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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.

Generalized spatial modulation (GSM) technology is an extension of spatial modulation (SM) technology, and one of its main advantages is to further improve band efficiency. However, the multiple active antennas for transmission also brings the demodulation difficulties at the receiver. To solve the problem of high computational complexity of the optimal maximum likelihood (ML) detection, two sub-optimal detection algorithms are proposed through reducing the number of transmit antenna combinations (TACs) detected at the receiver. One is the maximum ratio combining detection algorithm based on repetitive sorting strategy, termed as (MRC-RS), which uses MRC repetitive sorting strategy to select the most likely TACs in detection. The other is the maximum ratio combining detection algorithm, which is based on the iterative idea of the orthogonal matching pursuit, termed the MRC-MP algorithm. The MRC-MP algorithm reduces the number of TACs through finite iterations to reduce the computational complexity. For M-QAM constellation, a hard-limited maximum likelihood (HLML) detection algorithm is introduced to calculate the modulation symbol. For the M-PSK constellation, a low-complexity maximum likelihood (LCML) algorithm is introduced to calculate the modulation symbol. The computational complexity of these two algorithms for calculating the modulation symbol are independent of modulation order. The simulation results show that for GSM systems with a large number of TACs, the proposed two algorithms not only achieve almost the same bit error rate (BER) performance as the ML algorithm, but also can greatly reduce the computational complexity.

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]

This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results. [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]

The problem of direction-of-arrival (DOA) estimation is important in array signal processing. To estimate the DOAs of uncorrelated, partly correlated and coherent signals, a new iterative DOA estimation algorithm, named AOP-DOA, is proposed by using alternating oblique projection (AOP). In each iteration, the oblique projection approach is employed to separate the received signals, then the DOA of each separated signal is estimated one after another. After theoretical analysis on the relationship between the proposed AOP-DOA and the conventional alternating projection based maximum likelihood estimator (AP-MLE), an AOP&AP-DOA algorithm, which is a combination of AOP-DOA and AP-MLE, is developed to reduce the computational complexity of AOP-DOA. Extensive experiments validate the effectiveness and complexity of the proposed two algorithms. Particularly, AOP&AP-DOA keeps the merits of AOP-DOA, but exhibits superiority over AOP-DOA in terms of computational complexity when proper adaptive grid refinement strategy is applied.

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]

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a recently proposed multicarrier transmission technique, which transmits distinguishable constellations (modes) according to their full permutations. For an uncoded system, the maximum-likelihood (ML) detector achieves the optimal performance, but its computational complexity is extremely high due to a large number of symbol modes. Existing low-complexity detectors may output an illegal mode permutation, which leads to catastrophic errors in the demodulation. redIt is well known that sphere decoding algorithms can reduce the computational complexity effectively and achieve near-ML bit error rate (BER) performance. In this paper, employing a sphere-decoding-like algorithm, we propose a novel detector, which can achieve near-ML performance with considerably low computational complexity. For coded MM-OFDM-IM systems, we propose a simplified log-likelihood ratio (LLR) calculation algorithm to achieve near-optimal coded BER performance with considerably low computational complexity. Computer simulation and numerical results demonstrate that the proposed detectors can achieve outstanding performance at the cost of low computational complexity. [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]

The primary aim of this study is to explore and investigate the maximum likelihood (ML) estimation and the Bayesian approach to estimating the parameters of log-logistic distribution and to calculate the approximate intervals for the parameters and the survival function based on adaptive progressive type-II censored data. The ML estimators of the parameters of the probability distribution were obtained via the Newton–Raphson Method. The approximate confidence intervals for the reliability function were calculated using the delta method. The Bayes estimators based on squared error loss function (SELF) and the approximate credible intervals for the unknown parameters and the survival function using the Bayesian approach were constructed using the Markov Chain Monte Carlo (MCMC) method. A Monte Carlo study was performed to examine the proposed methods under different situations, based on mean-squared error, bias, coverage probability, and expected length-estimated criteria. The Bayesian approach appears to be better than the likelihood for estimating the log-logistic model parameters. An application to real data was included. [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.

We propose an expectation maximization (EM) strategy for improving the precision of time of flight (ToF) light detection and ranging (LiDAR) scanners. The novel algorithm statistically accounts not only for the bias induced by temperature changes in the laser diode, but also for the multi-modality of the measurement noises that is induced by mode-hopping effects. Instrumental to the proposed EM algorithm, we also describe a general thermal dynamics model that can be learned either from just input-output data or from a combination of simple temperature experiments and information from the laser’s datasheet. We test the strategy on a SICK LMS 200 device and improve its average absolute error by a factor of three.

This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results.

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]

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2. [ABSTRACT FROM AUTHOR]

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]

The Generalized Cross Correlation (GCC) framework is one of the most widely used methods for Time Difference Of Arrival (TDOA) estimation and Sound Source Localization (SSL). TDOA estimation using cross correlation without any pre-filtering of the received signals has a large number of errors in real environments. Thus, several filters (weighting functions) have been proposed in the literature to improve the performance of TDOA estimation. These functions aim to mitigate TDOA estimation error in noisy and reverberant environments. Most of these methods consider the noise or reverberation, and as one of them increases, TDOA estimation error increases. In this paper, we propose a new weighting function. This function is a combined and modified version of Maximum Likelihood (ML) and PHAT-/ργ functions. We named our proposed function as Modified Maximum Likelihood with Coherence (MMLC). This function has merits of both ML and PHAT-/ργ functions and can work properly in both noisy and reverberant environments. We evaluate our proposed weighting function using real and synthesized datasets. Simulation results show that our proposed filter has better performance in terms of TDOA estimation error and anomalous estimations. [ABSTRACT FROM AUTHOR]

In this paper, the performance of the Multiple Input Multiple Output (MIMO) technique evaluated in term of Bit Error Rate (BER) with respect to Signal to Noise Ratio (SNR) by using Binary Phase Shift Keying (BPSK) modulation for two algorithms Maximum Likelihood (MLD) and Zero-Forcing (ZF) with different configurations of antennas array in Rayleigh and Additive White Gaussian Noise (AWGN) channels. The results were compared between them to determine which of the numbers antenna elements are suitable in the transmitter and receiver of each algorithm. The results of MLD offers a better configuration when 4×4 and 3×4 antennas array were used, while the ZF remains the same performance for the 2×2, 3×3 and 4×4 configurations. In different numbers of antennas, the best performance of ZF is got when the number of transmitter and receiver antennas are equal to 2×4 respectively. Also, the last one is better than the 4×4 and 3×4 configurations of MLD algorithm.

This study discusses the localization problem based on time delay and Doppler shift for a far-field scenario. The conventional location methods employ two steps that first extract intermediate parameters from the received signals and then determine the source position from the measured parameters. As opposed to the traditional two-step methods, the direct position determination (DPD) methods accomplish the localization in a single step without computing intermediate parameters. However, the DPD cost function often remains non-convex, thereby it will cost a high amount of computational resources to find the estimated position through traversal search. Weiss proposed a DPD estimator to mitigate the computational complexity via eigenvalue decomposition. Unfortunately, when the computational resources are rather limited, Weiss’s method fails to satisfy the timeliness. To solve this problem, this paper develops a DPD estimator using expectation maximization (EM) algorithm based on time delay and Doppler shift. The proposed method starts from choosing the transmitter-receiver range vector as the hidden variable. Then, the cost function is separated and simplified via the hidden variable, accomplishing the transformation from the high dimensional nonlinear search problem into a few one dimensional search subproblems. Finally, the expressions of EM repetition are obtained through Laplace approximation. In addition, we derive the Cramér⁻Rao bound to evaluate the best localization performance in this paper. Simulation results confirm that, on the basis of guaranteeing high accuracy, the proposed algorithm makes a good compromise in localization performance and computational complexity.