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This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user. [ABSTRACT FROM PUBLISHER]

The aspects of sum rate and fairness for dirty paper coding (DPC) based MIMO downlink communications are investigated in this paper. We first apply the $\ell _{1}$ -norm fairness measure to formulate the problem of fairness maximization for a given sum rate as an optimization problem. The problem is unfortunately nonconvex and cannot be efficiently solved. To overcome the difficulty, we invoke the uplink-downlink duality to transform the problem back and forth between uplink and downlink communications. An efficient, iterative waterfilling based algorithm is then proposed to yield achievable rates with the best possible fairness values. Simulation results show that the proposed approach offers an enormous gain in the achievable sum rates for a wide range of fairness values, when compared to the popular successive zero-forcing DPC-based and block diagonalization based coding schemes. [ABSTRACT FROM AUTHOR]

This paper presents a modified orthogonal propagator method (OPM) with spline interpolation for direction of arrival (DoA) estimation. The conventional OPM is based on the noise-free signal model. When noise exists, the main contribution of noise (additive temporally and spatially white Gaussian noise) is in the diagonal elements of the data covariance matrix. Therefore, the influence of noise can be minimized by restoring the noise free diagonal elements through an interpolation procedure and the propagator can then be directly extracted from the denoised data covariance matrix, which is the main idea of the proposed method in this paper. The performance of the proposed modified OPM is tested through numerical simulations. Simulation results show that the proposed modified OPM achieves low root-mean square errors (RMSEs) with small computational load in DoA estimation. [ABSTRACT FROM AUTHOR]

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains in this paper. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. Furthermore, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used in hybrid massive MIMO systems with multiple RF chains. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

We consider the problem of multiantenna source detection with dimension reduction in distributed wireless networks. Traditional strategies typically collect and perform raw data at the fusion center. In moderate-to-large-scale networks, however, the schemes create the bottleneck of computation and communication for high-dimensional data. The goal of this paper is to design a distributed algorithm in multiple-antenna wireless networks to project the raw data into the low-dimensional data to reduce the communication and computation burden while maintaining high detection performance. In this paper, a pseudosketching matrix is constructed to transform the raw data of each multiple-antenna node into the low-dimensional data. Furthermore, it is transformed into the vector by using the subspace method. By gathering the data vectors of all nodes at the fusion center, the eigenvalue-based detection methods, such as the generalized likelihood ratio test (GLRT), can be applied directly to determine if the source signal exists or not. Moreover, using the concentration inequalities of subgamma random variables, the theoretical analysis is derived to support the claim that the proposed algorithm has high detection performance. The simulations are presented to demonstrate the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper studies a cognitive radio network (CRN), in which a multiple-input single-output (MISO) secondary transmitter (SU-Tx) aims to send confidential messages to its receiver (SU-Rx) in the presence of unknown eavesdroppers, while having to control its generated interference to the primary users (PUs) under a given threshold. Because the eavesdroppers are unknown, this paper considers the artificial noise (AN) approach or masked beamforming to provide the information secrecy. The objective of this paper is to maximize the power of AN in order to degrade the eavesdroppers, subject to the signal-to-interference-plus-noise ratio (SINR) constraint at the SU-Rx, as well as the interference temperature limits (ITLs) for the PUs. In the case of perfect channel state information (CSI), we reveal that the optimal strategy for the information-bearing signal is beamforming. Imperfect CSI cases of bounded and stochastic uncertainties are investigated. In the case of ellipsoid-bounded errors, we derive the equivalent forms for the SINR and ITL constraints and then transform the optimization problem into a form of semidefinite programming (SDP). For the case of probabilistic CSI uncertainties, we propose an outage-constrained robust formulation where the CSI errors are Gaussian distributed. With the aid of two kinds of Bernstein-type inequalities, we reexpress the probabilistic constraints into deterministic forms, which results in a safe approximate solution. [ABSTRACT FROM PUBLISHER]

In this paper, a low complexity massive multiple-input multiple-output technique is studied with a geometry-based stochastic channel model, called COST 2100 model. We propose to exploit the discrete-time Fourier transform of the antenna correlation function to perform user scheduling. The proposed algorithm relies on a tradeoff between the number of occupied bins of the eigenvalue spectrum of the channel covariance matrix for each user and spectral overlap among the selected users. We next show that linear precoding design can be performed based only on the channel correlation matrix. The proposed scheme exploits the angular bins of the eigenvalue spectrum of the channel covariance matrix to build up an “approximate eigenchannels” for the users. We investigate the reduction of average system throughput with no channel state information at the transmitter (CSIT). Analysis and numerical results show that while the throughput slightly decreases due to the absence of CSIT, the complexity of the system is reduced significantly. [ABSTRACT FROM AUTHOR]

In multiantenna downlink, linear precoders like channel inversion (CI), regularized CI (RCI), and phase alignment (PA) are more desirable than their nonlinear counterparts due to their reduced complexity. However, to exploit the full benefits of multiuser downlink, the availability of perfect channel state information (CSI) at the base station (BS) is mandatory. Since such an assumption is not realistic in practice, subject to CSI mismatch, the downlink precoders may undergo a severe degradation in performance. Therefore, an adaptive linear precoder is required to achieve better performance under the availability of imperfect CSI at the BS. In this paper, we propose such a linear precoder by judiciously picking up an optimized regularization parameter based on the knowledge of the channel estimation error variance in advance. To do so, we first introduce a generalized CSI mismatch model where the variance of the channel estimation error is a function of the signal-to-noise ratio (SNR) and is thus able to accommodate a variety of distinct scenarios such as reciprocal channels and CSI feedback. It is shown that the proposed precoding scheme, dubbed adaptively regularized PA (RPA), is capable of achieving lower bit error rates (BERs) compared to standard linear precoders under both perfect and imperfect CSI. [ABSTRACT FROM PUBLISHER]

Multiple remote antenna units (RAUs) cooperate to serve a certain user in cell-free distributed massive multiple-input multiple-output (MIMO) systems. Due to the different distances between each RAU and the user, asynchronous reception effects will occur. This paper studies the impacts of asynchronous reception effects on cell-free distributed massive MIMO systems. We first derive the minimum-mean-square error (MMSE) channel estimation under the asynchronous reception effects. Given the estimated channel state information, we derive the closed-form expressions of the user achievable rates with maximum ratio transmission (MRT) and regularized zero-forcing (RZF) precoders, which are further used to analyze the impacts of asynchronous reception effects. The accuracy of the closed-form expressions are verified. Simulation results demonstrate that asynchronous reception effects cause a non-negligible drop on the system spectral efficiency. Moreover, the performance degradation is proportional to the subcarrier sequence number and even more serious with the increasing of signal-to-noise ratio (SNR) or the number of antennas per RAU. [ABSTRACT FROM AUTHOR]

Effective suppression of inadvertent or deliberate jamming signals is crucial to ensure reliable wireless communication. However, as demonstrated in this paper, when the transmitted jamming signals are highly correlated, and especially when the correlation coefficient varies, nullifying the jamming signals can be challenging. Unlike existing techniques that often assume uncorrelated jamming signals or non-zero but constant correlation, we analyze the impact of the non-zero and varying correlations between transmitted jamming signals on the suppression of the jamming signals. Specifically, we observe that by varying the correlation coefficients between transmitted jamming signals, jammers can “virtually change” the jamming channels hence their nullspace, even when these channels do not physically change. This makes most jamming suppression techniques that rely on steering receiving beams towards the nullspace of jamming channels no longer applicable. To tackle the problem, we develop techniques to effectively track the jamming nullspace and correspondingly update receiving beams. Monte Carlo simulations show that our proposed techniques can suppress/nullify jamming signals for all considered scenarios with non-zero and varying correlation coefficients amongst transmitted jamming signals. [ABSTRACT FROM AUTHOR]

In cooperative cognitive radio networks (CRNs), multiple secondary base stations are managed by a central unit (CU) via finite-capacity backhaul links, which performs joint encoding and precoding of the messages intended for the secondary users (SUs). In this paper, the limitation of backhaul links is handled with the implementation of compression strategies, by which precoding is followed at the CU. In particular, focusing on underlay cooperative CRNs, we propose the joint optimal design of precoding and backhaul quantization with the aim of maximizing the sum throughput of the secondary network under limited backhaul capacity, transmit power, and interference power constraints. Furthermore, both perfect and imperfect channel state information (CSI) available at the CU are considered. In the imperfect-CSI case, the uncertainty of the channels is explicitly accounted for via a robust or worst-case optimization formulation. The proposed algorithmic solutions leverage rank relaxation and majorization-minimization programming, whose performances are evaluated via numerical results. [ABSTRACT FROM PUBLISHER]

In this paper, a new unsupervised carrier frequency offset (CFO) estimation method is proposed for orthogonal frequency division multiplexing (OFDM) systems using constant modulus constellations. Under the assumption that the channel variation is slow within two adjacent OFDM symbols, the influence of the channel on corresponding subcarriers of the two OFDM symbols will be the same. Utilizing this feature, a robust cost function is formulated based on covariance fitting criterion between two nearby OFDM symbols, and the CFO is estimated by minimizing this objective function. The mean square error (MSE) and bit error rate (BER) performance of the proposed covariance-fitting-based CFO estimation method is compared with that of the prominent conventional estimation schemes under noisy multipath channels with high delay spreads and Doppler spreads. The effect of channel length on the performance of the proposed estimator is also investigated. Based on Monte Carlo simulations, it is shown that the proposed method is robust under different channel conditions and provides more accurate CFO estimates. [ABSTRACT FROM PUBLISHER]

Conventional direction-of-arrival (DOA) estimation algorithms like MUSIC only allow localization of fewer number of sources than the number of physical sensors. In this paper, underdetermined azimuth localization (localizing more sources than the number of sensors) using arbitrary planar arrays has been proposed, using only second-order statistics of the received data. To achieve this, we utilize the difference coarray of the actual array and express the elements of the array covariance matrix as the signal received by the virtual sensors of the coarray. We explore the structure and geometry of the difference coarray of an $N$ -element planar array and show that the coarray can provide an increased degree-of-freedom (DOF) of $\mathcal {O}(N^{2})$ which enables underdetermined localization. Then, we extend the manifold separation (MS) technique to the coarray to express the coarray steering matrix in terms of a Vandermonde structured matrix by designing a signal independent coarray characteristic matrix. As the signal model of a coarray is a single snapshot model, the Vandermonde structure enables us to perform a spatial smoothing type operation to restore the rank of the coarray covariance matrix. This allows us to propose a novel subspace-based algorithm, which we call the coarrayMS-MUSIC, to perform underdetermined source localization using arbitrary planar arrays. We have also introduced the polynomial rooting version of our algorithm called the coarrayMS-rootMUSIC. Finally, we have conducted extensive numerical simulations to verify the effectiveness and usefulness of the proposed methods. [ABSTRACT FROM AUTHOR]

As the network slicing is one of the critical enablers in communication networks, one anomalous physical node (PN) or physical link (PL) in substrate networks that carries multiple virtual network elements can cause significant performance degradation of multiple network slices. To recover the substrate networks from anomaly within a short time, rapid and accurate identification of whether or not the anomaly exists in PNs and PLs is vital. Online anomaly detection methods that can analyze system data in real-time are preferred. Besides, as virtual nodes and links mapped to PNs and PLs are scattered in multiple slices, the distributed detection modes are required to adapt to the virtualized environment. According to those requirements, in this paper, we first propose a distributed online PN anomaly detection algorithm based on a decentralized one-class support vector machine (OCSVM), which is realized through analyzing real-time measurements of virtual nodes mapped to PNs in a distributed manner. Specifically, to decouple the OCSVM objective function, we transform the original problem to a group of decentralized quadratic programming problems by introducing the consensus constraints. The alternating direction method of multipliers is adopted to achieve the solution for the distributed online PN anomaly detection. Next, by utilizing the correlation of measurements between neighbor virtual nodes, another distributed online PL anomaly detection algorithm based on the canonical correlation analysis is proposed. The network only needs to store covariance matrices and mean vectors of current data to calculate the canonical correlation vectors for real-time PL anomaly analysis. The simulation results on both synthetic and real-world network datasets show the effectiveness and robustness of the proposed distributed online anomaly detection algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the anti-jamming problem of a directional modulation (DM) system with the aid of intelligent reflecting surface (IRS). As an efficient tool to combat malicious jamming, receive beamforming (RBF) is usually designed by using the statistical properties of the jamming received by Bob. Thus, it is very necessary to estimate the receive jamming covariance matrix (JCM) at Bob. To achieve a precise JCM estimation, three JCM estimation methods, including eigenvalue decomposition (EVD), parametric estimation method by gradient descend (PEM-GD) and parametric estimation method by alternating optimization (PEM-AO), are proposed. Here, the proposed EVD is derived according to the rank-2 constraint of JCM. The PEM-GD method fully explores the structure features of JCM and the PEM-AO is proposed to decrease the computational complexity of the former via dimensionality reduction. The simulation results show that the proposed three methods perform better than directly using sample covariance matrix. Additionally, the proposed PEM-GD and PEM-AO outperform EVD method and the clutter and disturbance covariance estimator RCML. [ABSTRACT FROM AUTHOR]

This paper investigates the joint design of fronthaul compression and precoding for the downlink of cloud radio access networks (C-RANs). In a C-RAN, a central unit (CU) controls a cluster of radio units (RUs) through low-latency fronthaul links. Most previous works on the design of fronthaul compression and precoding assume constant channels and instantaneous channel state information (CSI) at the CU. This paper, in contrast, concentrates on a more practical scenario with block-ergodic channels and considers either instantaneous or stochastic CSI at the CU. Moreover, the analysis encompasses two types of CU–RU functional splits at the physical layer, which we refer to as compression-after-precoding (CAP) and compression-before-precoding (CBP). With the CAP approach, which is the standard C-RAN solution, all baseband processing is done at the CU. With the CBP scheme, channel encoding and precoding are instead performed at the RUs: The CU does not perform precoding but rather forwards separately the information messages of a subset of mobile stations (MSs) along with the compressed precoding matrices to each RU. Optimization algorithms over fronthaul compression and precoding for both CAP and CBP are proposed, which are based on a stochastic successive upper bound minimization (SSUM) approach. Numerical results yield insights into the optimal RU–CU functional split for C-RANs. As a general conclusion, the relative advantages of the two functional splits depend on the interplay between the enhanced interference management abilities of CAP, particularly for dense networks, and the lower fronthaul requirements of CBP in terms of precoding information overhead, particularly for large coherence periods and with stochastic, rather than instantaneous, CSI. [ABSTRACT FROM AUTHOR]

In this paper, we study user selection (US) strategies for a multiple-input–multiple-output (MIMO) cognitive radio (CR) downlink network, where the r-antenna underlay CR secondary users (SUs) coexist with a primary user (PU), and all terminals are equipped with multiple antennas. Two main scenarios are considered: 1) The t-antenna cognitive base station (CBS) has perfect or partial channel state information at the transmitter (CSIT) from the CBS to the PU receiver (RX), and 2) the CBS has absolutely no PU CSIT. For these scenarios, we propose and evaluate multiple SU selection schemes that are applicable to both best-effort PU interference mitigation and hard interference temperature (IT) constraints. The computational complexity of the proposed schemes can be significantly smaller than that of an exhaustive search with negligible performance degradation. For the selection of C SUs out of K, whereas an exhaustive search is on the order of \mathcal{O}(\binom{K}{C}C^4r^3). When t$ and r$ are of the same order, the computational complexity of the proposed scheme can be \binom{K}{C}C^4/K times smaller. Mathematical complexity analysis and numerical simulations are provided to show the advantage of our schemes. [ABSTRACT FROM PUBLISHER]

Channel estimation is crucial to beamforming techniques in directional millimetre wave (mmWave) communications, which is generally designed based on channel state information static. However, due to the Doppler effect caused by the mobility of users, such as unmanned aerial vehicles, high-speed trains and autonomous vehicles, the mmWave channel is changing rapidly. Spatial channel covariance, defined by long-term statistic information of channels, is a promising solution to reduce channel estimation frequency and can be used to design hybrid precoders. In this paper, we first proposed a highly mobile hybrid mmWave multi-user (MU) multiple input multiple output (MIMO) system based on transition probabilities which can represent moving action of the MU. Secondly, we investigate compressive sensing based spatial channel covariance estimation based on the proposed dynamic system. We then propose a dynamic covariance forward-backward pursuit (DCFBP) algorithm which introduces forward and backward mechanisms to reconstruct the Hermitian sparse covariance matrix. We further explore the constructed MU sensing matrix quality for conventional sparse Bayesian learning (SBL) framework. The updated sparse Bayesian learning (Updated-SBL) algorithm is developed to reduce the total squared coherence of a constructed sensing matrix with updated receive precoder. Numerical analysis demonstrates the proposed DCFBP method outperforms the benchmark methods. The total squared coherence of the proposed Updated-SBL algorithm is dramatically reduced. and the superiority of this algorithm is validated compared with other benchmark methods with comparable computation complexity. [ABSTRACT FROM AUTHOR]

In secure spatial modulation with a malicious full-duplex attacker, how to obtain the interference subspace or channel state information (CSI) is very important for Bob to reduce or even completely cancel the interference from Mallory. In this paper, different from existing work with perfect CSI, the covariance matrix of malicious interference (CMMI) from Mallory is estimated to construct the interference subspace. To improve the estimation accuracy, a CMMI rank detector relying on the Akaike information criterion (AIC) is derived first. To achieve a high-precision CMMI estimation, two methods, principal component analysis-eigenvalue decomposition (PCA-EVD) and joint diagonalization (JD) are proposed. The proposed PCA-EVD is a rank deduction method whereas the JD method is a joint optimization method with improved performance in the low jamming-to-noise ratio (JNR) region at the expense of increased complexity. Simulation results show that the proposed PCA-EVD performs much better than existing methods like sample estimated covariance matrix (SCM) and EVD in terms of normalized mean square error (NMSE) and secrecy rate (SR). Additionally, the proposed JD has achieved a better NMSE performance than PCA-EVD in the low JNR region (JNR $\leq$ 0 dB) while the proposed PCA-EVD performs better than JD in the high JNR region. [ABSTRACT FROM AUTHOR]

The use of the emerging hybrid analog-digital structure for future millimeter-wave communications has attracted much attention. Although this structure can reduce the power consumption considerably, the spatial covariance matrix (SCM), as the core of subspace-based direction of arrival (DOA) estimation algorithms, cannot be obtained directly. The beam sweeping algorithm (BSA) was proposed for SCM reconstruction from compressed measurements. However, the BSA is computationally intensive owing to the high-dimensional matrix-to-matrix multiplication and matrix inversion. To address this issue, a BSA algorithm with high accuracy and low computational-cost using the submatrix multiplication (BSASM) is proposed in this paper. By appropriately adjusting the weight, which consists of a switch and a phase shifter, connected to each antenna, the SCM can be accurately reconstructed with low-dimensional matrix-to-vector multiplication and 2-dimensional matrix inversion, thus reducing the computational-cost significantly. After SCM reconstruction, various DOA estimation algorithms can be exploited to obtain the DOA information. Simulation experiments are conducted to verify the performance of the proposed algorithm. The results indicate that using a substantially lower computational-cost, BSASM can reconstruct SCM more accurately than BSA. [ABSTRACT FROM AUTHOR]

This paper considers a novel algorithmic framework for the joint spectral sharing design of a colocated MIMO pulse radar and a MIMO communication system under signal direction mismatch, wherein both radar transceiver and communication transmitter designs are investigated, and the mutual information (MI) is maximized under a number of constraints to suppress the mutual interference of the coexistence system. The designed multi-variate formulation is non-convex, an iterative procedure is derived to compute a sequence of simpler sub-problems which admit a low complexity closed-form solution for each primal variable. The convergence of all devised algorithms is guaranteed. Finally, a thorough performance assessment is conducted to demonstrate the merits of the proposed solutions. [ABSTRACT FROM AUTHOR]

Spectrum sensing is a fundamental component of cognitive radio (CR). How to promptly sense the presence of primary users (PUs) is a key issue to a CR network. The time requirement is critical in that violating it will cause harmful interference to the PU, leading to a system-wide failure. The motivation of this paper is to provide an effective spectrum sensing method to detect PUs as soon as possible. In the language of streaming-based real-time data processing, short time means small data. In this paper, we propose a cumulative spectrum sensing method dealing with limited sized data. A novel method of covariance matrix estimation is utilized to approximate the true covariance matrix. The theoretical analysis is derived based on McDiarmid's concentration inequalities and random matrix theory to support the claims of detection performance. Comparisons between the proposed method and other traditional approaches, judged by the simulation using a captured digital TV (DTV) signal, show that this proposed method can operate either using smaller data or working under a lower signal-to-noise ratio (SNR) environment. [ABSTRACT FROM PUBLISHER]

It has been shown that the knowledge of both channel and data information at the base station prior to downlink transmission can help increase the received signal-to-noise ratio (SNR) of each user without the need to increase the transmitted power. Achievability is based on the idea of phase alignment (PA) precoding, where instead of nulling out the destructive interference, it judiciously rotates the phases of the transmitted symbols. In this way, they add up coherently at the intended user and yield higher received SNRs. In addition, it is well known that regularized channel inversion (RCI) precoding improves the performance of channel inversion (CI) in multiantenna downlink communications. In line with this and similar to the RCI precoding, in this paper, we propose the idea of regularized PA (RPA), which is shown to improve the performance of original PA precoding. To do this, we first rectify the original PA precoding, deriving a closed-form expression to evaluate the amount of transmit-power reduction achieved for the same average output SNR compared with CI precoding. We then use this new analysis to select the appropriate regularization factor for our proposed RPA scheme. It is shown by means of theoretical analysis and simulations that the proposed RPA precoding outperforms CI, RCI, and PA precoders from both symbol error rate (SER) and throughput perspectives and provides a more power-efficient alternative. This is particularly pronounced as the number of transmit antennas becomes larger, where up to a 50-times reduction in the transmit power is achieved by RPA (PA) compared with RCI (CI) precoding for a given performance. [ABSTRACT FROM AUTHOR]

In multiple-input–multiple-ouput (MIMO) interference channels (IFCs), interference alignment (IA) with zero-forcing (ZF) linear detection can be used to achieve the maximum degrees of freedom (DoFs). However, ZF linear detection usually degrades the performance due to the noise enhancement in practical systems. In this paper, we propose lattice reduction (LR)-aided successive interference cancelation (SIC) in conjunction with IA over lattice for three-user (and extendable to $K$-user) MIMO IFCs in which the interfering signals aligned over the lattices are detected and canceled rather than nulled out. To complete the proposed LR-aided SIC method, the detection ordering and the precoding matrix that minimize the mean square error are also determined. [ABSTRACT FROM PUBLISHER]

In this paper, we analyze the diversity performance of source–relay joint precoding in spatial multiplexing (SM) multiple-input–multiple-output (MIMO) amplify-and-forward (AF) relaying systems with channel coding. First, we evaluate the achievable diversity order of singular-value-decomposition-based precoding schemes as a function of a code rate. Then, we show that a significant diversity improvement can be obtained by utilizing signal space diversity. Our analysis provides helpful insights on the tradeoff between the code rate and the diversity order in SM MIMO AF relaying systems. Simulation results demonstrate that our analytical expressions match well with the numerical results. [ABSTRACT FROM PUBLISHER]

In cognitive radio (CR), the test statistics of most spectrum sensing algorithms are generated from the model-based features such as the signal energy and the eigenvalues from the sample covariance matrix (CM). Despite their low complexity, their detection performance depends very much on the accuracy of the presumed model. Also, these model-based statistics may not be able to exploit the full potential of the signal samples. To this end, the data-driven deep learning-based detectors have been proposed, with test statistics generated directly from signal samples in an automatic manner. However, existing deep learning-based detectors are all supervised learning-based and they usually require a massive amount of labeled training data to achieve decent detection performance. In practical CR scenarios, however, obtaining a large amount of labeled training data may be difficult. To address this issue, in this paper, we propose an unsupervised deep learning based spectrum sensing method named unsupervised deep spectrum sensing (UDSS). The UDSS algorithm requires no prior information such as the noise power or the signal's statistical CM. Moreover, the UDSS only requires a small amount of samples collected in absence of the primary user's (PU) signals ($H_0$ labeled data). Simulation results show that the proposed UDSS algorithm is able to approach the performance of the benchmark deep supervised learning-based spectrum sensing algorithm and outperforms the model-based benchmark algorithms under both Gaussian noise and Laplace noise. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal training design for multiple-input multiple-output (MIMO) amplify-and-forward (AF) two-way relay systems in the presence of interference. In order to address the interference issue while accurately estimating the channels, we minimize the total transmit power consumed by two source nodes and a relay subject to the constraint on mean square error (MSE) of the channel estimation, which is a nonconvex problem. To solve this challenging problem, we first derive the optimal structure of the training signals, and then, we convert the original problem into a tractable convex form, from which the optimal training scheme is designed efficiently. We further consider a practical special case, for which the optimal training scheme is designed with a lower complexity and the useful insights into the optimal training design are obtained. Also, to reduce the computational complexity of the optimal training design, we propose an asymptotically optimal training scheme in the high signal-to-interference-plus-noise ratio (SINR) regime with a low complexity. Simulation results demonstrate that the proposed schemes significantly outperform the existing schemes. [ABSTRACT FROM AUTHOR]

In the field of indoor pedestrian navigation (IPN), the orientation information of a pedestrian is often obtained by means of strap-down inertial navigation system (SINS). To deal with the problem of divergence in SINS based orientation estimates, additional orientation sensors, such as a camera, are needed to provide external orientation observations, resulting in increased cost and complexity of system. Although a low-cost magnetometer (or compass) can be used, it is significantly affected by geomagnetic disturbances indoors. Besides, the magnetometer can only give the heading observation which is insufficient to correct orientation errors in all three directions. In this paper, we propose a novel IPN method based on shoe-mounted micro-electro-mechanical systems inertial measurement unit and ultra-wideband. The biggest advantage of this method is able to obtain high-precision position and orientation estimates at low cost. In addition, in the proposed method, the data fusion is implemented by a quaternion Kalman filter which does not involve any complex linearization and hence the system complexity is reduced. Experimental results show that a decimeter level position accuracy is achieved and the orientation drifts can be limited to 0.066 radians in indoor environments. [ABSTRACT FROM AUTHOR]

This paper investigates the sensor selection problem in time difference of arrival (TDOA) tracking scenario to find the optimal sensor activation strategy for the upcoming time step. We propose a multiobjective optimization framework to minimize two conflicting objectives, i.e., tracking accuracy and quantity budget, which are represented by the trace of the conditional posterior Cramér Rao lower bound (CPCRLB) and the number of selected sensors, respectively. Due to the reduced measurement dimension and correlated noise caused by the common reference sensor (CRS), sensor selection algorithms for the general nonlinear model in existing literature cannot be applied to the TDOA tracking directly as they mostly assume that each sensor produces an independent measurement. Therefore, we introduce two Boolean vectors to indicate the CRS and ordinary sensors respectively. The sensor selection problem is then formulated as a multiobjective optimization problem (MOP), which can be further transformed as a single objective optimization problem (SOOP) using the linear weighted-sum method. We prove that the SOOP satisfies the rules of discrete monotonic optimization (DMO), and propose the polyblock outer approximation (POA)-based algorithms to seek for a globally optimal solution. For comparison, we introduce the conventional semidefinite program (SDP)-based algorithm to solve the SOOP with multi-step relaxation. Simulation results demonstrate that the proposed POA-based algorithms can considerably outperform the SDP-based ones in solving the optimization problem. [ABSTRACT FROM AUTHOR]

Location awareness is becoming essential for various wireless applications, and cooperation among nodes is highly beneficial for localization performance. However, the existing cooperative localization (CL) algorithms may suffer from major performance loss when applied to the mobile environments with fast changing network topologies. In this paper, we extend the joint spatial and temporal cooperation (JSTC) model to the range-based localization problem. Based on mobility constraints and the maximum likelihood estimation (MLE) criterion, we formulate the CL problems in both centralized and distributed cases. Different from the conventional distributed CL problems, the proposed one takes the transmission delay and the cooperation among multi-hop nodes into account, which is more practical. Moreover, the performance limits of the considered problems are investigated by the Cramér-Rao lower bound (CRLB). Finally, these two problems are solved by the Sequential Quadratic Programming (SQP) algorithm. Both simulation and experiment results show that the extended JSTC model can effectively improve the localization reliability and accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the achievable rate of the multi-cell multi-user generalized spatial modulation uplink is analyzed in the presence of realistic pilot contamination. A practical channel model associated with arbitrary transmit and receive correlation matrices is assumed for each user and base station (BS). Imperfect channel estimation is assumed with pilot contamination. The performance of a single-cell based minimum mean squared error (SMMSE) combiner is analyzed and compared to the ubiquitous zero forcing (ZF) combiner as well as matched filtering (MF). The transmit antenna (TA) index detection and the classic amplitude/phase modulation signal detection process are carried out separately for the sake of low complexity. Moreover, an algorithm based on order statistics is proposed for calculating the antenna detection probability conditioned on the actual TA. Finally, an approximation of the achievable rate is derived by exploiting the characteristics of massive multiple-input multiple-outputs (MIMO). Simulation results show that SMMSE has the best performance, followed by ZF, and then MF. Furthermore, the relationships between the system's achievable rate and three systems’ parameters, namely the number of BS antennas, the signal-to-noise-ratio (SNR), and the interference factor of pilot contamination, are presented. The performance of different number of activated antennas is also compared. [ABSTRACT FROM AUTHOR]

Multi-autonomous underwater vehicles (AUVs) cooperative localization has become a research hotspot in the marine navigation field. In this paper, a filtering algorithm for slave AUV with compass failure is proposed based on ‘two-master-one slave’ cooperative localization model. In this algorithm, the course angle is unknown, and non-Gaussian measurement noise caused by measurement outliers is considered. The cooperative localization model is reconstructed, and a filtering algorithm is derived whose performance will not be affected by the unknown course angle. Moreover, the maximum information potential (MIP) criterion is introduced into a recursive model to deal with the non-Gaussian measurement noise in a complicated underwater environment. Simultaneously, the kernel bandwidth is adaptively adjusted by the innovation matrix and the measurement error covariance matrix. The proposed algorithm can accurately estimate the variation of the unknown course angle without expanding the dimension of state under the condition of non-Gaussian measurement noise. Finally, the proposed algorithm is verified through field experiments. The results show that the proposed algorithm has higher accuracy and robustness than other existing algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the Bayesian detection problem for a moving target that is embedded in a homogeneous Gaussian clutter with an unknown but stochastic covariance matrix for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. First, we propose a Bayesian detector based on structured generalized likelihood ratio test (SGLRT), namely BSGLRT, criteria that requires no training data. Then, we present a detector based on the Bayesian unstructured generalized likelihood ratio test (BUGLRT) to reduce the three dimensions (range-angle-Doppler) search into one-dimension Doppler searches for low-complexity implementation in practical applications. Moreover, the robustness of the BSGLRT and BUGLRT detectors is also analyzed. Numerical results reveal that the proposed Bayesian detectors and estimators, i.e. BSGLRT and BUGLRT, outperform their non-Bayesian counterparts in Gaussian clutter with a small number of snapshots and/or low signal-to-clutter rate (SCR) for FDA-MIMO radar. [ABSTRACT FROM AUTHOR]

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms. [ABSTRACT FROM AUTHOR]

In several adaptive algorithms, the data covariance matrix must be frequently estimated, particularly in modern wireless systems where, due to high mobility, signal statistics change in time. When the underlying distribution is degenerate, likelihood-based estimators of the covariance matrix and/or scatter matrix may not exist for non-Gaussian signals. A well-known class of non-Gaussian distributions is the family of complex elliptically symmetric (CES) distributions, which includes several well-known distributions as special cases. In this paper, we give some results on the degeneracy of CES distributions and derive the maximum likelihood (ML) estimator and $M$-estimators of the scatter matrix for degenerate CES vectors. Estimation with insufficient data is addressed by a regularization approach. We also formulate the Cramér–Rao lower bound (CRLB) on the elements of the scatter matrix for degenerate CES vectors. Our results coincide with those of the nondegenerate CES vectors whenever the scatter matrix is full rank. Numerical results are presented to justify the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a promising technique for interference management in wireless networks. On the other hand, jamming is a potential threat to wireless communications, which can degrade the performance of IA-based networks severely. Although some excellent work has been done on IA and antijamming, these two important aspects have traditionally been addressed separately in most previous work. In this paper, we study the antijamming issues and propose two antijamming schemes for IA-based networks. In the first scheme, the jamming signal and interferences are aligned into the same subspace at each receiver, and thus, can be perfectly eliminated together. When the requirement of perfect alignment can be relaxed, the second scheme is proposed to further improve the antijamming performance by maximizing the received signal-to-interference-plus-noise ratio (SINR). We derive the proper and feasibility conditions of the proposed two schemes. Simulation results are presented to show the effectiveness of the proposed schemes in mitigating jamming signals in IA-based wireless networks. [ABSTRACT FROM PUBLISHER]

Channel state information at the transmitter (CSIT) plays a key role in achieving potential gain of massive multiple-input–multiple-output (MIMO) systems. In frequency-division duplex (FDD) systems, CSIT can be obtained through feedback from the receiver. Conventional limited feedback schemes, which have been designed for small-scale MIMO systems, suffer from a prohibitive amount of feedback requirement and encoding complexity when the number of transmit antennas becomes massive. In this paper, a projection-based differential feedback (PBDF) protocol is proposed for FDD massive MIMO systems. In the PBDF framework, the difference between original and predicted vectors is projected, and quantization is performed in a smaller dimensional subspace. With an appropriate projection exploiting spatial and temporal correlation of massive MIMO channels, the feedback amount and encoding complexity can be significantly reduced. The simulation results show that the proposed scheme achieves a large portion of potential throughput gain of massive MIMO systems with a small amount of channel feedback. [ABSTRACT FROM PUBLISHER]

Large-scale multiple-input–multiple-output (MIMO) networks, together with relay technology, have received considerable attention due to their remarkable capacity potential. Nevertheless, the majority of recent research studies on the asymptotic capacity of large-scale MIMO amplify-and-forward (AF) relay networks are limited to a fundamental assumption of small-scale fading, where the effect of large-scale fading, including shadow fading and path loss, is ignored. Motivated by this observation, in this paper, we present an extended model for the asymptotic capacity analysis in a composite fading environment, where Rayleigh fading, shadow fading, and path loss effect are incorporated altogether. Moreover, a realistic relay distribution is considered for real-world application of our capacity analysis. The proposed model consists of one source and one destination that are both equipped with $M$ antennas, which communicate through $K$ relays, each having $N$ antennas. We first employ random matrix theory to derive the empirical cumulative distribution of the channel covariance matrix and analyze the asymptotic capacity per source–destination antenna pair as both $M$ and $NK$ tend to infinity, but their ratio remains as a constant. We then consider the asymptotic capacity under two asymptotic cases for a finite and an infinite number of relays, respectively. It is shown through both theoretical analysis and Monte Carlo simulations that increasing the shadowing standard deviation can improve asymptotic capacity, whereas increasing the path loss exponent would lead to a capacity reduction. Our simulation results also reveal the effects of the relay distribution, as well as the source and relay transmission powers, on the asymptotic capacity. [ABSTRACT FROM PUBLISHER]

This paper investigates multiuser multiple-input–multiple-output (MIMO) wireless powered communication networks where a multiantenna hybrid access point (H-AP) transfers wireless energy to multiantenna users in a downlink phase, and the users utilize the harvested energy for their information transmission to the H-AP in an uplink phase. By employing space-division multiple-access techniques, we propose an optimal algorithm that jointly computes the downlink energy precoding matrices, the uplink information precoding matrices, and time allocation between the downlink and the uplink phases for maximizing the uplink sum-rate performance. To this end, we first obtain the optimal energy and information transmit covariance matrices with given time allocation. Then, the optimal time allocation can be efficiently identified by a simple line search method. Simulation results verify that the proposed joint optimal algorithm significantly improves the average sum-rate performance, compared with a conventional scheme that determines time allocation and precoding matrices separately. [ABSTRACT FROM AUTHOR]

We recently proposed a direction-of-arrival (DOA) estimation method for the coexistence of uncorrelated and coherent signals, which is related to the uniform linear antenna arrays with mutual coupling (MC) in the presence of unknown nonuniform noise. This technique, however, relies on the assumption of mixed signals and is not suitable for scenarios with purely coherent signals. This paper extends the mixed signal scenario to the purely coherent signals one on one hand, and on the other hand, allows us to directly adopt the ESPPRIT algorithm to perform DOA estimation in MC environments. To be specific, with the help of the least squares (LS) criterion, the nonuniform noise is first eliminated, and the traditional middle subarray scheme can then be employed to be immune to the influence of MC. Unlike the former operation, following a different line of attack the latter can make full use of the whole array and provide a larger array antenna aperture. Furthermore, we derive a new strategy to directly determine the DOAs without taking MC into account and performing spectral search. This brings substantial benefits to the DOA estimation accuracy and computational cost. The superiority of the proposed approaches is verified by numerical examples. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of adaptive moving target detection, embedded in homogeneous Gaussian noise with unknown covariance matrix, for frequency diverse array multiple-input multiple-output (FDA-MIMO) radar operating in interference-dominant environment. Unlike traditional adaptive moving target detectors that need training data to estimate the jamming covariance matrix (JCM), we present the Rao and Wald test based adaptive detector, which requires no training data. Furthermore, we proposed a two-stage approach to obtain maximum likelihood estimate (MLE) of the joint range, angle and Doppler, respectively. The corresponding signal-to-jamming-plus-noise ratio (SJNR) is derived to evaluate the FDA-MIMO radar performance. All proposed methods are validated by numerical results, which show that the proposed detector outperforms the existing generalized likelihood ratio test (GLRT). [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to the statistical characterization of non-central complex Gaussian quadratic forms (CGQFs). Its key strategy is the generation of an auxiliary random variable that replaces the original CGQF and converges in distribution to it. This technique is valid for both definite and indefinite CGQFs and yields simple expressions of the probability density function (PDF) and the cumulative distribution function (CDF) that only involve elementary functions. This overcomes a major limitation of previous approaches, where the complexity of the resulting PDF and CDF does not allow for further analytical derivations. Additionally, the mean square error between the original CGQF and the auxiliary one is provided in a simple closed-form formulation. These new results are then leveraged to analyze the outage probability and the average bit error rate of maximal ratio combining systems over correlated Rician channels. [ABSTRACT FROM AUTHOR]

This paper describes an adaptive fault-tolerant triple-integration vehicle navigation system. The proposed triple-integration system is based on a tightly coupled design, which fuses measurements from the Global Navigation Satellite System (GNSS), an Inertial Navigation System (INS), and optical velocity sensors (OVS). The difference between GNSS-measured and INS-derived pseudorange and pseudorange rate, and the difference between INS-derived and OVS-measured velocity are taken as the measurement inputs to the system filter, which uses them to obtain accurate and continuous navigation solutions. However, to ensure the optimality and to improve the robustness of the integration system in GNSS-challenging environments, a fault-tolerant fault detection and processing (FDP) algorithm is then applied to detect faults and further process the detected faults in fault-varying methods. The detected faults can be classified as Outliers and Biases according to the designed fault factor; Outliers are excluded and Biases are still utilized by adaptively adjusting the corresponding measurement noise covariance matrix. To evaluate the performance of the proposed triple-integration system, a road test was conducted under different scenarios, including GNSS open-sky, GNSS partly blocked, and GNSS-difficult conditions. The results indicate that the proposed integration system can generate more accurate position solutions than a loosely coupled system. To verify the fault-tolerant ability of the proposed FDP algorithm, typical types of GNSS faults are simulated. The results confirm that the proposed FDP algorithm can improve the system performance in terms of its fault-tolerant ability and accuracy as well. [ABSTRACT FROM AUTHOR]

This paper considers the direction of arrival (DOA) estimation of coherent and uncorrelated targets by exploiting a flexible multiple input multiple output radar. First, the proposed radar is generalized with two coprime expansion factors for enlarging the inter-element spacing of transmit and receive arrays, referred to as sparse arrays with flexible inter-element spacing (SA-FIS), which shows that the conventional nested or coprime ones are the special cases. The range of consecutive lags and the number of unique lags in the sum-difference coarray are derived in closed form. It is verified that SA-FIS can obtain the maximum unique lags and also suppress the mutual coupling effects. We then extend the FIS viewpoint to sparse nonuniform linear arrays, where the two-level nested and coprime arrays are employed to achieve a significant increase in degrees of freedom (DOFs). Furthermore, to fully utilize these unique lags, we propose a reduced-complexity two-step sparse representation algorithm. By modifying and removing the off-diagonal elements of the estimated target covariance matrix, the proposed method can just identify the diagonal ones, thereby leading to further improved performance with much lower computational complexity. Finally, the Cramér-Rao bound on DOAs and correlated coefficients for SA-FIS is derived. Numerical simulations demonstrate the superiority of the proposed method with SA-FIS in terms of DOFs, computations, estimation accuracy, and resolution capability compared with previous ones. [ABSTRACT FROM AUTHOR]

This paper uses an inexpensive single-beam laser sensor mounted on a rotationally controlled platform to simultaneously search for and track multiple vehicles that are behind a bicycle. Vehicles in the bicycle's lane and in the adjacent left lane are both considered. The tasks involved are searching both lanes to detect the presence of vehicles, tracking a vehicle's trajectory once it has been detected, and switching between searching and tracking as needed. A rigorous search algorithm that minimizes the number of sensor rotational angles needed to search the entire region of interest is developed. An error covariance matrix approach is utilized to switch between tracking vehicles and searching the region of interest. Detailed simulation results are presented to show how the developed system handles the absence and presence of vehicles in the two lanes and handles different types of lane change maneuvers while tracking multiple vehicles. Since the developed system uses an inexpensive lightweight sensor that is suitable for on-bicycle implementation, it can be used to detect the danger of a collision and provide a corresponding horn-like audio alert to the motorist. [ABSTRACT FROM AUTHOR]

Considering that wireless unidirectional full-duplex (FD) transmission is one of the FD transmissions over 5G mobile wireless networks, this paper proposes a joint receive and transmit relay beamformer design for systems consisting of a source, a FD relay, an intended destination, and an eavesdropper. The source and destination are single-antenna nodes, whereas the FD relay is a multi-antenna node. Assuming that full channel state information (CSI) is available and the eavesdropper processes two blocks of received data at a time, the joint optimization problem is reformulated as a semidefinite relaxation (SDR) problem (in terms of relay transmit beamformer), which is tight in the sense that the rank-one beamformer can always be recovered from the SDR solution. In order to significantly minimize the computational cost of the SDR implementation, a novel semi-analytical approach based on dual optimization is proposed to obtain the optimum transmit beamformer. When the number of data blocks is arbitrary, we propose a successive convex approximation approach for solving the original non-convex optimization problem and show that a stationary solution can be efficiently obtained. We then propose a distributionally robust beamformer design for the case of an arbitrary number of blocks, assuming that the means and covariance matrices of the eavesdropper's channels are known. Computer simulations show that the proposed semi-analytical solution matches with that obtained from numerical optimization of the SDR problem, the FD scheme significantly outperforms its half-duplex counterpart, and the proposed robust design provides robustness against CSI uncertainty. [ABSTRACT FROM AUTHOR]

Extended object tracking has become an integral part of many autonomous systems during the last two decades. For the first time, this paper presents a generic spatio-temporal Gaussian process (STGP) for tracking an irregular and non-rigid extended object. The complex shape is represented by key points and their parameters are estimated both in space and time. This is achieved by a factorization of the power spectral density function of the STGP covariance function. A new form of the temporal covariance kernel is derived with the theoretical expression of the filter likelihood function. Solutions to both the filtering and the smoothing problems are presented. A thorough evaluation of the performance in a simulated environment shows that the proposed STGP approach outperforms the state-of-the-art GP extended Kalman filter approach [N. Wahlström and E. Özkan, “Extended target tracking using Gaussian processes, IEEE Transactions on Signal Processing,” vol. 63, no. 16, pp. 4165–4178, Aug. 2015] with up to $\text{90}\%$ improvement in the accuracy in position, $\text{95}\%$ in velocity and $\text{7}\%$ in the shape, while tracking a simulated asymmetric non-rigid object. The tracking performance improvement for a non-rigid irregular real object is up to $\text{43}\%$ in position, $\text{68}\%$ in velocity, $\text{10}\%$ in the recall, and $\text{115}\%$ in the precision measures. [ABSTRACT FROM AUTHOR]

The temporal correlation of target can be exploited to improve the radar estimation performance. This paper studies the estimation of target scattering coefficients in an adaptive radar system, and a novel estimation method based on Kalman filter (KF) with waveform optimization is proposed for the temporally correlated target in the scenario with both noise and clutter. Different from the existing indirect methods, a direct optimization method is proposed to design the transmitted waveform and minimize the mean square error of the KF estimation. Additionally, the waveform is optimized subject to the practical constraints including the transmitted energy, the peak-to-average power ratio, and the target detection performance. With clutter and noise, the waveform optimization problem is non-convex. Therefore, a novel two-step method is proposed and converts the original non-convex problem into several semidefinite programming problems, which are convex and can solve efficiently. Simulation results demonstrate that the proposed KF-based method with waveform optimization can outperform state-of-art methods and significantly improve the estimation performance. [ABSTRACT FROM AUTHOR]

Cognitive dynamic system (CDS) is a structured engineering model and research tool inspired by certain features of the human brain. As a special function of CDS, cognitive risk control (CRC) actualizes the concept of predictive adaptation to bring risk under control when encountered with unexpected uncertainty. In this paper, the first experimental demonstration of CRC is presented in the practical application of vehicular radar systems, and an algorithm for transmit-waveform selection in cognitive vehicular radar (CVR) based on CRC is proposed. During each perception–action cycle, the perceptor of CVR processes new environmental inputs and provides the processed information to the executive through feedback channel for the selection of cognitive action. With the mechanism of task-switch control being functional all the time, the CVR will switch to a more capable operation mode in the face of unexpected disturbances or adverse events. In such cases, a new subsystem of executive is brought into play, in which the risk-sensitive cognitive action is finally selected and applied to the environment. Simulation results have shown the robustness and effectiveness of the proposed CVR system, which can make the next-generation vehicular radars more intelligent and play an important role in future self-driving cars. [ABSTRACT FROM AUTHOR]