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As a type of the spectrally efficient modulation, the m-ary phase position shift keying (MPPSK) has been considered to meet the increasing spectrum requirement in the future wireless system. To limit the signal bandwidth and cancel the out-band interference the band-pass filters are used, which introduce the waveform distortion and inter-symbol interference (ISI). Therefore, a single hidden-layer neural network (NN)-based receiver is proposed to jointly equalize and demodulate the received signal. The impulse response of the system is static and the network parameters can be obtained after off-line training. The number of the hidden nodes is also determined through simulations. Simulation results show that the NN-based receiver works well in the communication system with different allocated bandwidths. By observing the modified confusion matrix, the false symbol decision is relevant to modulation index, waveform distortions and the ISI.

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness. [ABSTRACT FROM AUTHOR]

Radar system design for the detection of targets with unknown actual range in spectrally crowded environments is a challenge due to increasing demands for spectrum and the possibly experienced echo eclipsing. Assuming unknown target echo delay, this article deals with robust joint design of the radar code and receive filter bank guaranteeing spectral compatibility with the surrounding electromagnetic radiators. The average signal-to-interference-plus-noise ratio (SINR) at the output of the filter bank is considered as the performance measure to optimize under both similarity and spectral compatibility constraints on the radar code. Through an appropriate reformulation of the resulting nonconvex optimization problem, we devise an alternate optimization algorithm, which sequentially improves the average SINR and converges to a point fulfilling the Karush–Kuhn–Tucker (KKT) conditions of the design problem. Each iteration of the algorithm involves both a convex and a hidden convex optimization problem, which can be solved via the rank-1 decomposition procedure. Finally, the performance is assessed by studying the tradeoff among the SINR, spectral shape, and autocorrelation. Numerical results reveal that the new method has better flexibility and robustness compared to several counterparts, which are presented in open literature. [ABSTRACT FROM AUTHOR]

We develop a novel method for high-resolution inverse synthetic aperture radar (ISAR) imaging by exploring the block-sparse structure inherent in the ISAR image. First, the Laplacian scale mixture (LSM) prior is utilized to encode the sparsity of the ISAR image. Then, a 2-D pattern-coupled scale prior for sparse coefficients that encourage dependencies among neighboring coefficients is utilized to capture the block-sparse structure of the target scene. Finally, the Laplace approximation-based variational Bayesian inference (VBI) is employed to infer the posterior of the ISAR image along with hyperparameters. Numerical results based on simulated and measured data are provided to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and on-line centralized training and distributed execution, and a mathematical analysis is presented for the top-level design of the near-static problem. Policy-based REINFORCE, value-based deep Q-learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed for this sum-rate problem. Simulation results show that the data-driven approaches outperform the state-of-art model-based methods on sum-rate performance. Furthermore, the DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness. [ABSTRACT FROM AUTHOR]

This correspondence deals with the joint cognitive design of transmit coded sequences and instrumental variables (IV) receive filter to enhance the performance of a dual-function radar-communication (DFRC) system in the presence of clutter disturbance. The IV receiver can reject clutter more efficiently than the match filter. The signal-to-clutter-and-noise ratio (SCNR) of the IV filter output is viewed as the performance index of the complexity system. We focus on phase only sequences, sharing both a continuous and a discrete phase code and develop optimization algorithms to achieve reasonable pairs of transmit coded sequences and IV receiver that fine approximate the behavior of the optimum SCNR. All iterations involve the solution of NP-hard quadratic fractional problems. The relaxation plus randomization technique is used to find an approximate solution. The complexity, corresponding to the operation of the proposed algorithms, depends on the number of acceptable iterations along with on and the complexity involved in all iterations. Simulation results are offered to evaluate the performance generated by the proposed scheme. [ABSTRACT FROM AUTHOR]

A novel scheme to optimize the adaptive transmit waveform of chaotic multiple-input multiple-output (MIMO) radar is developed. The main objective of this work is to achieve high ability in target discrimination by using a Dirichlet process mixture model (DPMM)-based clustering method based on nonparametric Bayesian theory and to improve the capability of target detection by minimizing the mean square error of radar channel response via Kalman filtering (KF) technique. The two stages are the discrimination of multiple range-extended targets and the optimization of the adaptive chaos-based waveform for transmission. The adaptive chaotic MIMO waveform optimization scheme overcomes the problem of target discrimination and detection in an intelligent transportation system, where there is a need for extracting the feature of target information achieved from vehicle-mounted sensor. As the number of iterations increases, simulation experiments demonstrate better target discrimination capability provided by the proposed DPMM-KF technique as compared with the traditional waveform design method. In addition, the proposed DPMM-KF technique leads to improved target detection probability and receiver operating characteristics in the interference environment. [ABSTRACT FROM AUTHOR]

This paper presents a novel timing synchronization scheme for orthogonal frequency-division multiplexing communication systems by leveraging the phase shift caused by the timing error and the power difference between subcarriers. The proposed scheme is applied in the physical layer of vehicular ad hoc networks using the IEEE 802.11p standard. The proposed work joints three different estimators to present a robust synchronizer. The results demonstrate that the proposed estimators greatly improve the performance of the communication system and outperform other approaches in terms of the mean square error and the bit error rate. The estimators can either work as totally blind or data-aided using the preamble field of the IEEE 802.11p. [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]

Automatic modulation classification (AMC), which plays critical roles in both civilian and military applications, is investigated in this paper through a deep learning approach. Conventional AMCs can be categorized into maximum likelihood (ML) based (ML-AMC) and feature-based AMC. However, the practical deployment of ML-AMCs is difficult due to its high computational complexity, and the manually extracted features require expert knowledge. Therefore, an end-to-end convolution neural network (CNN) based AMC (CNN-AMC) is proposed, which automatically extracts features from the long symbol-rate observation sequence along with the estimated signal-to-noise ratio (SNR). With CNN-AMC, a unit classifier is adopted to accommodate the varying input dimensions. The direct training of CNN-AMC is challenging with the complicated model and complex tasks, so a novel two-step training is proposed, and the transfer learning is also introduced to improve the efficiency of retraining. Different digital modulation schemes have been considered in distinct scenarios, and the simulation results show that the CNN-AMC can outperform the feature-based method, and obtain a closer approximation to the optimal ML-AMC. Besides, CNN-AMCs have the certain robustness to estimation error on carrier phase offset and SNR. With parallel computation, the deep-learning-based approach is about $ 40$ to $ 1700$ times faster than the ML-AMC regarding inference speed. [ABSTRACT FROM AUTHOR]

The system architecture for cognitive radar-communication (CRC) transceiver is proposed. A cognitive waveforms design approach, which is suitable for simultaneously performing both data communication and target detection, is presented. This approach aims at estimating target scattering coefficient (TSC) from the radar scene and facilitating high data rate communications. In order to minimize the mean square error (MSE) of the TSC, a convex cost function is established. The peak to average power ratio- (PAPR-) constrained optimal solution is achieved by applying the Kalman filtering-based strategy to design the set of ultra-wideband (UWB) transmission pulses and embed into them the information data with the M-ary position phase shift keying modulation technique. In addition to theoretical considerations, the simulation results show an improvement in target scattering coefficient (TSC) estimation and target detection probability as the number of iterations increases, while still transmitting data rates in the range of several Mbps with low bit error rates between CRC transceivers. [ABSTRACT FROM AUTHOR]

Prior asymptotic performance analyses are based on the series expansion of the moment-generating function (MGF) or the probability density function (PDF) of channel coefficients. However, these techniques fail for lognormal fading channels because the Taylor series of the PDF of a lognormal random variable is zero at the origin and the MGF does not have an explicit form. Although lognormal fading model has been widely applied in wireless communications and free-space optical communications, few analytical tools are available to provide elegant performance expressions for correlated lognormal channels. In this paper, we propose a novel framework to analyze the asymptotic outage probabilities of selection combining (SC), equal-gain combining (EGC), and maximum-ratio combining (MRC) over equally correlated lognormal fading channels. Based on these closed-form results, we show: 1) the outage probability of EGC or MRC becomes an infinitely small quantity compared to that of SC at high signal-to-noise ratio (SNR); 2) channel correlation can cause an infinite performance loss at high SNR; and 3) negatively correlated lognormal channels can outperform the independent lognormal channels. The analyses reveal insights into the long-standing problem of asymptotic performance analyses over correlated lognormal channels, and circumvent the time-consuming Monte Carlo simulation and numerical integration. [ABSTRACT FROM PUBLISHER]

Vehicular Ad Hoc networks (VANETs) have been employed in supporting several applications related to safety and comfort. VANET applications have strict security requirements, as they have direct impact on people comfort and safety. Although many ordinary networks security systems can be applied to provide security services in VANET, the key establishment between legitimate vehicles is the common challenge among all of these systems. This paper proposes a secret key establishment technique for vehicular communications exploiting the special properties and randomness inherent of the wireless channel. Our comprehensive simulations show that the proposed key extraction technique suits VANETs rather than other communication systems, owing to the multi environments operation of VANET which causes more randomness due to fading, noise multipath and velocity variation. The proposed approach can be tuned to extract low or high rate secret key with high entropy rate and less information exchange between legitimate vehicles. The extracted secret key can be employed to support providing security services in VANET. [ABSTRACT FROM AUTHOR]

Extended binary phase shift keying/M-ary position phase shift keying (EBPSK/MPPSK)-MODEM provides radar and communication functions on a single hardware platform with a single waveform. However, its range estimation accuracy is worse than continuous-wave (CW) radar because of the imbalance of power in two carrier frequencies. In this article, the power allocation method for dual-frequency EBPSK/MPPSK modulated systems is presented. The power of two signal transmitters is adequately allocated to ensure that the power in two carrier frequencies is equal. The power allocation ratios for two types of modulation systems are obtained. Moreover, considerations regarding the range of operation of the dual-frequency system are analysed. In addition to theoretical considerations, computer simulations are provided to illustrate the performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of moving target detection, and a novel radar system with multiple moving platforms is proposed. Each moving platform is equipped with multiple colocated antennas and serves as a transmitter or a receiver. Thus, this system possesses the advantages of both distributed and colocated multiple-input multiple-output radars. To exploit the clutter sparsity in the surveillance area, a novel compressed sensing (CS)-based model is proposed. Since the clutter cannot exactly reside on discretized grids often employed by most CS approaches, a novel two-step algorithm which extends the orthogonal matching pursuit is proposed to reconstruct the off-grid clutter. Then, a fusion center combines all received signals by using a generalized likelihood ratio test to detect the moving target. To further improve the detection performance, a novel online waveform optimization algorithm is developed to maximize the signal-to-clutter-and-noise ratios of each transmitter platform. Extensive simulation results are provided to demonstrate the effectiveness of the proposed radar system and algorithms. [ABSTRACT FROM PUBLISHER]

In this paper, the ranges and velocities of multiple extended targets are estimated by exploiting the target sparsity in the cognitive radar system. Different from the point targets in the traditional compressed sensing (CS) radar, the parameters of extended targets are expressed and estimated by using a novel CS-based model. Since the echo signals from extended targets are the convolutions between the transmitted waveform and target impulse responses (TIRs), the dictionary matrices in the proposed cognitive radar for all extended targets must be first established in the CS-based reconstruction algorithm. Then, the target parameters are estimated by reconstructing the nonzero entries of a sparse vector. To further improve the performance of CS reconst-ruction, a novel two-step method is proposed to minimize the mutual coherence of the dictionary matrix by optimizing the transmitted waveform. Simulation results demonstrate that the estimation performance of the extended targets is significantly improved by optimizing the transmitted waveform. [ABSTRACT FROM PUBLISHER]