Joint Maximum Likelihood Research Articles

RSS-Based Self-Localization Framework for Future Wireless Networks

The localization is an important asset in all existing and emerging wireless networking solutions since it can extend the radio environmental awareness and assist in providing better network operation. The received signal strength (RSS) based non-Bayesian transmitter localization is especially interesting due to the inherent presence of the RSS observations in all commercial radio devices and the instantaneous estimations without the need for extensive training/learning phases. The existing RSS based localization solutions neglect the problems arising from the inherent sensing network topology uncertainty. The sensors, usually assumed to have a-priori known positions, are often a subject of a previous estimation which propagates errors in the transmitter localization procedure, and, hence, results in significant transmitter localization performance degradation. This paper presents a recently developed generic RSS based joint transmitter/sensors localization framework, founded on the assumption of uncertain topology information. The derived joint maximum likelihood (JML) algorithm simultaneously estimates the transmitter and uncertain sensor positions providing twofold gains: improving the transmitter localization and reducing the network topology uncertainty. The paper broadly evaluates the JML algorithm, emphasizing the substantial localization gains originating from the joint transmitter/sensor position estimation. The results prove up to 85 % sensor position uncertainty reduction with the general system model with multiple transmitters locations and multiple previous estimations of the sensors positions. The paper also derives the theoretical lower bounds of the joint estimation framework, and proves the convergence of the JML algorithm. The presented joint estimation framework is applicable to a variety of wireless networking applications. It can provide self-awareness in future wireless networks and cope with the environment and topology dynamism in wireless ad-hoc networks.

Joint Channel and Frequency Offset Estimation for Oversampled Perfect Reconstruction Filter Bank Transceivers

Recently, DFT-based oversampled perfect reconstruction filter banks (OPRFB), as a special form of filtered multitone, have shown great promises for applications to multicarrier modulation. Still, accurate frequency synchronization and channel equalization are needed for their reliable operation in practical scenarios. In this paper, we first derive a data-aided joint maximum likelihood (ML) estimator of the carrier frequency offset (CFO) and the channel impulse response (CIR) for OPRFB transceiver systems operating over frequency selective fading channels. Then, by exploiting the structural and spectral properties of these systems, we are able to considerably reduce the complexity of the proposed estimator through simplifications of the underlying likelihood function. The Cramer Rao bound on the variance of unbiased CFO and CIR estimators is also derived. The performance of the proposed ML estimator is investigated by means of numerical simulations under realistic conditions with CFO and frequency selective fading channels. The effects of different pilot schemes on the estimation performance for applications over time-invariant and mobile time-varying channels are also examined. The results show that the proposed joint ML estimator exhibits an excellent performance, where it can accurately estimate the unknown CFO and CIR parameters for the various experimental setups under consideration.

A modified minimum divergence estimator: some preliminary results for the Rasch model

Since its introduction, the joint maximum likelihood (JML) has been widely used as an estimation method for Rasch measurement models. As is well known, when the JML method is used, all item and person parame- ters are regarded as unknowns to be estimated. In this paper we focus on some drawbacks of the JML for the Rasch model: viz. i) the occasional non-existence of estimates, and ii) the bias of item parameter estimates. We propose a new estimation method which is based on the Minimum Divergence Estimation approach and consists in appropriately modifying the empirical distribution function. We provide empirical evidence that this method can solve the problem of the non-existence of the estimates and, at the same time, can reduce the bias of item parameter estimates compared to those obtained with both traditional JML estimation and the ( k − 1) /k correction factor (where k is the number of items) commonly applied in JML software.

Joint maximum likelihood estimation of activation and Hemodynamic Response Function for fMRI.

Blood Oxygen Level Dependent (BOLD) functional magnetic resonance imaging (fMRI) maps the brain activity by measuring blood oxygenation level, which is related to brain activity via a temporal impulse response function known as the Hemodynamic Response Function (HRF). The HRF varies from subject to subject and within areas of the brain, therefore a knowledge of HRF is necessary for accurately computing voxel activations. Conversely a knowledge of active voxels is highly beneficial for estimating the HRF. This work presents a joint maximum likelihood estimation of HRF and activation based on low-rank matrix approximations operating on regions of interest (ROI). Since each ROI has limited data, a smoothing constraint on the HRF is employed via Tikhonov regularization. The method is analyzed under both white noise and colored noise. Experiments with synthetic data show that accurate estimation of the HRF is possible with this method without prior assumptions on the exact shape of the HRF. Further experiments involving real fMRI experiments with auditory stimuli are used to validate the proposed method.

SPEAR: Source Position Estimation for Anchor Position Uncertainty Reduction

This letter introduces an RSS-based framework (termed Source Position Estimation for Anchor position uncertainty Reduction - SPEAR) for joint estimation of the positions of a wireless transmitter source and the corresponding measuring anchors. The framework exploits the imprecise anchor position information using non-Bayesian estimation and employs a novel Joint Maximum Likelihood (JML) algorithm for reliable anchor and agent position estimations. It proposes to use the iterative Trust Region (TR) strategy as a solution to the JML nonlinear minimization problem. Simulation results show that the JML results in source localization improvements (compared to the ML that ignores the anchor position uncertainty) and provides a more reliable anchors? positions estimates.

Does the Identity of the Third-Party Payer Matter for Prescribing Doctors?

TNF-alpha inhibitors represent one of the most important areas of biopharmaceuticals by sales, with three blockbusters accounting for 8 per cent of total pharmaceutical sale in Norway. Novelty of the paper is to examine, with the use of a unique natural policy experiment in Norway, to what extent the price responsiveness of prescription choices is affected when the identity of the third-party payer changes. The three dominating drugs in this market, Enbrel, Remicade, and Humira, are substitutes, but have had different and varying funding schemes - hospitals and the national insurance plan. A stochastic structural model for the three drugs, covering demand and price setting, is estimated in a joint maximum likelihood approach. We find that doctors are more responsive when the costs are covered by the hospitals compared to when costs are covered by national insurance.

Joint maximum likelihood time-delay estimation for LTE positioning in multipath channels

This paper presents a joint time-delay and channel estimator to assess the achievable positioning performance of the Long Term Evolution (LTE) system in multipath channels. LTE is a promising technology for localization in urban and indoor scenarios, but its performance is degraded due to the effect of multipath. In those challenging environments, LTE pilot signals are of special interest because they can be used to estimate the multipath channel and counteract its effect. For this purpose, a channel estimation model based on equi-spaced taps is combined with the time-delay estimation, leading to a low-complexity estimator. This model is enhanced with a novel channel parameterization able to characterize close-in multipath, by introducing an arbitrary tap with variable position between the first two equi-spaced taps. This new hybrid approach is adopted in the joint maximum likelihood (JML) time-delay estimator to improve the ranging performance in the presence of short-delay multipath. The JML estimator is then compared with the conventional correlation-based estimator in usual LTE conditions. These conditions are characterized by the extended typical urban (ETU) multipath channel model, additive white Gaussian noise (AWGN) and LTE signal bandwidths equal to 1.4, 5 and 10 MHz. The resulting time-delay estimation performance is assessed by computing the cumulative density function (CDF) of the errors in the absence of noise and the root-mean-square error (RMSE) and bias for signal-to-noise ratio (SNR) values between −20 and 30 dB.

Iterative Joint Detection, ICI Cancelation and Estimation of Multiple CFOs and Channels for DVB-T2 in MISO Transmission Mode

When DVB-T2 uses the option of (distributed) multiple-input single-output transmission mode, a pair of Alamouti-encoded orthogonal frequency division multiplex signals is transmitted simultaneously from two spatially separated transmitters in a single frequency network. Since both transmitters have their own local oscillators, two distinct carrier frequency offsets (CFOs), one for each transmitter-receiver link, occur in the received signal due to the frequency mismatch between the local oscillators. Unfortunately, the multiple CFOs cannot be compensated simultaneously by merely adjusting the carrier frequency at the receiver side, so intercarrier interference (ICI) always exists. In this paper, we present an iterative receiver design to combat multiple CFOs for DVB-T2 application. To estimate both multiple CFOs and channels jointly without sacrificing the spectral efficiency, the joint maximum likelihood (ML) estimation based on the soft information from the channel decoder is used. The proposed receiver performs the iterative joint processing of the data detection, ICI cancelation, and joint ML estimation of the multiple CFOs and channels by exchanging the soft information. We also show that the complexity of the joint ML estimation can be reduced significantly by transforming a huge matrix pseudo-inversion into two sub-matrix pseudo-inversions. The performances are evaluated via a full DVB-T2 simulator. The numerical results show that the mean-squared error performance of the joint ML estimation is closely matched with the Cramér-Rao bound. Furthermore, the resulting bit error rate performance is enhanced in a progressive manner and is able to approach the ideal CFOs-free performance within a few iterations.

Maximum Likelihood Frequency Estimation and Preamble Identification in OFDMA-based WiMAX Systems

In multi-cellular WiMAX systems based on orthogonal frequency-division multiple-access (OFDMA), the training preamble is chosen from a set of known sequences so as to univocally identify the transmitting base station. Therefore, in addition to timing and frequency synchronization, preamble index identification is another fundamental task that a mobile terminal must successfully complete before establishing a communication link with the base station. In this work we investigate the joint maximum likelihood (ML) estimation of the carrier frequency offset (CFO) and preamble index in a multicarrier system compliant with the WiMAX specifications, and derive a novel expression of the relevant Cramer-Rao bound (CRB). Since the exact ML solution is prohibitively complex in its general formulation, suboptimal algorithms are developed which can provide a reasonable trade-off between estimation accuracy and processing load. Specifically, we show that the fractional CFO can be recovered by combining the ML estimator with an existing algorithm that attains the CRB in all practical scenarios. The integral CFO and preamble index are subsequently retrieved by a suitable approximation of their joint ML estimator. Compared to existing alternatives, the resulting scheme exhibits improved accuracy and reduced sensitivity to residual timing errors. The price for these advantages is a certain increase of the system complexity.

Low complexity metaheuristics for joint ML estimation problems

Joint maximum likelihood (ML) estimation of multiple parameters is an important problem with wide-spread relevance in many domains. The high computational complexity involved in joint ML problems has led to the search for more efficient methods. Efficient heuristic algorithms for joint ML problems can be developed by exploiting the characteristics of the objective functions used in the estimation problem. This paper proposes a novel reformulation of existing heuristic algorithms, which considerably reduces their computational complexity with significant improvement in performance. The method is applicable for joint maximum likelihood estimation problems, with cost functions that exhibit asymptotic separability with increase in observation vector size. The proposed method is adopted to five recently discovered heuristic algorithms and consequently applied to a relevant recent signal processing problem in wireless communication. It is found that the reformulated algorithms deliver both reduced computational complexity as well as better mean square error (MSE) performance. The significant features of the proposed method are substantiated through extensive computer simulation studies.

On the Properties of the Likelihood Function of Spanos' Conditional t Heteroskedastic Model

This article investigates the properties of the likelihood function of Spanos’ conditional t heteroskedastic model (Spanos, 1994) On modeling heteroskedasticity: the student's t and elliptical linear regression models. It is shown that estimability of the degrees of freedom of t distribution and the block-diagonality of the information matrix of the joint likelihood function with respect to conditional mean parameters and remaining parameters hold for the model. The joint maximum likelihood estimator and its inference based on the t-statistic and χ2-statistic are examined in finite samples by simulation when the degrees of freedom is known and unknown.

Low-Complexity Detection Scheme for Cooperative MIMO Systems with Decode-and-Forward Relays

In this paper, we study low-complexity detection schemes for cooperative multiple-input multiple-output (MIMO) systems with decode-and-forward (DF) relays. In fixed DF protocols, the error propagation effect that limits the diversity gain can be overcome by using joint maximum-likelihood (JML) detection, but its complexity increases exponentially with the number of streams and modulation orders. To develop a low-complexity detection scheme, we first extend the cooperative maximum-ratio combining (C-MRC) scheme to MIMO relay networks, and then investigate the diversity order of the system. We show that C-MRC achieves full diversity gain for orthogonal space-time code, but its diversity gain is limited when the deployed space-time code is non-orthogonal. To address this problem, we propose an effective detection scheme that is applicable to arbitrary space-time codes. On the basis of pairwise error probability approximation, max-log approximation, and the Chernoff bound, we construct an equivalent point-to-point MIMO signal model, and then apply tree-search detection techniques. Compared to JML, the proposed detection scheme significantly reduces the detection complexity while preserving diversity gain. Moreover, it can be readily extended to complex-field network-coding systems whose performance is superior to that of Galois-field network-coding systems. Analysis and simulation results confirm the efficiency of the proposed scheme.

Complexity Reduction in Joint Decoding of Block Coded Signals in Overloaded MIMO-OFDM System

This paper presents a low complexity joint decoding scheme of block coded signals in an overloaded multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. In previous literature, a joint maximum likelihood decoding scheme of block coded signals has been evaluated through theoretical analysis. The diversity gain with block coding prevents the performance degradation induced by signal multiplexing. However, the computational complexity of the joint decoding scheme increases exponentially with the number of multiplexed signal streams. Thus, this paper proposes a two step joint decoding scheme for block coded signals. The first step of the proposed scheme calculates metrics to reduce the number of the candidate codewords using decoding based on joint maximum likelihood symbol detection. The second step of the proposed scheme carries out joint decoding on the reduced candidate codewords. It is shown that the proposed scheme reduces the complexity by about 1/174 for 4 signal stream transmission.

Robust carrier frequency offset and channel estimation for orthogonal frequency division multiple access downlink systems in the presence of severe adjacent‐cell interference

This study investigates joint estimation carrier frequency offset (CFO) and channel impulse response (CIR) in the problematic orthogonal frequency division multiple access (OFDMA) downlink scenario at the cell boundary, where a maximum supposition of three adjacent base stations preambles can be received. A novel scheme is proposed for estimating the CFOs and CIRs of different cells. First, the highly efficient joint maximum-likelihood (JML) algorithm is used to enable robust CFO estimation by exploiting the idempotent property of the projection matrix. Next, in order to estimate the precise CIR, the composite CFO-based and circularly shifted preamble signatures are proposed by applying the constrained minimum variance (CMV) algorithm to suppress adjacent-cell interference. Finally, the CFO and CIR estimations are enhanced by an iterative cancellation scheme. To the best of the authors research, there is no publication study proposed joint JML-CFO and CMV-CIR estimators for OFDMA downlink systems with adjacent-cell interference. Simulation results show that the proposed algorithms provide better performance than the conventional estimators and approach the theoretical Cramer–Rao lower bound at the cell boundary over frequency-selective fading channels.

Strategic or Non-Strategic: The Role of Financial Benefit in Bankruptcy

A partial test for strategic behavior in bankruptcy filing may be formulated by testing whether consumers manipulate their debt and filing decision jointly, or not: that is, testing for endogeneity of financial benefit and the bankruptcy filing decision. Using joint maximum likelihood estimation of an extended discrete choice model, test results are consistent with non-strategic filing: financial benefit is exogenous to the filing decision. This result is confirmed in two different datasets (PSID and SCF). This result is consistent with an ex ante low net gain from a bankruptcy filing; a type of “rational inattention” to rare events such as bankruptcy.

A joint maximum likelihood algorithm for simultaneous track correlation and sensorbias estimation

In this study a joint maximum likelihood (JML) algorithm was developed to solve problems regarding interdependent and contradictory relationships between track correlation and sensor bias estimation in multi-sensor information fusion systems. First, the relationships between track correlation and sensor bias estimation of a multi-sensor system were analyzed. Then, based on these relationships, a JML function of the track correlation and sensor bias estimation was developed, while an iterative two-step optimization procedure was adopted to solve the JML function. In addition, transformation of sensor bias from Cartesian coordinates to polar coordinates and a complete design of track quality and ambiguity processing were provided. Finally, several Monte Carlo simulations were built to test the effect of target density and different sensor bias in the JML algorithm. Simulation results showed that the JML algorithm presented in this paper had a higher correct correlation rate and more accurate sensor bias estimation than traditional methods, demonstrating that the JML algorithm had good performance.

High reliability of real-time visual data transmission using superposition coding with receiver diversity

Supporting visual data applications in the real-time communication systems are among the most challenging issues over the next generation wireless communication systems. This challenge is further magnified by the fact that the quality of reception is highly sensitive to transmission delay, data losses and bit error rate (BER) in such applications. In this paper, we proposed Superposition Coding with Receiver Diversity (SPC-RD) scheme, which employs unequal error protection (UEP) to improve the error performance, maximize the received signal to noise ratio (SNR) and optimize the reliability of the transmission system. In the transmitter side, the visual data is divided into a number of different priority layers based on their effects on the reception quality. These layers are modulated individually where the highest priority layer is modulated with the highest UEP level against error-prone channels, and vice versa. These modulated signals are then superimposed together and transmitted via wireless Single-Input Multiple-Output (SIMO) Rayleigh fading channel. In the receiver side, three different diversity combining approaches; selection combining (SC), equal gain combining (EGC) and maximal ratio combining (MRC) are considered. The combined signal is then passed through a multiuser demodulator so-called the ordered successive interference cancellation (O-SIC) demodulator to reconstruct and separate the data layers. This demodulation technique is evaluated and compared with the traditionally maximum likelihood joint detection (MLJD) technique. Extensive simulations have been carried out to validate the various assertions. Under the assumption of equal transmission power, the simulation results illustrate that the proposed SPC-RD scheme provides a SNR gain of 14.5 dB over the Rayleigh fading channel at the diversity order of three for the acceptable BER level of 10−3 when BPSK scheme is exploited compared to the traditional equal error protection system. In addition, the proposed scheme with O-SIC demodulation technique achieves almost similar performance compared to MLJD technique but using less computational complexity.

Integrated track initialization and maintenance in heavy clutter using probabilistic data association

Target tracking in high clutter or low signal-to-noise ratio (SNR) environments is an important topic and still a challenging task. Joint Maximum Likelihood Probabilistic Data Association (JML-PDA) is a well-known batch method for initializing the tracks of very low observable (VLO) targets in heavy clutter environments. On the other hand, the Joint Probabilistic Data Association (JPDA) algorithm, which is commonly used for recursive track maintenance, lacks track initialization capability. In this paper, we propose a Combined JML-PDA and JPDA (CJML-PDA) algorithm to automatically initialize and maintain the tracks. This combined approach seamlessly shares information between the initialization and maintenance stages of the tracker. In contrast, in other batch-recursive approaches the initialization and maintenance algorithms operate rather independent of each other. The effectiveness of the proposed algorithm is demonstrated on a heavy clutter scenario and its performance is tested on closely-spaced (but resolved) targets with association ambiguity using angle-only measurements.

Successive-Relaying-Aided Decode-and-Forward Coherent Versus Noncoherent Cooperative Multicarrier Space–Time Shift Keying

Successive-relaying-aided (SR) cooperative multicarrier (MC) space-time shift keying (STSK) is proposed for frequency-selective channels. We invoke SR to mitigate the typical 50% throughput loss of conventional half-duplex relaying schemes and MC code-division multiple access (MC-CDMA) to circumvent the dispersive effects of wireless channels and to reduce the SR-induced interference. The distributed relay terminals form two virtual antenna arrays (VAAs), and the source node (SN) successively transmits frequency-domain (FD) spread signals to one of the VAAs, in addition to directly transmitting to the destination node (DN). The constituent relay nodes (RNs) of each VAA activate cyclic-redundancy-checking-based (CRC) selective decode-and-forward (DF) relaying. The DN can jointly detect the signals received via the SN-to-DN and VAA-to-DN links using a low-complexity single-stream-based joint maximum-likelihood (ML) detector. We also propose a differentially encoded cooperative MC-CDMA STSK scheme to facilitate communications over hostile dispersive channels without requiring channel estimation (CE). Dispensing with CE is important since the relays cannot be expected to altruistically estimate the SN-to-RN links for simply supporting the source. Furthermore, we propose soft-decision-aided serially concatenated recursive systematic convolutional (RSC) and unity-rate-coded (URC) cooperative MC STSK and investigate its performance in both coherent and noncoherent scenarios.

Using Neural Network Analysis to Define Methods of DINA Model Estimation for Small Sample Sizes

The DINA model is a commonly used model for obtaining diagnostic information. Like many other Diagnostic Classification Models (DCMs), it can require a large sample size to obtain reliable item and examinee parameter estimation. Neural Network (NN) analysis is a classification method that uses a training dataset for calibration. As a result, if this training dataset is determined theoretically, as was the case in Gierl’s attribute hierarchical method (AHM), the NN analysis does not have any sample size requirements. However, a NN approach does not provide traditional item parameters of a DCM or allow for item responses to influence test calibration. In this paper, the NN approach will be implemented for the DINA model estimation to explore its effectiveness as a classification method beyond its use in AHM. The accuracy of the NN approach across different sample sizes, item quality and Q-matrix complexity is described in the DINA model context. Then, a Markov Chain Monte Carlo (MCMC) estimation algorithm and Joint Maximum Likelihood Estimation is used to extend the NN approach so that item parameters associated with the DINA model are obtained while allowing examinee responses to influence the test calibration. The results derived by the NN, the combination of MCMC and NN (NN MCMC) and the combination of JMLE and NN are compared with that of the well-established Hierarchical MCMC procedure and JMLE with a uniform prior on the attribute profile to illustrate their strength and weakness.