with using the Gamma distribution approximation, we derive accurate analytic formulae for the false-alarm and detection probabilities for IID noise situations. This enables us to develop theoretical decision threshold as well as receiver operating characteristic. Numerical results are presented to validate our theoretical findings.

Hadamard ratio test is a well-known approach to robust signal detection in multivariate analysis. Recently, it has been exploited for robust spectrum sensing in cognitive radio, but its detection performance is not yet completely analyzed. This work is devoted to accurate detection performance analysis of the Hadamard ratio method for robust spectrum sensing. By computing the first and second exact negative moments for the signal-presence hypothesis along with employing the Beta distribution approximation, we derive accurate analytic formulae for detection probability. This enables us to theoretically evaluate the detection behavior of the Hadamard ratio test. Numerical results are presented to validate our theoretical findings.

Spectrum sensing is a fundamental problem in cognitive radio. In this paper, we introduce two spectrum sensing methods based on Gerschgorin disk. The Gerschgorin radii contain the information of signal subspace, whereas the Gerschgorin centers capture the signal energy. The first proposal only relies on the Gerschgorin radii and thereby is robust against nonuniform noise. The second one, utilizing both the Ger-schgorin radii and centers, can significantly improve the detection performance. Simulation results are included to illustrate the superiority of the proposed methods.

A structured least squares based ESPRIT method is devised for joint direction-of-arrival and frequency estimation. By considering the errors in the estimated signal subspace and employing an iterative minimization procedure, the proposed approach is able to efficiently refine the estimated signal subspace, leading to significant enhancement in estimation performance. Simulation results demonstrate the effectiveness of the proposed approach.

A novel pseudo-noise resampling (PR) based unitary root-MUSIC algorithm for direction-of-arrival (DOA) estimation is derived in this letter. Our solution is able to eliminate the abnormal DOA estimator called outlier and obtain an approximate outlier-free performance in the unitary root-MUSIC algorithm. In particular, we utilize a hypothesis test to detect the outlier. Meanwhile, a PR process is applied to form a DOA estimator bank and a corresponding root estimator bank. We propose a distance detection strategy which exploits the information contained in the estimated root estimator to help determine the final DOA estimates when all the DOA estimators fail to pass the reliability test. Furthermore, the proposed method is realized in terms of real-valued computations, leading to an efficient implementation. Simulations show that the improved MUSIC scheme can significantly improve the DOA resolution at low signal-to-noise ratios and small samples.

Recently, the CORe CONsistency DIAgnostic (CORCONDIA) has attractedmore and more attention as an effective tool for determining the number of components in parallel factor analysis (PARAFAC) or Tucker 3 models. In CORCONDIA, a proper user-defined threshold is required to ensure reliable performance. The optimal threshold increases with the signal-to-noise ratio (SNR), which results in significant probability of over-enumeration of the number of components for high SNRs under fixed threshold settings. We propose to first use a threshold interval to obtain lower and upper bounds of the estimates. The estimate takes the upper bound as its initial value and is then refined based on a sequence of hypothesis tests by exploiting the reconstruction error of the PARAFAC decomposition. The proposed scheme provides accurate detection for both low and high SNRs at almost no extra computational cost.

A novel R-dimensional (R ≥ 3) model order selection (MOS) criterion is proposed for estimating the number of sources embedded in noise. By extending the classical r-mode matrix unfolding of a Rth-order measurement tensor to multi-mode matrix unfolding, (2R−1− 1) unfolded matrices are obtained. To maximize the identifiability, the unfolded matrix whose number of rows is closest to that of the columns is chosen. Meanwhile, as the so-obtained unfolded matrix is of large size, a sequence of nested hypothesis tests on its associated eigenvalues is utilized for MOS in the framework of the random matrix theory. The maximum number of sources the proposed enumerator able to identify is on the order of the square root of the product of all dimension sizes, whereas the identifiability of existing criteria is limited to the maximum dimension size minus one. Numerical results are included to illustrate the performance of the proposed enumerator.

In civilian communication systems, the signature sequence of the desired signal in training phase is known to the receiver. In this letter, using the mutual information, we bridge the probability density function and minimum mean-square error (MMSE) between the observed data and training sequence of the desired signal, and then employ the MMSE to construct a minimum description length (MDL) criterion for accurate source enumeration. Numerical results demonstrate that the proposed method is superior to existing MDL methods in terms of detection performance particularly for small number of snapshots and/or source angular separation.

A low-complexity method for source enumeration is proposed in this letter. Given the training data of a desired signal, an array data matrix is partitioned into orthogonal signal and noise components. The noise components are then used to calculate the total description length required to encode the array data. The model with the minimum description length (MDL) is chosen as the best model. Unlike the traditional MDL methods, the proposed method linearly partitions the array data into the cleaner signal and noise components and thereby is more accurate and computationally efficient. Its performance is demonstrated via numerical results.

A low-complexity ESPRIT method for direction-of-arrival (DOA) estimation is proposed in this paper. Unlike the conventional subspace based methods for DOA estimation, the proposed method only needs the training data of one signal to perform the forward recursions of the multi-stage Wiener filter (MSWF), does not involve the estimate of the covariance matrix or its eigendecomposition. Thus, the proposed method is computationally advantageous. Numerical results are given to illustrate the performance of the proposed method.