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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.

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.

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.

The model-based power allocation algorithm has been investigated for decades, but it requires the mathematical models to be analytically tractable and it usually has high computational complexity. Recently, the data-driven model-free machine learning enabled approaches are being rapidly developed to obtain near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as of great potential for future intelligent networks. In this paper, the DRL approaches are considered for power control in multi-user wireless communication cellular networks. Considering the cross-cell cooperation, the off-line/on-line centralized training and the distributed execution, we present a mathematical analysis for the DRL-based top-level design. The concrete DRL design is further developed based on this foundation, and policy-based REINFORCE, value-based deep Q learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed. Simulation results show that the proposed data-driven approaches outperform the state-of-art model-based methods on sum-rate performance, with good generalization power and faster processing speed. Furthermore, the proposed DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness, and can be incorporated into existing resource allocation schemes due to its generality.

We present a signaling scheme for information embedding into the illumination of radar using frequency-hopping pulses. A frequency-hopping-based joint radar-communication system enables implementing a primary radar operation and a secondary communication function simultaneously. Then, we consider the problems of radar codes optimization under a peak-to-average-power ratio and an energy constraint. These radar codes design problems can be converted into non-convex quadratic programs with a finite or an infinite number of quadratic constraints. All problems are proved to be NP-hard optimization problems. Therefore, we develop optimization approaches, resorting to semi-definite programming relaxation technique along with to the idea of trigonometric polynomials, offering expected approximate solutions with a polynomial time calculation burden. We assess the capability of the proposed schemes, considering both the detection probability and the robustness in correspondence of Doppler shifts offered by the Neyman–Pearson detector. Simulation results show an improvement in detection performance as the average signal-to-noise ratio value increases, while still maintaining low symbol error rates between the proposed system nodes.

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.

This research work considers waveform design for an adaptive radar system. The aim is to achieve enhanced feature extraction performance for multiple extended targets. There are two scenarios to consider: multiple extended targets separated in range and multiple extended targets close in range. We propose a waveform optimization scheme based on Kalman filtering by minimizing the mean square error of separated target scattering coefficient estimation and a waveform optimization approach by minimizing the mean square error of closed power spectrum density estimation. A convex cost function is established, and the optimal solution can be obtained using the existing convex programming algorithm. With subsequent iterations of the algorithm, the simulation results demonstrate an improvement in the estimation of target parameters from the dynamic scene, such as target scattering coefficient and power spectrum density, while maintaining relatively lower computational complexity.

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.

Connected devices generate large amount of data for IoT applicatons. Assisted by edge computing, caching IoT data at the edge nodes is considered as a promising technique for its advantage in reducing network traffic and service delay of cloud platform. However, the IoT data is characterized by transient lifetime and cache capacity that is limited by the edge nodes. As a consequence, caching policy should consider both data transiency and storage capacity of edge nodes. Inspired by the success of deep reinforcement learning (DRL) in deal with Markov Decision Process (MDP) problem in unknown environment, A DRL-based algorithm for edge caching problem is proposed in this paper. The proposed Advantage Actor Critic (A2C)-based algorithm is aimed at maximizing the long-term energy saving without knowledge of the IoT data popularity profiles. Simulation results demonstrate that the proposed DRL-based algorithm can achieve higher energy saving and cache hit ratio compared with the baseline algorithms.

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.

Extended-binary phase shift keying (EBSPK)-MODEM radar-communication transceiver is considered as high-range resolution (HRR) radar in the ranging mode. In this paper, a new range-extended target detection method for EBPSK transceiver is investigated. First, the signal to noise ratio (SNR) improved amount of the proposed system is deduced. Secondly, Mean square error (MSE) of target scattering parameter is estimated. The optimal EBPSK waveform is designed to make sure that the estimated performance of the target frequency domain scattering parameters is optimal. Target detection performances of the optimized EBPSK signal in the additive white Gaussian noise (AWGN) channel and the dispersive channel are investigated, respectively. Finally, through optimizing carrier frequency and modulation parameters, the simulation experiment results showed that detection performance of the optimized EBSPK signal is better than the unoptimized EBSPK signal and traditional radar signals.

In this paper, we propose an anti-noise modem based on the improved M-ary position phase shift keying (MPPSK) for visible light communication (VLC) systems. The line spectral interferences in the original MPPSK are theoretically analyzed and approximately mitigated by adopting a set of improved waveform samples. Then, within one symbol duration, the peak envelopes of the received signals are captured at each of synchronization bit with a quantity of sampling offsets to improve the proportion of the reliable data tones. In addition, a novel demodulation scheme using an amplitude-position detector based on soft decision is proposed to recover the MPPSK symbols, which can minimize the contamination influence of channel noise on symbol decision. With this scheme, the false alarm error and wrong slot error caused by the multipath fading in VLC channel can be effectively corrected and the bit error rate (BER) performance of the system is significantly improved. Simulation results demonstrate that the proposed scheme reduce the required signal-to-noise ratio (SNR) at least by 2 dB for BER = 10−4, which show the feasibility and validity of this anti-noise modem.

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.

Exact performance analyses over correlated $\alpha$ - $\mu$ fading channels result in intractable multifold integrals or infinite series. In this paper, upper and lower bounds are derived for outage probabilities and error rates of maximum-ratio combining (MRC), selection combining (SC), and equal-gain combining (EGC) over the $\alpha$ - $\mu$ fading channels with arbitrary correlation. The derived bounds are in closed form and asymptotically tight. Our applied correlation structure uniquely determines the joint probability density function of the $\alpha$ - $\mu$ random variables, thus it is a generalization of the other correlation models based on branches’ hyperpower or power correlation. Based on these closed-form bounds, one can easily estimate the performance of MRC, SC, and EGC in large signal-to-noise ratio region, where performing Monte Carlo simulation is time consuming. These new bounds also reveal a fact that SC can in fact outperform EGC under some certain channel conditions. More importantly, these bounds reveal which factors determine the asymptotic performance of diversity reception systems, and provide criteria for system design.

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.

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.

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.

Since a better localisation performance is achieved by exploiting the spatial diversity in the distributed multiple-input and multiple-output (MIMO) radar systems, the authors consider the problem of further improving this performance by optimising the antenna placement in this study. In the localisation processes, a novel compressed sensing-based method is proposed to exploit the target sparsity, where an over-complete dictionary matrix containing all the possible echo waveforms from the discretised area is first established. In addition, to further improve the localisation performance, a novel iterative method is proposed to minimise the mutual coherence of the dictionary matrix by optimising the antenna placement of both transmitters and receivers. Moreover, the theoretical distribution of the mutual coherence is derived, and an asymptotic performance with increasing the number of antennas is also provided. Simulation results demonstrate the localisation performance improvement for multiple targets by optimising the antenna placement in the distributed MIMO radar.

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.

In vehicular ad hoc network (VANET), the accuracy of the channel estimation plays a key role toward obtaining considerable improvement to the physical layer performance. In this study, least square (LS) and minimum mean square error (MMSE) estimation techniques are discussed and their performances in different VANET transmission scenarios are thoroughly investigated. Furthermore, a modified scheme of these techniques to improve the performance of the system and reduce the complexity in such a way that suits VANET communications is developed. Discreet Fourier transform and singular value decomposition techniques are companied with LS and MMSE to develop a channel estimation scheme which suits VANET channel conditions and ensures performance improvement and remarkable complexity reduction. Comprehensive simulations results show that the developed scheme greatly improves the performance of the system compared with LS and substantially reduces the complexity of MMSE.

A frequency-hopping (FH)-based dual-function multiple-input multiple-output (MIMO) radar communications system enables implementation of a primary radar operation and a secondary communication function simultaneously. The set of transmit waveforms employed to perform the MIMO radar task is generated using FH codes. For each transmit antenna, the communication operation can be realized by embedding one phase symbol during each FH interval. However, as the radar channel is time-variant, it is necessary for a successive waveform optimization scheme to continually obtain target feature information. This research work aims at enhancing the target detection and feature estimation performance by maximizing the mutual information (MI) between the target response and the target returns, and then minimizing the MI between successive target-scattering signals. The two-step cognitive waveform design strategy is based upon continuous learning from the radar scene. The dynamic information about the target feature is utilized to design FH codes. Simulation results show an improvement in target response extraction, target detection probability and delay-Doppler resolution as the number of iterations increases, while still maintaining high data rate with low bit error rates between the proposed system nodes.

This chapter investigates the problem of mobile tracking in mixed line‐of‐sight (LOS)/non‐line‐of‐sight (NLOS) conditions. It reviews the state‐of‐the‐art methods in this field. The chapter considers the problem in the Bayesian estimation framework and focus on two types of Bayesian filters: the Gaussian mixture filter (GMF) and the particle filter (PF). In the GMF section, the approximation property and the convergence results are summarized. Then, the modified extended Kalman filter (EKF) banks method, as one specific GMF, is described. In the PF section, generic PF is first introduced, and a more effective PF, approximated Rao‐Blackwellized particle filtering (ARBPF), is discussed in detail. The chapter closes with a discussion on the computation of a posterior Cramer‐Rao lower bound (CRLB) for this kind of mobile tracking problem. Simulation results are provided to compare the performance of the filtering algorithms and the posterior CRLB.

In order to detect multiple sclerosis (MS) subjects from healthy controls (HCs) in magnetic resonance imaging, we developed a new system based on machine learning. The MS imaging data was downloaded from the eHealth laboratory at the University of Cyprus, and the HC imaging data was scanned in our local hospital with volunteers enrolled from community advertisement. Inter-scan normalization was employed to remove the gray-level difference. We adjust the misclassification costs to alleviate the effect of unbalanced class distribution to the classification performance. We utilized two-level stationary wavelet entropy (SWE) to extract features from brain images. Then, we compared three machine learning based classifiers: the decision tree, k-nearest neighbors (kNN), and support vector machine. The experimental results showed the kNN performed the best among all three classifiers. In addition, the proposed SWE+kNN approach is superior to four state-of-the-art approaches. Our proposed MS detection approach is effective.

Prior results on exact error rates and outage probabilities of diversity receptions over arbitrarily correlated fading channels involve intractable nested integrals, which makes the performance analyses challenging to carry out. In this paper, we derive asymptotic expressions and asymptotically tight bounds for error rates and outage probabilities of diversity receptions (including maximal-ratio combining, selection combining, and equal-gain combining) over arbitrarily correlated Beckmann fading channels. These new asymptotic performance expressions reveal insights into the diversity systems over arbitrarily correlated Beckmann fading channels, and the accuracy of the asymptotic performance expressions is explicitly quantified by the performance bounds. Using these analytical tools, one can assess the performance of the diversity reception systems over arbitrarily correlated Beckmann fading channels without resorting to time-consuming Monte Carlo simulation or numerical multifold integration.

Hybrid-selection maximal-ratio combining (HS/MRC) is a useful diversity reception technique to reduce the number of radio frequency chains, and this diversity scheme has been well studied. However, past studies on HS/MRC have often ignored the significant insertion loss associated with hardware implementation of HS/MRC. Therefore, we consider a special class of two-stage diversity receptions known as hierarchical diversity with low insertion loss. The asymptotic error rate performances of hierarchical selection-combining MRC (SC-MRC) and hierarchical selection-combining equal-gain combining are studied for Rician fading channels with arbitrary correlation. Based on our asymptotic analysis, it is shown that, for a practical number of antenna branches, SC-MRC will suffer, at most, 0.6-dB performance losses with respect to HS/MRC; however, SC-MRC will incur significantly less insertion loss. Based on closed-form asymptotic error expressions, we also study unique transmission properties of two-stage diversity receptions over Rician channels with arbitrary branch correlation.

The modulation and demodulation problems for efficient multi-carrier communications are considered in this study. To maintain compact spectrum and realise dense multi-carrier transmission, a novel modulation method based on random hopping position, namely time hopping m-ary phase position shift keying (TH-MPPSK), is proposed to remove the discrete spectrum. Then, a multi-carrier scheme without guard interval among subcarriers is provided to reduce complexity in the practical communication systems. Moreover, a novel demodulation method is proposed to improve the efficient communication performance. Simulation results demonstrate that the TH-MPPSK can significantly remove the discrete spectrum and the proposed demodulation method can satisfactory performance.

In cognitive radar systems, a closed-loop feedback is formed by the transmitter, environment, and receiver. Then, the transmit waveform can be optimised to improve the radar estimation performance. Unlike existing works that only consider single target with temporally correlated characteristic, waveform design for multiple extended targets is investigated in this study. The authors propose a Kalman filtering (KF)-based method to exploit the temporal correlation of target scattering coefficients (TSCs) and improve the corresponding estimation performance. Additionally, for the targets of both closed and separated in range, a novel optimisation problem is established to design the transmit waveform and minimise the mean square error of estimated TSC at each KF iteration, where an additional weight vector is introduced to achieve a trade-off among different targets. Since the optimisation problem is non-convex and cannot be solved efficiently, a two-step method is proposed to convert it into a convex problem, which can be solved by an optimisation toolbox such as CVX. Simulation results demonstrate that the joint method of waveform design outperforms the existing methods in TSC estimation for both separated and closed targets, and offers much more flexibility.

Prior performance analyses of diversity receptions over arbitrarily correlated Nakagami- $m$ fading channels use power correlation matrix to define correlation among branches. However, the power correlation matrix cannot uniquely determine the joint probability density function of the branch amplitudes as well as the system performance. In this paper, we derive asymptotically tight and closed-form upper and lower bounds for the error rates and the outage probabilities of maximum-ratio combining, equal-gain combining, and selection combining over arbitrarily correlated Nakagami- $m$ fading channels. The correlation between the Nakagami- $m$ random variables is defined through the correlation matrix of the accompanying Gaussian random variables, which can uniquely determine the joint probability density function of the Nakagami- $m$ variables. Using the analytical results and Monte Carlo simulation, we show that the performance of diversity reception systems over Nakagami- $m$ fading channels cannot be uniquely determined by the power correlation matrix of the branches. Furthermore, we also study the factors that determine the asymptotic performance of the diversity receptions.

There are a number of volunteer and statutory organizations who are capable of conducting an emergency response using helicopters. Rescue operations require a rapidly deployable high bandwidth network to coordinate necessary relief efforts between rescue teams on the ground and helicopters. Due to massive destruction and loss of services, ordinary communication infrastructures may collapse in these situations. Consequently, information exchange becomes one of the major challenges in these circumstances. Helicopters can be also employed for providing many services in rugged environments, military applications, and aerial photography. Ad hoc network can be used to provide alternative communication link between a set of helicopters, particularly in case of significant amount of data required to be shared. This paper addresses the ability of using ad hoc networks to support the communication between a set of helicopters. A simplified network structure model is presented and extensively discussed. Furthermore, a streamlined routing algorithm is proposed. Comprehensive simulations are conducted to evaluate the proposed routing algorithm.

Since the impacting filter-based receiver is able to transform phase modulation into amplitude peak, a simple threshold decision can detect the Extend-Binary Phase Shift Keying (EBPSK) modulated ranging signal in noise environment. In this paper, an analysis of the EBPSK-MODEM system output gives the probability density function for EBPSK modulated signals plus noise. The equation of detection probability (pd) for fluctuating and non-fluctuating targets has been deduced. Also, a comparison of the pd for the EBPSK-MODEM system and pulse radar receiver is made, and some results are plotted. Moreover, the probability curves of such system with several modulation parameters are analysed. When modulation parameter is not smaller than 6, the detection performance of EBPSK-MODEM system is more excellent than traditional radar system. In addition to theoretical considerations, computer simulations are provided for illustrating the performance.

An improved M-ary phase position shift keying (MPPSK) modulation, aiming to increase the spectral and energy efficiencies of existing MPPSK schemes while preserving their low demodulation complexity and error performance, is proposed in this study. Specifically, an improved MPPSK with orthogonal signals called OI-MPPSK is presented. For the proposed OI-MPPSK, the authors develop two demodulation schemes that employ a special impact filter (SIF): (i) the orthogonal SIF correlator (OSC) scheme, which consists of the orthogonal transmission, the SIF and a correlator; (ii) the orthogonal SIF position (OSP) scheme, which is the same as the OSC scheme except that the coherent demodulator is replaced by exploiting position-related information. As a result of exploiting the position-related information in the proposed OI-MPPSK scheme, the SER performance of the OSP-based demodulator is superior to that of the OSC-based demodulator.

Optimal relay placement is studied for free-space optical multihop communication with link obstacles and infeasible regions. An optimal relay placement scheme is proposed to achieve the lowest outage probability, enable the links to bypass obstacles of various geometric shapes, and place the relay nodes in specified available regions. When the number of relay nodes is large, the searching space can grow exponentially, and thus, a grouping optimization technique is proposed to reduce the searching time. We numerically demonstrate that the grouping optimization can provide suboptimal solutions close to the optimal solutions, but the average searching time linearly grows with the number of relay nodes. Two useful theorems are presented to reveal insights into the optimal relay locations. Simulation results show that our proposed optimization framework can effectively provide desirable solution to the problem of optimal relay nodes placement.

A distance-dependent cooperative scheme is proposed for free-space optical communications with a single-relay-assisted scenario. The new scheme exploits a fact that the diversity order of a free-space optical channel is related to the link distance. It is shown that, both analytically and numerically, the frame error rate of the new scheme can provide higher diversity order than a benchmark adaptive decode-and-forward scheme.

This work investigates the estimation of target scattering coefficients (TSC) in cognitive radar systems with temporally correlated targets. An estimation method based on Kalman filtering (KF) is proposed to exploit the temporal TSC correlation between the pulses in the frequency domain. To minimize the mean square error of the estimated TSC at each KF iteration, unlike existing indirect methods, in this paper the radar waveform is optimized directly under the constraints of transmitted power, peak-to-average power ratio (PAPR) and detection probability. Since the optimization problem regarding the waveform design is non-convex, a novel method is proposed to convert this problem into a convex one. Simulation results demonstrate that the performance of the TSC estimation for the temporally correlated target is significantly improved by radar waveform optimization. Meanwhile, no performance degradation is observed with the introduction of the additional PAPR constraints and the detection constraints for KF estimation with the optimized waveform.

In this paper, we consider the pilot design based on the mutual incoherence property (MIP) for sparse channel estimation in orthogonal frequency-division multiplexing (OFDM) systems. With respect to the length of channel impulse response (CIR), we first derive a sufficient condition for the optimal pilot pattern generated from the cyclic different set (CDS). Since the CDS does not exist for most practical OFDM systems, we propose three pilot design schemes to obtain a near-optimal pilot pattern. The first two schemes, including stochastic sequential search (SSS) and stochastic parallel search (SPS), are based on the stochastic search. The third scheme called iterative group shrinkage (IGS) employs a tree-based searching structure and removes rows in a group instead of removing a single row at each step. We later extend our work to multiple-input–multiple-output (MIMO) systems and propose two schemes, i.e., sequential design scheme and joint design scheme. We also combine them to design the multiple orthogonal pilot patterns, i.e., using the sequential scheme for the first several transmit antennas and using the joint scheme to design the pilot pattern for the remaining transmit antennas. Simulation results show that the proposed SSS, SPS, and IGS converge much faster than the cross-entropy optimization and the exhaustive search and are thus more efficient. Moreover, SSS and SPS outperform IGS in terms of channel estimation performance.

Aimed at the problem of detecting maneuvering targets in the Gaussian and sea clutter environments and based on the established motion state model, this paper proposed a new scheme that uses a joint radar-communication (JRC) system with Kalman filter to accurately detect the target with the generalized likelihood ratio test (GLRT) theory and a constant false alarm rate (CFAR) based threshold. Also, the theoretical threshold and probability function of GLRT target detection based on CFAR were given. Moreover, target detection probability of the new JRC system in Weibull andKdistribution clutter is deduced. In addition to theoretical considerations, simulations and measurement results of the new JRC systems demonstrate excellent detection performance for maneuvering targets in the Weibull andKdistribution channel.

Considering the problem of EBPSK signal demodulation, a new approach based on the wavelet scalogram using continuous wavelet transform is proposed. Our system is twofold: an adaptive wavelet construction method that replaces manual selection existing wavelets method and, on the other hand, a nonlinear demodulation system based on image processing and pattern classification is proposed. To evaluate the performance of the adaptive wavelet and compare the performance of the proposed system with the existing systems, a series of comprehensive simulation experiments is conducted under the environment of uniform white noise, colored noise, and additive white Gaussian noise channel, respectively. Simulation results of different wavelets show that the system using adaptive wavelet has lower bit error rate (BER). Moreover, simulation results of several systems show that the BER of the proposed system is the lowest among all systems, such as amplitude detection, integral detection, and some continuous wavelet transform systems (specific scales and times and maximum lines). In a word, the adaptive wavelet construction proposed in this paper yields superior performances compared with the manual selection, and the proposed system has better performances than the existing systems. Index terms are signal demodulation, adaptive wavelet, continuous wavelet transform, and BER.

The system optimization is considered in cognitive radar system (CRS) with extended binary phase shift keying- (EBPSK-) based multicarrier phase-coded (MCPC) signal. A novel radar working scheme is proposed to consider both target detection and estimation. At the detection stage, the generalized likelihood ratio test (GLRT) threshold is deduced, and the GLRT detection probability is given. At the estimation stage, an approach based on Kalman filtering (KF) is proposed to estimate target scattering coefficients (TSC), and the estimation performance is improved significantly by exploiting the TSC temporal correlation. Additionally, the optimal waveform is obtained to minimize the mean square error (MSE) of KF estimation. For the practical consideration, iteration algorithms are proposed to optimize the EBPSK-based MCPC signal in terms of power allocation and coding matrix. Simulation results demonstrate that the KF estimation approach can improve the estimation performance by 25% compared with maximum a posteriori MAP (MAP) method, and the KF estimation performance can be further improved by 90% by optimizing the transmitted waveform spectrum. Moreover, by optimizing the power allocation and coding matrix of the EBPSK-based MCPC signal, the KF estimation performances are, respectively, improved by 7% and 8%.

In single frequency network (SFN) positioning, base station (BS) identification is inevitable and non-line-of-sight (NLOS) propagation is usually dominant especially for indoor scenarios. BS identification and NLOS mitigation are two challenging problems which have significant impact on the SFN positioning performance. In this paper, a mobile tracking scheme is proposed to deal with these challenging issues. Specifically, BS identification is first formulated as a data validation problem. Each time-of-arrival (TOA) measurement is tentatively associated with a specific BS so that a number of TOA-BS relationship sets are produced. The gate technique is adapted to evaluate all the TOA-BS relationship sets and the set with the smallest gate parameter value is selected. This identification technique is suited for both line-of-sight (LOS) and NLOS propagation scenarios. The interacting multiple model (IMM) smoother is then utilized to smooth the identified TOA measurements at each BS to reduce the NLOS errors. In addition, the position determination and BS identification are jointly considered to enhance position estimation accuracy. Simulation results demonstrate that the proposed SFN positioning approach can perform satisfactorily in different propagation scenarios and has better performance than other SFN positioning algorithms.

In this paper, we propose a novel hybrid peak-to-average power ratio (PAPR) reduction approach combining multi-band Jacket matrix spreading (MB-JS) with the clipping and filtering for the orthogonal frequency division multiplexing (OFDM)-based optical communication systems. The PAPR performance of the proposed scheme is theoretical analyzed and the received signal-to-noise ratio (SNR) for each split sub-band are calculated. The 50 m step-index (SI) polymer optical fiber (POF) transmission based on offline processing is adopted as a special case to evaluate the system performance and then make comparisons with some other well known PAPR reduction techniques. With the help of this methodology, at least 2.57 dB PAPR reduction is obtained and 4 dBm power savings is achieved when compared to the original OFDM transmission. It is demonstrated that the proposed scheme can offer better system performance and make a favorable trade-off among the PAPR reduction, bit error rate and computational complexity.

In this paper, we propose a modified clipping noise cancellation scheme using compressed sensing (CS) technique with observation interference mitigation for direct current biased optical (DCO) orthogonal frequency division multiplexing (OFDM) systems. The interference components in potential observations are theoretically analyzed and approximately estimated by exploiting the statistical model of the clipped DCO-OFDM signal. Then, the modified CS scheme eliminates the estimated interference from the potential observations, which can minimize the contamination influence of channel noise on CS recovery algorithm. In addition, a strategy jointly considering the compressed ratio and the decision noise is presented to generate the measurement matrix, which can sample the reliable data tones as final observations. With this scheme, the clipping noise in time domain can be effectively corrected and the bit error rate (BER) performance of the system is significantly improved. Simulation results demonstrate that the proposed scheme can perform well even under severe clipping conditions.