Search

In this article, the estimation of random channel gain is studied for a single-input single-output (SISO) visible light communication (VLC) system. Five different estimators, namely maximum likelihood (ML), least square (LS), maximum posteriori probability (MAP), linear minimum mean square error (LMMSE), and minimum mean square error (MMSE), are proposed. The performances of these estimators are compared with the derived Bayesian Cramér–Rao lower bound (BCRLB), which can be used as a benchmark to evaluate the efficiency of the unbiased estimators. The presented analytical results, corroborated with Monte Carlo simulations, indicate that the MMSE estimator provides the best results. Additionally, the increasing number of pilot symbols as well as the ascending transmitted power improve the system performance. On the other hand, the noise variance has a negative effect on the channel estimation in terms of mean square error (MSE), and thus, it can dramatically reduce the performance of the estimators. [ABSTRACT FROM AUTHOR]

Target localization is widely applied in wireless sensor networks (WSNs). In this article, we tackle the problem of position estimation for multiple stationary targets using Doppler frequency shifts and angles by moving sensor arrays. The computational load for the exhaustive maximum likelihood (ML) direct position determination (DPD) search is insufferable. Based on Pincus’ theorem and the importance sampling (IS) concept, we propose a novel noniterative ML DPD method, and the circular mean is used for superior position estimation performance; meanwhile, a concrete criterion is also proposed for the choice of the importance function. The computational complexity of the proposed method is modest, and the off-grid problem that most existing DPD techniques face is significantly alleviated. Moreover, it can be implemented in parallel separately. In addition, we present the closed-form Cramér–Rao lower bound (CRLB) for the DPD with moving sensor arrays and stationary targets. Simulation results demonstrate that the proposed ML DPD estimator can achieve better estimation accuracy than state-of-the-art DPD techniques. With a reasonable parameter choice, the estimation performance of the proposed technique is very close to the CRLB, even in the adverse conditions of low signal-to-noise ratio (SNR) levels. [ABSTRACT FROM AUTHOR]

This paper presents a technique to mitigate multiple access interference (MAI) in multicarrier code division multiple access (MC-CDMA) wireless communications systems. Although under normal circumstances the MC-CDMA system can achieve high spectral efficiency and resistance towards inter symbol interference (ISI) however when exposed to substantial nonlinear distortion the issue of MAI manifests. Such distortion results when the power amplifiers are driven into saturation or when the transmit signal experiences extreme adverse channel conditions. The proposed technique uses a modified iterative block decision feedback equalizer (IB-DFE) that uses a minimal mean square error (MMSE) receiver in the feed-forward path to nullify the residual interference from the IB-DFE receiver. The received signal is re-filtered in an iterative process to significantly improve the MC-CDMA system’s performance. The effectiveness of the proposed modified IB-DFE technique in MC-CDMA systems has been analysed under various harsh nonlinear conditions, and the results of this analysis presented here confirm the effectiveness of the proposed technique to outperform conventional methodologies in terms of the bit error rate (BER) and lesser computational complexity.

This communication presents an efficient iterative approach for locating the near-field (NF) coherent sources. In each iteration, the covariance matrices only containing single source information are constructed by using alternating oblique projection. Then based on the principle of vector dot products, a new iterative direction-of-arrival (DOA) estimator is proposed. After the DOA of each separated signal is estimated, the paired ranges are obtained from the 1-D maximum likelihood (ML) estimator. The proposed algorithm avoids high-dimensional spectral searches, subspace extraction, and any preprocessing such as spatial smoothing, leading to low computational complexities and high estimation accuracy. Comparative simulations show the efficiency and merits of the proposed method. [ABSTRACT FROM AUTHOR]

The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direction-of-arrival estimation theory. The latter allows achieving super resolution, along with ambiguity levels reduction, thanks to the use of parametric focusing methods, as multiple signal classification (MUSIC) and statistical regularization techniques, like the maximum-likelihood-inspired adaptive robust iterative approach (MARIA). Nevertheless, in order to correctly suit the considered signal model, MUSIC and most regularization approaches require an appropriate setting of the involved parameters. In both cases, the accuracy of the retrieved solutions depends on the right selection of the assigned values. Thus, with the aim of dealing with such an issue, this article addresses several parameter selection strategies, adapted specifically to the TomoSAR scenario. Parametric techniques as MUSIC solve the TomoSAR problem in a different manner as the regularization methods do, hence, each approach demands different methodologies for the proper estimation of their parameters. Consequently, we refer to the Kullback-Leibler information criterion for the model order selection of parametric techniques as MUSIC, whereas we rather explore the Morozov's discrepancy principle, the L-Curve, the Stein's unbiased risk estimate, and the generalized cross-validation to choose the regularization parameters. After the incorporation of these criteria to MUSIC and MARIA, respectively, their capabilities are first analyzed through simulations, and later on, utilizing real data acquired from an urban area.

The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classification (MUSIC). Parametric methods like MUSIC are reduced to the problem of selecting an integer-valued parameter [so-called model order (MO)], which describes the number of signals impinging on the sensors array. Commonly, the best MO corresponds to the actual number of targets, nonetheless, relatively large model orders also retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit it. Most commonly employed MO selection (MOS) tools are based on information theoretic criteria (e.g., Akaike information criterion, minimum description length and efficient detection criterion). Normally, the implementation of these tools involves the eigenvalues decomposition of the data covariance matrix. A major drawback of such parametric methods (together with certain MOS tool) is the drastic accuracy decrease in adverse scenarios, particularly, with low signal-to-noise ratio, since the separation of the signal and noise subspaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this article also explores the possibility of selecting a single relatively large MO (rather than using MOS tools) and enhancing via regularization, the solutions retrieved by the treated parametric methods. In order to demonstrate the capabilities of this novel strategy, synthetic aperture radar tomography is considered as application.

We consider Global Navigation Satellite Systems (GNSS) spoofing attacks and devise a countermeasure appropriate for mobile GNSS receivers. Our approach is to design detectors that, operating after the signal acquisition, enable the victim receiver to determine with high probability whether it is under a spoofing attack or not. Namely, the binary hypothesis is that either the GNSS receiver tracks legitimate satellite signals, $\mathcal {H}_{0}$ , or spoofed signals, $\mathcal {H}_{1}$ . We assume that there exists an unknown number of multiple spoofers in the environment and the attack strategy (which legitimate signals are spoofed by which spoofers) is not known to the receiver. Based on these assumptions, we propose an algorithm that identifies the number of spoofers and clusters the spoofing data by using Bayesian information criterion (BIC) rule. Depending on the estimated and clustered data we propose a detector, called as generalized likelihood ratio (GLRT)-like detector. We compare the performance of the GLRT-like detector with a genie-aided detector in which the attack strategy and the number of spoofers is known by the receiver. In addition to this, we extend the GLRT-like detector for the case where the noise variance is also unknown and present the performance results.

In this article, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods. [ABSTRACT FROM AUTHOR]

This article addresses the 3-D reconstruction of urban areas using a minimal number of Synthetic Aperture Radar (SAR) acquisitions, that is, a set of three images, characterized by intermediate spatial resolution features. In such extreme conditions, conventional tomographic techniques reveal unadapted to refined 3-D imaging purposes, either due to the resulting intrinsic coarse vertical resolution, or to the low dimensionality of the data set, that prevents any separation of complex mixed scattering patterns. A new high-resolution (HR) tomographic estimator, based on a polarimetric signal subspace fitting criterion, is proposed to overcome these limitations, as this method adapts to the statistical behavior of the backscattered signals using robust metrics. The optimization of the corresponding focusing criterion is led through a new polarimetric alternating projection algorithm, characterized by a low computational cost, which may also be used to optimize the polarimetric the deterministic maximum likelihood criterion. The proposed polarimetric signal subspace fitting technique is shown to outperform the other studied HR techniques over both simulated signals and data acquired by the DLR’s ESAR sensor at L-band over Dresden city, Germany. Finally full-rank polarimetric tomographic estimators are proposed that generalize nonparametric polarimetric estimators, and permit to estimate second-order polarimetric representations in 3-D, instead of unitary rank target vector with their conventional versions. This approach makes it possible to characterize polarimetric scattering mechanisms in 3-D. [ABSTRACT FROM AUTHOR]

Due to the severe impact of dynamic plasma sheath on electromagnetic (EM) waves, the communication quality of hypersonic vehicles deteriorates, even resulting in communication blackouts during the reentry process. In this article, the hypersonic vehicle downlink integrated channels that cascade the plasma sheath channel and Rice fading channel, i.e., the downlink telemetry channels, are first established, and then the demodulation method and performance of typical telemetry modulations including the M-ary phase shift keying (MPSK), M-ary amplitude shift keying (MASK), and M-ary frequency shift keying (MFSK) are studied. Considering the high dynamics of the plasma sheath, the pilot signals are used to detect the signal correctly for MPSK, and the non-coherent demodulation is adopted for MASK and MFSK. Especially, an adaptive maximum likelihood (ML) receiver for ASK is proposed based on a reduced reversible jump Markov chain Monte Carlo (RRJ-MCMC) algorithm to estimate the probability density function (pdf) of the received signal. The simulation results show that the communication performance of PSK and ASK is improved with the modulation order $M$ decreasing; the performance of non-coherent FSK is improved with the modulation order $M$ increasing. Meanwhile, the performance follows the order of NC-MFSK (M ≥ 8) > BPSK > NC-4FSK > NC-2FSK > 2ASK. For NC-8FSK, when the electron density peak value ${Ne}_{\mathrm {peak}}$ = {2, 4, 6, 8, 10} $\times 10^{18}\,\,\text{m}^{-3}$ , the signal to noise ratio (SNR) required to reduce the bit error rate (BER) to 10−6 are 17, 18, 19, 21, and 34 dB, respectively. [ABSTRACT FROM AUTHOR]

This work proposes novel techniques toward the design of optimal pilot sequences to perform channel estimation in block transmission systems over wideband frequency selective wireless fading channels. The framework developed is based on minimization of the Bayesian Cramér-Rao bound (BCRB) for the mean squared error (MSE) of the channel state information (CSI) estimate. Optimal pilot signals are determined for the four predominant classes of block transmission systems, viz. single carrier zero padding (SC-ZP), multi-carrier zero padding (MC-ZP), single carrier cyclic prefix (SC-CP) and multi-carrier cyclic prefix (MC-CP) systems. This makes the techniques developed general in nature and thus applicable in a wide variety of block transmission systems. As part of this study, succinct expressions and results are also derived to characterize the error rate performance, incorporating also the effect of CSI estimation error resulting due to the proposed algorithms. Finally, numerical results obtained via Monte-Carlo simulation are presented to illustrate and compare the CSI acquisition performance of optimal pilot designs with that of conventional designs and also validate the theoretical analysis for the error rate performance.

A multiple-input multiple-output (MIMO) radarwith widely separated antennas on moving platforms suffers from the effects of platform deviations on the target parameter estimation since the trajectories of the moving platforms are sensitive to environmental factors, such as strong wind. This article addresses the joint estimation of multiple target positions and velocities as well as radar system deviations to minimize the impact of platform deviations. The proposed algorithm can also be regarded as a self-calibration technique. First, the grid search dimensions for parameter estimation are reduced via a generalized maximum likelihood (GML) algorithm. Second, the adaptive gradient (AdaGrad) method is used to implement the GML estimation for multitarget echo delays and Doppler shifts. Finally, to address the nonlinear estimation problem of interest, the iterative least squares method is used to estimate the multiple target positions and velocities as well as radar system deviations based on the estimated delays and Doppler shifts. Prior information, such as target positions or the probability distribution of the echo coefficient, is not needed in the proposed method. The parameter identifiability is also derived in this article. Numerical simulations show that compared to a method without the estimation of system deviations, the proposed method is more efficient with respect to the derived Cramér–Rao bound. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient channel estimation (CE) to determine the frequency response of overall subcarriers for universal filtered multicarrier systems, which promises excellent performance without a significant increase in computational complexity. Specifically, based on the even-subcarrier received signal, we design a pilot-assisted scheme combined with a partial inverse fast Fourier transform operation to obtain an estimate of the channel impulse response. Then, to alleviate the power efficiency loss caused by discarding the odd subcarriers, a performance-enhanced CE using the Neumann series expansion technique is proposed. This provides a performance comparable to that of the maximum likelihood-based approach involving a prohibitively burdensome computation. A closed-form mean square error expression and computational complexity analysis are derived to quantify the achievable advantages. Simulation study confirms the effectiveness of the proposed CE. [ABSTRACT FROM AUTHOR]

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position and timing information by round-trip communications to a number of anchor nodes (ANs) at known locations. Compared with the one-way TOA technique, the two-way TOA scheme is easy to implement and has higher localization and synchronization accuracy. Existing two-way TOA methods assume a stationary UD. This will cause uncompensated position and timing errors. In this article, we propose an optimal maximum likelihood (ML) based two-way TOA localization and synchronization method, namely TWLAS. Different from the existing methods, it takes the UD mobility into account to compensate the error caused by the UD motion. We analyze its estimation error and derive the Cramér-Rao lower bound (CRLB). We show that the conventional two-way TOA method is a special case of the TWLAS when the UD is stationary, and the TWLAS has high estimation accuracy than the conventional one-way TOA method. We also derive the estimation error in the case of deviated UD velocity information. Numerical result demonstrates that the estimation accuracy of the new TWLAS for a moving UD reaches CRLB, better than that of the conventional one-way TOA method, and the estimation error caused by the deviated UD velocity information is consistent with the theoretical analysis. [ABSTRACT FROM AUTHOR]

For 2-D causal systems, the variables depend both on time, and on spatial coordinates. This article develops two identification algorithms for two-dimensional causal systems. First, a maximum likelihood estimation algorithm is developed for two-dimensional causal systems when there is no missing data. Second, an expectation–maximization based auxiliary model algorithm, and an expectation–maximization based modified Kalman filtering and smoothing algorithm are derived for 2-D causal systems with missing outputs. It is demonstrated that the modified Kalman filtering, and smoothing algorithm is more effective for systems with missing outputs. The effectiveness of these two algorithms is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Maximum likelihood (ML) method for direction of arrival (DOA) estimation achieves an excellent performance in array signal processing, but the complexity and computational load of searching the multidimensional nonlinear function prevented it from practical application. Based on squirrel search algorithm (SSA), an improved SSA (ISSA) for ML DOA estimation is proposed in this paper, which can reduces the computational complexity. The idea of spatial variation and diffuse inspired by the invasive weed optimization(IWO) algorithm is applied to ISSA. The simulation experiments compared ISSA with SSA, IWO, seeker optimization algorithm(SOA), sine cosine algorithm (SCA), genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) method for ML DOA estimator show that the proposed algorithm has faster convergence speed, fewer iterations and lower root mean square error(RMSE) under different number of signal sources, different signal to noise ratio(SNR) and different population size. Therefor the proposed algorithm does not only ensure the estimation accuracy, but also greatly reduce the computation complexity of multidimensional nonlinear optimization for the ML method. Finally, the test experiment using Micro Electronic Mechanical Systems(MEMS) vector hydrophone array in Fenhe lake show the engineering practicability of proposed ML DOA estimator with ISSA.The results obtained will be valuable in the application of engineering.

The millimeter-wave (mmWave) technology is one possible solution to addressing the explosive requirements for mobile data access on high-speed railways (HSRs). However, utilizing the mmWave technology on HSRs will result in large Doppler shifts, which should be estimated and compensated. Traditional Doppler shift estimators, such as the cyclic prefix (CP)-based estimator (CPBE), the two-training-symbol-based estimator (TBE), and the best linear unbiased estimator (BLUE), have inferior estimation accuracy especially at low signal-to-noise ratio (SNR). In this paper, we, therefore, propose three new Doppler shift estimators for mmWave communication systems on HSRs: a radio environment map (REM)-based estimator (RBE) derived from the maximum a posteriori algorithm; an equally-divided structure-based estimator (ESBE) that divides the effective orthogonal frequency-division multiplexing symbol (OFDM) into multiple equal fragments; and an enhanced ESBE (EESBE) that takes the results of RBE as a priori knowledge. Simulation results show that these three algorithms outperform existing Doppler shift estimators in mmWave communication systems on HSRs.

In a multi-connected, multi-technology, and pervasive mobile infrastructure, such as what is being planned for 5G, artificial intelligence and cognition will play a major role. An important goal of future mobile infrastructures is to self-adapt their characteristics to their operating conditions, at the physical link, as well as at the network and application layers, which gives rise to a new paradigm known as context-aware cognitive radio (CR). CR transceivers (CTRs) mostly incorporate a cognitive engine that relies on various sensorial entities, which attempt to provide sufficient information about the quality of the link through the estimation of various key channel parameters. Two important parameters are required in a wide range of CTR architectures: the signal-to-noise ratio (SNR) and the Doppler spread. Within this context, we tackle the hardware design and integration of a joint data-aided (DA) maximum likelihood (ML) SNR and Doppler spread estimator recently shown to outperform main state-of-the-art solutions both in terms of accuracy and complexity. We propose a deep-pipelined and resource-efficient architecture for the outlined joint DA ML estimator, and we integrate our design on an FPGA-based software-defined radio (SDR) platform. We finally validate and test this prototype in real time under realistic over-the-air propagation conditions reproduced by a highly-scalabile channel emulator. Compared to its MATLAB floating-point version, our hardware prototype suggests negligible losses in performance despite the existence of several hardware impairments, thereby confirming its very strong potential and attractiveness for possible integration in future 5G CTRs.

This article deals with the problem of adaptive target detection in the presence of homogeneous Gaussian interference with frequency diverse array multiple-input multiple-output radar. Adaptive detectors are devised according to the generalized likelihood ratio test criterion, where the position of the target within each range cell is assumed unknown. To obtain the maximum likelihood estimate of the target incremental range under the $H_1$ hypothesis, three different optimization strategies are pursued. They are, respectively, based on semidefinite programming, discrete grid search, and Newton method. At the analysis stage, a detection performance comparison is carried on among the new proposed adaptive detectors, benchmark, and mismatched receivers. Numerical results corroborate the effectiveness of the developed receivers. [ABSTRACT FROM AUTHOR]

This paper divides antennas at the transmitter into several transmit antenna groups (TAGs) and extends the generalized spatial modulation (GSM) idea to a new precoding-aided multiple-input multiple-output system, which is referred to as precoding-aided GSM (PGSM) system. To mitigate the influence caused by correlated channels, we consider the interleaved grouping scheme which leads to a minimum antenna correlation within the same TAG. To select the receive antenna subset with low complexity, we propose an iterative greedy (IG) algorithm that contributes to a maximum equivalent channel gain with low-computational complexity. To reduce the complexity of maximum likelihood (ML) detector, a low complexity IG algorithm-based ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with an ordered block minimum mean-squared error detector at a given BER of 10−4. It is also shown that the proposed PGSM with the IG-ML detector achieves a similar BER performance in a high signal-to-noise power ratio region compared with that of an ideal precoding for the GSM with an optimal ML detector.

Image segmentation lies at the heart of multiple image processing chains, and achieving accurate segmentation is of utmost importance as it affects later processing. Image segmentation has recently gained interest in the field of remote sensing, mostly due to the widespread availability of remote sensing data. This increased availability poses the problem of transmitting and storing large volumes of data. Compression is a common strategy to alleviate this problem. However, lossy or near-lossless compression prevents a perfect reconstruction of the recovered data. This letter investigates the image segmentation performance in data reconstructed after a near-lossless or a lossy compression. Two image segmentation algorithms and two compression standards are evaluated on data from several instruments. Experimental results reveal that segmentation performance over previously near-lossless and lossy compressed images is not markedly reduced at low and moderate compression ratios (CRs). In some scenarios, accurate segmentation performance can be achieved even for high CRs. [ABSTRACT FROM AUTHOR]

As bias estimation methods are developed, it becomes necessary to obtain the bound on bias estimation for more complex bias and sensor models. Three-dimensional (3-D) sensors, such as radars commonly used in applications, contain both scale and additive biases in sine space which result in a nonlinear estimation problem that may have poor observability and accuracy depending on the geometry of the sensors. By converting the sine space and range measurements to Cartesian using an unbiased conversion, it is possible, via creation of pseudomeasurements, to eliminate the need to estimate the target's state thereby reducing the sensor bias estimator complexity. The present paper evaluates the Cramér–Rao lower bound (CRLB) for estimating scale and additive biases in sine space for 3-D sensors and compares it with a maximum likelihood formulation implemented via iterated least squares, which is thereby shown to be statistically efficient. Additionally, the importance of measurement diversity is investigated with respect to the CRLB. [ABSTRACT FROM AUTHOR]

This paper addresses the performance characterization of a direction of arrival (DoA) estimator in the low signal-to-noise-ratio (SNR) region. The case of a sensor array simultaneously collecting signals emitted at multiple carrier frequencies by a single source is considered. A maximum likelihood (ML) approach is used as a reference method for DoA estimation and its accuracy is characterized in terms of mean square error (MSE). It is well known that for SNR values included in the so-called threshold region, the DoA estimation accuracy decreases rapidly, due to the presence of outliers. This effect can be possibly mitigated when multiple frequency channels are jointly exploited. However, the capability to predict this performance degradation is fundamental either for assessing the robustness of an existing sensor or for supporting its design. Therefore, the scope of this paper is to introduce appropriate approximations to the MSE of a multifrequency ML DoA estimator in order to provide a reliable characterization of its performance in the threshold region. Two models for the source signals are considered and separately discussed, namely the deterministic (or conditional) and stochastic (or unconditional). An extensive simulated analysis is reported to prove the tightness of the approximations and to characterize the benefits steming from the exploitation of signals emitted at multiple carriers. [ABSTRACT FROM AUTHOR]

We consider range estimation and range-Doppler imaging using signed measurements of a radar system. Known time-varying thresholds are investigated for taking the signed measurements through one-bit sampling, and are compared with unknown dithering with known probability density function. The maximum likelihood (ML) approach is considered for signal parameter estimation. Since the ML algorithm is computationally prohibitive, a relaxation-based approach, referred to as the One-Bit RELAX algorithm, is used for signal parameter estimation. The conventional RELAX algorithm proposed for high-resolution sampling is also considered for comparison purposes. Moreover, a model-order selection tool, namely the Bayesian information criterion, is used to determine the number of scatterers within the scene of interest. Both numerical and experimental examples are provided to demonstrate the performance of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel scheme termed layered orthogonal frequency division multiplexing with index modulation (L-OFDM-IM) to increase the spectral efficiency (SE) of OFDM-IM systems. In L-OFDM-IM, all subcarriers are first divided into multiple layers, each determining the active subcarriers and their modulated symbols. The index modulation (IM) bits are carried on the indices of the active subcarriers of all layers, which are overlapped and distinguishable with different signal constellations so that the number of the IM bits is larger than that in traditional OFDM-IM. A low-complexity detection is proposed to alleviate the high burden of the optimal maximum-likelihood detection at the receiver side. A closed-form upper bound on the bit error rate, the achievable rate, and diversity order are derived to characterize the performance of L-OFDM-IM. To enhance the diversity performance of L-OFDM-IM, we further propose coordinate interleaving L-OFDM-IM (CI-L-OFDM-IM), which interleaves the real and imaginary parts of the modulated symbols over two different subchannels. Computer simulations verify the theoretical analysis, and results show that L-OFDM-IM outperforms the conventional OFDM-IM scheme. Moreover, it is also confirmed that CI-L-OFDM-IM obtains an additional diversity order in comparison with L-OFDM-IM. [ABSTRACT FROM AUTHOR]

This paper introduces sparse low-power sensor networks using passive electric potential sensors for crowd-aware smart buildings. The proposed networks can detect human presence, track their positions, and estimate room occupancy by measuring the electric field disturbances caused by human movement. In addition, the proposed passive sensing system can remotely monitor human physiological signals of nearby stationary occupants (standoff ~1.5 m) when the ambient 60-Hz fields are filtered out. Based on an experimentally calibrated source range model, we further propose an improved multiple-occupant near-field indoor localization method using a maximum likelihood algorithm combined with dividing rectangular search. The tradeoff between search efficiency and localization accuracy is also discussed. Experimental results in two different rooms for 1 week duration show the effectiveness of our proposed sensing system: human identification with an accuracy of 98.3% on six participants, occupant localization methods with room occupancy estimation accuracy of 89.03%, and average localization error less than 0.3 m. These results open up new possibilities in security, indoor navigation or positioning systems, human–machine interfaces, and tele-health applications. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method to assess the social activity maintained by psychiatric patients using information and communication technologies. In particular, we model the daily usage patterns of phone calls and social and communication apps using point processes. We propose a novel nonhomogeneous Poisson process model with periodic (circadian) intensity function using a truncated Fourier series expansion, which is inferred using a trust-region algorithm. We also extend the model using a mixture of periodic intensity functions to cope with the different daily patterns of a person. The analysis of the usage of phone calls and social and communication apps of a cohort of 259 patients reveals common patterns shared among patients with relatively high homogeneity and differences among patient pathologies. [ABSTRACT FROM AUTHOR]

In underwater wireless sensor networks, time synchronization and localization are basic requirements in many applications. A joint synchronization and localization framework is expected to provide better accuracy. In this paper, we propose a unified framework to execute synchronization and localization simultaneously taking stratification effect into account. In this method, the stratification effect of underwater medium is modeled using a ray tracing approach. The maximum likelihood (ML) estimator is derived, which is shown to be highly nonlinear and nonconvex. Therefore, we employ the Gauss-Newton algorithm to solve the original nonconvex ML problem in an iterative manner. Furthermore, the Cramér-Rao lower bound for this problem is derived as a benchmark. Simulation results indicate that the proposed method outperforms the existing methods in both accuracy and energy efficiency.

We consider the problem of estimating the Probability Mass Function (PMF) of a discrete random vector (RV) from partial observations, namely when some elements in each observed realization may be missing. Since the PMF takes the form of a multi-way tensor, under certain model assumptions the problem becomes closely associated with tensor factorization. Indeed, in recent studies it was shown that a low-rank PMF tensor can be fully recovered (under some mild conditions) by applying a low-rank (approximate) joint factorization to all estimated joint PMFs of subsets of fixed cardinality larger than two (e.g., triplets). The joint factorization is based on a Least Squares (LS) fit to the estimated lower-order sub-tensors. In this letter we take a different estimation approach by fitting the partial factorization directly to the observed partial data in the sense of Kullback-Leibler divergence (KLD). Consequently, we avoid the need for particular selection and direct estimation of sub-tensors of a particular order, as we inherently apply proper weighting to all the available partial data. We show that our approach essentially attains the Maximum Likelihood estimate of the full PMF tensor (under the low-rank model) and therefore enjoys its well-known properties of consistency and asymptotic efficiency. In addition, based on the Bayesian model interpretation of the low-rank model, we propose an Estimation-Maximization (EM) based approach, which is computationally cheap per iteration. Simulation results demonstrate the advantages of our proposed KLD-based hybrid approach (combining alternating-directions minimization with EM) over LS fitting of sub-tensors. [ABSTRACT FROM AUTHOR]

Channel estimation is of great challenge for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The underlying reason can be attributed to two facts. One is the huge dimension of MIMO channel matrix, since mmWave systems typically employ large antenna arrays at both the transmitter and receiver to realize sufficient link margin. The other fact is the limited number of radio-frequency chains available for transmitter/receiver array, due to the power and cost limitations. This paper proposes a two-stage accurate channel estimation scheme for mmWave MIMO. Initially, we utilize sparse Bayesian learning to obtain a coarse channel estimate. Then we refine the channel estimate by iteratively maximizing the likelihood function of channel parameters. It is shown through simulations that the proposed scheme significantly outperforms the counterparts, and can achieve accurate channel estimation performance. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations. [ABSTRACT FROM AUTHOR]

In this paper, we investigate maximum likelihood (ML) time delay (TD) and carrier frequency offset (CFO) synchronization in multi-node decode-and-forward cooperative relaying systems operating over time-varying channels. This new synchronization scheme is embedded into a distributed multiple input multiple output (MIMO)-relay beamforming transceiver structure to avoid the drawbacks of multidimensional ML estimation at the destination and to minimize the overhead cost. By accounting for a perfect Doppler spread value, the new synchronization solution delivers accurate TD and CFO estimates. For real-world operation, however, this new technique can be jointly implemented with any Doppler spread estimator in a new iterative scheme using a time-constant channel (TCC)-based synchronization method at the initialization step. The resulting TD and CFO estimates along with the channel estimates are then fed into a distributed MIMO-relay beamforming transceiver of $K$ single-antenna nodes, for pre-compensation at each node of the transmitted signals, to ensure constructive maximum ratio combining (MRC) at the destination. Simulation results show significant synchronization accuracy improvement over previous distributed multi-node synchronization techniques assuming TCCs. The latter translates into noticeable gains in terms of useful link-level throughput, more so at higher Doppler or with more relaying nodes. [ABSTRACT FROM AUTHOR]

The product model is assumed to be an appropriate statistical model for multilook polarimetric synthetic radar data (PolSAR). According to this model, the observed signal is considered as the product of independent random variates of a complex Gaussian speckle and a non-Gaussian texture. With different texture distributions, the product model leads to different expressions for the compound distribution considered as an infinite mixture model. In this paper, the maximum-likeli-hood (ML) estimator is derived to jointly estimate the speckle and texture parameters in the compound distribution model using the multilook polarimetric radar data. In particular, we estimate: 1) the equivalent number of looks; 2) the covariance matrix of the speckle component; and 3) the texture distribution parameters. The expectation–maximization algorithm is developed to compute the ML estimates of the unknown parameters. The hybrid Cramer–Rao bounds (HCRBs) are also derived for these parameters. First, a general HCRB expression is derived under an arbitrary texture distribution. Then, this expression is simplified for a specific texture distribution. The performance of the ML is compared with the performance of other known estimators using the simulated and real multilook PolSAR data. For real data, a goodness of fit of multilook PolSAR data histograms is used to assess the fitting accuracy of the compound distributions using different estimators. [ABSTRACT FROM AUTHOR]

Machine Type Communications (MTC) is one of the prominent solutions to enable the Internet of Things (IoT). With a large number of IoT applications envisioned over the cellular network, the Third Generation Partnership Project (3GPP) has initiated the support for MTC in the Long Term Evolution (LTE)/ LTE-Advanced (LTE-A) standards. A significant portion of the MTC devices is expected to be low-complexity and low-power User Equipment (UE), requiring an energy efficient mode of operation. In addition, many such UEs can be located in the regions of low network coverage. In this paper, we show that an accurate estimation and compensation of the residual carrier frequency offset (CFO) at the base-station (eNB) results in a reduction in energy consumption for MTC devices in low coverage. For robust and accurate CFO estimation in low coverage, we propose a Maximum Likelihood (ML) based CFO estimation technique that works for data and/or pilot repetitions in LTE/LTE-A uplink. Through simulations, we illustrate that our technique shows a significant performance improvement over the conventional CFO estimation technique using the phase angle of the correlation between the repeated data. We determine that residual CFO estimation and compensation at the eNB results in 22.5%-55.2% reduction in energy consumption of the MTC devices, when compared to the case without CFO compensation.

Symbol timing offset (STO) estimation constitutes an important part in orthogonal frequency-division multiplexing (OFDM) signal synchronization, and many blind methods based on the maximum likelihood (ML) principle have been proposed. A theoretical analysis of the estimation performance, however, is very difficult, due in part to the discrete nature of the STO. As a result, most studies on OFDM STO estimation have relied only on computer simulation for performance evaluation. This paper presents a theoretical performance analysis for ML-based blind OFDM STO estimation. We start with a review of the formulation of ML blind STO estimation problem and its solution. We then conduct the said performance analysis. The analysis turns out to involve a study of the eigenvalues of some matrix functions of the autocorrelation of the received signal. As the general case leads to highly complicated mathematics, we treat the case of full-band OFDM in additive white Gaussian noise in more detail. In this case, we are able to obtain mathematically tractable bounds and approximation to the estimation error probability and the corresponding mean-square error. In high signal-to-noise ratio (SNR), these bounds and approximation show inverse power-law dependence on the SNR. We also compare the bounds with simulation results and with an existing approximation in the literature. [ABSTRACT FROM AUTHOR]

This paper proposes a novel carrier frequency offset (CFO) estimator for the coherent optical orthogonal frequency-division multiplexing (CO-OFDM) systems using the matched-filter (MF) concept. We consider an OFDM symbol with $N$ subcarriers so that the receiver takes $N$ time samples of the received noisy CO-OFDM signal. Our idea is to first append these $N$ received samples with $(L-1)N$ zeros at the end to get a sequence of $LN$ time samples. By taking the discrete Fourier transform of these $LN$ samples, we compute the spectrum of the CO-OFDM symbol at $LN$ discrete frequency points. We think of this spectrum as being made up of $L$ sets of $N$ points each, each set consisting of the frequency points $\lbrace \frac{2\pi }{LN}l, \frac{2\pi }{LN}l+\frac{2\pi }{N}, \ldots, \frac{2\pi }{LN}l+\frac{2\pi (N-1)}{N}\rbrace$ , for each value of $l$ , where $l=0,1,\ldots,L-1$. Each set of $N$ points for one value of $l$ can be considered as the spectrum of the CO-OFDM symbol corresponding to one hypothesized value $\frac{2\pi }{LN}l$ of the CFO. The receiver can now obtain the maximum likelihood (ML) CFO estimate by determining the hypothesized value that best matches the spectrum of the CO-OFDM symbol received from the channel. In the case of data-aided (DA) ML estimation, we treat the OFDM symbol as a pilot symbol in which we know the data in all the subcarriers, and this pilot symbol is used as the reference signal for the MF. In the case of blind (BL) ML estimation, the data in all the subcarriers are unknown, and the receiver uses an OFDM symbol with no data modulation in all the subcarriers as the reference signal. After making the ML decision of the hypothesized value of the CFO, a fine search will be carried out to determine the actual value more accurately. It turns out only one step is necessary to locate the actual value of the CFO. The DA MF ML algorithm can achieve full-range (integral and fractional part) CFO estimation with high accuracy, while the BL MF ML algorithm has low computational complexity but focuses on the fractional CFO estimation only. We discuss the complexity and performance of this MF ML algorithm, and present simulation results to demonstrate the estimation error variance for 16-PSK/QAM modulations. The simulation results show that the estimation error variance of the DA MF ML algorithm can achieve the Cramér–Rao lower bound in the absence of the laser phase noise. [ABSTRACT FROM AUTHOR]

In this paper, an iterative maximum likelihood (ML) finite impulse response (FIR) filter is proposed for discrete-time state estimation in dynamic mechanical systems with better robustness than the Kalman filter (KF). The ML FIR filter and the error covariance matrix are derived in batch forms and further represented with fast iterative algorithms to have a clearer insight into the ML FIR filter performance. Provided that all of the model parameters are known, the ML FIR filter has an intermediate accuracy between the robust unbiased FIR (UFIR) filter and the KF. Under the uncertainties in not exactly known noisy environments, the ML FIR filter performs much better than the KF. A fundamental feature of the ML FIR estimate is that it develops gradually from the UFIR estimate on small horizons to the KF estimate on large horizons. Properties of the ML FIR filter are learned in more detail based on the drifting stochastic resonator and rotary flexible joint. [ABSTRACT FROM AUTHOR]

Focusing on every individual electric vehicle (EV) while optimally charging a significant number of EV units at the workplace is normally computationally burdensome. Such charging optimization requires not only a long runtime but also a large CPU memory due to numerous decision variables involved. This paper develops a new combined state of charge (SOC) based methodology to calculate day-ahead combined probabilistic charging loads for a large number of EV units. Here, several models are proposed to estimate different combined statistical parameters based on historical data. The proposed methodology determines the transition of the combined SOC distribution of EV units from one timeslot to the next using these estimated parameters. Various strategies of SOC-based charging (e.g., unfair and fair modes) are investigated to control EV loads. Numerical results show that the proposed SOC-based charging can reduce the number of decision variables significantly, and require less computational time and memory accordingly. [ABSTRACT FROM PUBLISHER]

This paper addresses the self-interference (SI) cancellation for full-duplex operation in the presence of imperfect radio-frequency (RF) components. In particular, we develop a new scheme to jointly estimate and cancel the in-phase/quadrature mixer imbalance, power amplifier nonlinearities, up-/down-conversion phase noise, and the SI channel. First, we develop a detailed baseband model that captures the most significant transceiver RF imperfections, for both separate- and common-oscillator structures used in the up- and down-conversions. Then, a basis expansion model is derived to approximate the time-varying phase noise and to transform the problem of estimating the time-varying phase noise into the estimation of a set of time-invariant coefficients. Subsequently, the likelihood function is derived in the presence of the unknown intended signal to formulate the joint estimation of the intended channel, SI channel, nonlinear impairments, and phase noise, under the maximum likelihood (ML) criterion. An iterative procedure is developed to find the ML estimate of the different parameters based on the known transmitted data, the known pilot symbols, and the statistics of the unknown intended signal received from the intended transmitter. The full use of the received signal significantly reduces the required number of pilot symbols as compared to training-based techniques. We consider the two pilot-insertion structures used in LTE for the frequency-multiplexed pilots and the time-multiplexed pilots. Simulation results indicate that the proposed ML algorithms can offer a superior SI-cancellation performance with the resulting intended-signal-to-SI-and-noise ratio very close to the intended-signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

In this letter, we propose a reduced-complexity optimal modified sphere decoding (MSD) detection scheme for sparse code multiple access (SCMA). As SCMA systems are characterized by a number of resource elements that are less than the number of the supported users, the channel matrix is rank-deficient, and SD cannot be directly applied. Inspired by the Tikhonov regularization, we formulate a new full-rank detection problem that it is equivalent to the original rank-deficient detection problem for constellation points with constant modulus and an important subset of non-constant modulus constellations. By exploiting the SCMA structure, the computational complexity of MSD is reduced compared with the conventional SD. We also employ list MSD to facilitate channel coding. Simulation results demonstrate that in uncoded SCMA systems, the proposed MSD achieves the performance of the optimal maximum likelihood detection. Additionally, the proposed MSD benefits from a lower average complexity compared with message passing algorithm. [ABSTRACT FROM PUBLISHER]

Some practical systems such as blast furnaces and turbofan engines have degradation processes with memory effects. The term of memory effects implies that the future states of the degradation processes depend on both the current state and the past states because of the interaction with environments. However, most works generally used a memoryless Markovian process to model the degradation processes. To characterize the memory effects in practical systems, we develop a new type of degradation model, in which the diffusion is represented as a fractional Brownian motion (FBM). FBM is actually a special non-Markovian process with long-term dependencies. Based on the monitored data, a Monte Carlo method is used to predict the remaining useful life (RUL). The unknown parameters in the proposed model can be estimated by the maximum likelihood algorithm, and then the distribution of the RUL is predicted. The effectiveness of the proposed model is fully verified by a numerical example and a practical case study. [ABSTRACT FROM PUBLISHER]

In this paper, we address the problem of time delay estimation (TDE) from single-carrier (SC) or multi-carrier (MC) direct-sequence spread spectrum (DSSS) multipath transmissions in the presence of multiple transmit and/or receive antennas that will characterize future 5G radio interface technologies (RITs), such as coded-domain nonorthogonal multiple access. We derive for the first time a closed-form expression for the Cramer-Rao lower bound (CRLB) and develop two maximum likelihood (ML) multipath TDEs for SC DSSS single-input multiple-output (SIMO) in the non-data-aided (NDA) case. The first TDE, based on iterative expectation maximization (EM), provides accurate estimates whenever a good initial guess of the parameters is available at the receiver. The second TDE implements the ML criterion in a non-iterative way and finds the global maximum of the compressed likelihood function using the importance sampling (IS) technique without requiring any initialization. We also extend both the SC DSSS SIMO CRLB and the two new SC DSSS SIMO ML NDA TDEs to MC DSSS RITs and to multiple-input multiple-output structures with any diversity versus multiplexing pre-coding type before generalizing them all to the data-aided (DA) case. Simulations suggest that the EM TDE is suitable for large observation in space, time, and/or frequency, whereas the IS TDE is preferred in the opposite case of very short data records. Moreover, we show in the NDA case, both analytically and by simulations, that spatial (transmit and receive), temporal, and frequency samples interchangeably have the same impact on estimation accuracy and performance bound regardless of the channel correlation type and amount present in each dimension. Furthermore, we are able to properly cope with such channel correlations that do indeed arise in practice and, hence, become very challenging both in estimation and CRLB derivation in the DA case, but that have been so far overlooked in previous works. [ABSTRACT FROM PUBLISHER]

One application for time-difference-of-arrival (TDOA) estimation is in emitter localization. A signal from an emitter reaching a group of sensors, each in a separate location, will have different arrival times. Finding the TDOAs between the output of pairs of sensors will provide the necessary measurements for the hyperbolic localization of the emitter. When the sensors acquire the signal by compressed sensing (CS), their outputs are reduced dimension linear transformation of the time samples of the signal. This shuffling of the time samples breaks up their time relation. Thus, a cross correlation of the CS output of two sensors cannot determine the TDOA. To apply cross correlation, it is necessary to reconstruct the time samples. This letter proposes an alternative that uses only the coefficients of the discrete Fourier transform (DFT) of the CS samples. It begins with the derivation of the maximum likelihood (ML) equation and the ML estimator. This estimator requires known values of signal and noise powers. Substituting these values by their estimates lead to the approximate ML estimator. The phase of the product of two DFT coefficients from each sensor is proportional to the unknown TDOA. Hence, these coefficients can provide an estimation of the TDOA. Simulation results show that although ML is the best, as expected, all these estimators have very close performance. [ABSTRACT FROM PUBLISHER]

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly). [ABSTRACT FROM AUTHOR]

In this letter, the maximum likelihood (ML) finite-impulse response (FIR) filter is proposed for discrete time-variant state-space models with nonsingular system matrix. The ML FIR filter has the deadbeat property and its form is universal for all known bias constrained FIR filters. By the identity weighting matrix, the ML FIR filter becomes the unbiased FIR filter, which ignores the noise statistics and the initial error statistics. Otherwise, the ML FIR filter is equivalent to the optimal FIR filter with embedded unbiasedness and to the minimum variance unbiased FIR filter. An example of a stochastic resonator demonstrates higher immunity of the ML FIR filter against errors in the imprecisely defined noise statistics than in the kalman filter. [ABSTRACT FROM PUBLISHER]

Generalized frequency-division multiplexing (GFDM) inherits the cyclic-prefixed (CP) block signaling structure from orthogonal frequency-division multiplexing (OFDM). The feasibility of CP-based maximum-likelihood (ML) blind synchronization for GFDM systems, however, has not been assessed in the literature. We find that such synchronization is complicated by the covariance structure of the signal which, due to nonorthogonal pulse shaping, possesses a time-varying signal power and some weak self-induced interference. Neglecting the latter and approximating the former by the average power enable us to obtain an approximate-ML solution based on a recent result on OFDM synchronization in wide-sense stationary uncorrelated scattering (WSSUS) channels. Simulations show that the resulting method for GFDM yields comparable performance, under commonly considered pulse-shaping filters, to its kin for OFDM. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a maximum likelihood method for jointly estimating carrier frequency offset (CFO) and sampling frequency offset (SFO) in receivers for orthogonal frequency division multiplexing signals. The proposed estimator exploits the fact that a single source (such as a crystal oscillator or voltage controlled oscillator) is typically used in modern wireless receivers to provide a reference frequency for both the sampling clock and the radio frequency synthesizers. The proposed CFO estimator is capable of a large CFO estimation range (i.e., more than half of intercarrier spacing) without extending the length of the training sequence. The Crámer–Rao lower bound (CRLB) of the variance of the estimated joint CFO and SFO is derived and compared to the CRLBs of the CFO and SFO estimators in the literature. More importantly, the proposed method is validated on a real-time wireless test bed. [ABSTRACT FROM PUBLISHER]

Multiple access broadcasting (MABC) is a spectral efficient protocol in a two-way relay channel (TWRC). The drawback of this protocol in decode-and-forward (DF) mode is decoding complexity at the relay side. Erroneous decoding at the relay can lead to error propagation that degrades the performance of all relay networks operating in the DF mode. In this paper, a maximum likelihood (ML) detector at the multiple-antenna relay is used, and it is proved that this decoding rule can achieve full diversity order at the multiple access (MAC) phase of the MABC protocol. Then, a new beamforming algorithm named as channel alignment is proposed, and it is shown that it achieves full diversity order. A new relay selection algorithm is proposed, and a tight upper bound for the bit error rate (BER) of this scheme is derived. It is proved that it achieves full diversity order. The analytical results and BER performance of the proposed schemes are investigated and compared through simulations. [ABSTRACT FROM AUTHOR]