To assess the performance of maximum-likelihood (ML) based Nakagami m parameter estimators, current methods rely on Monte Carlo simulation. In order to enable the analytical performance evaluation of ML-based m parameter estimators, we study the statistical properties of a parameter Δ, which is defined as the log-ratio of the arithmetic mean to the geometric mean for Nakagami-m fading power. Closed-form expressions are derived for the probability density function (PDF) of Δ. It is found that for large sample size, the PDF of Δ can be well approximated by a two-parameter Gamma PDF.

In this paper, we present a maximum a posteriori probability (MAP) approach to the problem of blind estimation of single-input, multiple-output (SIMO), finite impulse response (FIR) channels. A number of methods have been developed to date for this blind estimation problem. Some of those utilize prior knowledge on input signal statistics. However, there are very few that utilize channel statistics too. In this paper, the unknown channel to be estimated is assumed as the frequency-selective Rayleigh fading channel, and we incorporate the channel prior distributions (and hyperprior distributions) into our model in two different ways. Then for each case an iterative MAP estimator is derived approximately. Performance comparisons over existing methods are conducted via numerical simulation on randomly generated channel coefficients according to the Rayleigh fading channel model. It is shown that improved estimation performance can be achieved through the MAP approaches, especially for such channel realizations that have resulted in large estimation error with existing methods.

In non-cooperative scenarios, the estimation of direct sequence spread spectrum (DS-SS) signals has to be done in a blind manner. In this letter, we consider the spreading sequence estimation problem for DS-SS signals. First, the maximum likelihood estimate (MLE) of spreading sequence is derived, then a semidefinite relaxation (SDR) approach is proposed to cope with the exponential complexity of performing MLE. Simulation results demonstrate that the proposed approach provides significant performance improvements compared to existing methods, especially in the case of low numbers of data samples and low signal-to-noise ratio (SNR) situations.

For an indoor area where a target node is tracked with anchor nodes, we can calculate the priori probability density functions (pdfs) on the distances between the target and anchor nodes by using its shape, three-dimensional sizes and anchor nodes locations. We call it “the area layout information (ALI)” and apply it for two indoor target tracking methods with received signal strength indication (RSSI) assuming a square location estimation area. First, we introduce the ALI to a target tracking method which tracks a target using the weighted sum of its past-to-present locations by a simple infinite impulse response (IIR) low pass filter. Second, we show that the ALI is applicable to a target tracking method with a particle filter where the motion of the target is nonlinearly modelled. The performances of the two tracking methods are evaluated by not only computer simulations but also experiments. The results demonstrate that the use of ALI can successfully improve the location estimation performance of both target tracking methods, without huge increase of computational complexity.

In this paper, we develop linear-programming (LP) decoding for multiple-access channels with binary linear codes. For single-user channels, LP decoding has attracted much attention in recent years as a good approximation to maximum-likelihood (ML) decoding. We demonstrate how the ML decoding problem for multiple-access channels with binary linear codes can be formulated as an LP problem. This is not straightforward, because the objective function of the problem is generally a non-linear function of the codeword symbols. We introduce auxiliary variables such that the objective function is a linear function of these variables. The ML decoding problem then reduces to the LP problem. As in the case for single-user channels, we formulate the relaxed LP problem to reduce the complexity for practical implementation, and as a result propose a decoder that has the desirable property known as the ML certificate property (i.e., if the decoder outputs an integer solution, the solution is guaranteed to be the ML codeword). Although the computational complexity of the proposed algorithm is exponential in the number of users, we can reduce this complexity for Gaussian multiple-access channels. Furthermore, we compare the performance of the proposed decoder with a decoder based on the sum-product algorithm.

Repetitive synchronization sequences in the time domain can be used to estimate Carrier Frequency Offset (CFO). The Un-Guarded Maximum Likelihood (UGML) estimator and Guarded ML (GML) estimator of CFO in the frequency selective channel are proposed in this paper. The results of theoretical analysis show that the UGML estimator is hard to implement if the channel response is not known while the GML estimator can be easily implemented due to inserted guard sequences. The guard sequences can be easily implemented as Cyclic Prefix (CP) in OFDM system. Therefore, the UGML estimator is only suitable for the systems where the channel response can be predetermined. This paper also gives a comparison with the existing CFO estimator. Theoretical and simulation results show that both the proposed estimators outperform the existing estimator.

In order to realize multi-service in TDS-OFDM system, a novel multiplexing scheme based on space time block code is proposed along with the corresponding demultiplexing method with low complexity. Simulations show the presented scheme can not only achieve full diversity gain, but also effectively improve the system capacity.

We create an efficient statistical pruning (SP) algorithm for fixed-complexity sphere decoder (FSD) by utilizing partial decision feedback detection (i.e., SP-FSD). Simulation results show that SP-FSD not only attains the near-optimal performance, but also achieves much lower complexity than the original FSD and its two lately-developed variants: the simplified FSD (SFSD) and the statistical threshold-based FSD (ST-FSD).

A low peak-to-average-power-ratio (PAPR) transceiver with a time-frequency cyclic shift orthogonal keying (TF-CSOK) technique is proposed for the uplink multi-carrier CDMA (MC-CDMA) system over multiple access interference (MAI) and multipath interference (MPI) channels. The low complexity structure of the TF-CSOK MC-CDMA system is designed to involve the FCSOK and TCSOK techniques to combat MPI and MAI effects, respectively. In particular, at the besestation, the multiuser detector employs the maximum likelihood (ML) rule and the TFSOK despreading and demapping techniques to acquire the M-ary modulation gain and diversity gain. Simulation results show that the multuser receiver has the robustness against strong MAI. Moreover, it outperforms the conventional single-carrier frequency division multiple access (SC-FDMA) system and the conventional MC-CDMA system under MAI and MPI environments.

This paper addresses the issue of Unconditional or Stochastic Maximum likelihood (SML) estimation of directions-of-arrival (DOA) finding using sensors with arbitrary array configuration. The conventional SML estimation is formulated without an important condition that the covariance matrix of signal components must be non-negative definite. An likelihood function can not be evaluated exactly for all possible sets of directions. First, this paper reveals that the conventional SML has three problems due to the lack of the condition. 1) Solutions in the noise-free case are not unique. 2) Global solution in the noisy case becomes ambiguous occasionally. 3) There exist situations that any local solution does not satisfy the condition of the non-negative definiteness. We propose an exact formulation of the SML estimation of DOA to evaluate an likelihood function exactly for any possible set of directions. The proposed formulation can be utilized without any theoretical difficulty. The three problems of the conventional SML are solved by the proposed exact SML estimation. Furthermore we show a local search technique in the conventional SML has a good chance to find an optimal or suboptimal DOA although the suboptimal solutions violate the condition of the non-negative definiteness. Finally some simulation results are shown to demonstrate good estimation properties of the exact SML estimation.

In this paper, we propose a genetic algorithm (GA) based equalization approach for direct sequence ultra-wideband (DS-UWB) wireless communication systems, where the GA is combined with a RAKE receiver to combat the inter-symbol interference (ISI) due to the frequency selective nature of UWB channels for high data rate transmission. The proposed GA based equalizer outperforms significantly the RAKE and the RAKE-minimum mean square error (MMSE) receivers according to results obtained from intensive simulation work. The RAKE-GA receiver also provides bit-error-rate (BER) performance very close to that of the optimal RAKE-maximum likelihood detection (MLD) approach, while offering a much lower computational complexity.

In this Letter, the maximum likelihood (ML) estimator for the parameters of a real sinusoid in additive white Gaussian noise using irregularly-spaced samples is derived. The ML frequency estimate is first determined by a one-dimensional search, from which optimum amplitude and phase estimates are then computed. It is shown that the estimation performance of the ML method can attain Cramér-Rao lower bound when the signal-to-noise ratio is sufficiently large.

In this paper, we propose a novel noncoherent maximum likelihood detection (NMLD) method for differential spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems. Unlike the conventional maximum likelihood detection (MLD) method which needs the knowledge of channel state information (CSI) at the receiver, NMLD method has no need of CSI at either the transmitter or receiver. After repartitioning the observation block of multiple-symbol differential detection (MSDD) and following a decision feedback process, the decision metric of NMLD is derived by reforming that of MSDD. Since the maximum Doppler frequency and noise power are included in the derived decision metric, estimations of both maximum Doppler frequency and noise power are needed at the receiver for NMLD. A fast calculation algorithm (FCA) is applied to reduce the computational complexity of NMLD. The feasibility of the proposed NMLD is demonstrated by computer simulations in both slow and fast fading environments. Simulation results show that the proposed NMLD has good bit error rate (BER) performance, approaching that of the conventional coherent MLD with the extension of reference symbols interval. It is also proved that the BER performance is not sensitive to the estimation errors in maximum Doppler frequency and noise power.

Many existing software reliability models (SRMs) are based on the assumption that fault correction activities take a negligible amount of time and resources, which is often invalid in real-life situations. Consequently, the estimated and predicted software reliability tends to be over-optimistic, which could in turn mislead management in related decision-makings. In this paper, we first make an in-depth analysis of real-life software testing process; then a Markovian SRM considering fault correction process is proposed. Parameter estimation method and software reliability prediction method are established. A numerical example is given which shows that by using the proposed model and methods, the results obtained tend to be more appropriate and realistic.

In this paper, a novel intra-die spatial correlation extraction method referred to as MLEMTC (Maximum Likelihood Estimation for Multiple Test Chips) is presented. In the MLEMTC method, a joint likelihood function is formulated by multiplying the set of individual likelihood functions for all test chips. This joint likelihood function is then maximized to extract a unique group of parameter values of a single spatial correlation function, which can be used for statistical circuit analysis and design. Moreover, to deal with the purely random component and measurement error contained in measurement data, the spatial correlation function combined with the correlation of white noise is used in the extraction, which significantly improves the accuracy of the extraction results. Furthermore, an LU decomposition based technique is developed to calculate the log-determinant of the positive definite matrix within the likelihood function, which solves the numerical stability problem encountered in the direct calculation. Experimental results have shown that the proposed method is efficient and practical.

For a group of wirelessly networked robots, called "a robot swarm," to accomplish a unified task as a group, it is necessary to generate a set of common coordinates among all member robots and to notify each member robot of its heading direction in the generated common coordinates. However, when the member robots are not equipped with sensors to identify their locations or bearings, they can use only a ranging capability based in the wireless communication protocol being used to network them as a tool to generate a set of common coordinates among them. This paper presents the detailed principles of a method for generating a set of common coordinates/heading direction for a robot swarm with only ranging capability which we have proposed so far. After showing the theoretical Cramer-Rao lower-bound on the location estimation error variance, we demonstrate several computer simulation results for the proposed method with Received Signal Strength Indication (RSSI)-based ranging.

The situations where several population parameters need to be estimated simultaneously arise frequently in wide areas of applications, including reliability modeling, survival analysis and biological study. In this paper, we propose Bayesian methods of estimation of the ordered parameters of the two exponential populations, which incorporate the prior information about the simple order restriction, but sometimes breaks the order restriction. A simulation study shows that the proposed estimators are more efficient (in terms of mean square errors) than the isotonic regression of the maximum likelihood estimators with equal weights. An illustrative example is finally presented.

An antenna-permutation (AP) scheme is described for channel-vector quantization (CVQ) in zero-forcing beamforming (ZFBF) multiuser multiple-input and multiple-output orthogonal frequency-division multiplexing systems with multiple receive antennas. Different sets of multiple channel sub-matrices are selected for different subcarriers and then quantized to multiple quantization vectors for finite rate feedback. Based on the quantization vectors, ZFBF provides a single stream or multiple streams to users while increasing frequency selectivity. Simulation results demonstrate that AP-CVQ with four-bit quantization that incorporates with pre-whitening maximum likelihood detection for two stream reception achieved better average packet error rates than minimum mean square error receive beamforming for single stream reception when the frequency selectivity was not severe.

In this letter, we propose a new multi-step maximum likelihood predictor with a finite impulse response (FIR) structure for discrete-time state-space signal models. This predictor is called a maximum likelihood FIR predictor (MLFP). The MLFP is linear with the most recent finite outputs and does not require a prior initial state information on a receding horizon. It is shown that the proposed MLFP possesses the unbiasedness property and the deadbeat property. Simulation study illustrates that the proposed MLFP is more robust against uncertainties and faster in convergence than the conventional multi-step Kalman predictor.

This paper proposes an adaptive selection algorithm for the surviving symbol replica candidates (ASESS) based on the maximum reliability in maximum likelihood detection with QR decomposition and the M-algorithm (QRM-MLD) for Orthogonal Frequency Division Multiplexing (OFDM) multiple-input multiple-output (MIMO) multiplexing. In the proposed algorithm, symbol replica candidates newly-added at each stage are ranked for each surviving symbol replica from the previous stage using multiple quadrant detection. Then, branch metrics are calculated only for the minimum number of symbol replica candidates with a high level of reliability using an iterative loop based on symbol ranking results. Computer simulation results show that the computational complexity of the QRM-MLD employing the proposed ASESS algorithm is reduced to approximately 1/4 and 1/1200 compared to that of the original QRM-MLD and that of the conventional MLD with squared Euclidian distance calculations for all symbol replica candidates, respectively, assuming the identical achievable average packet error rate (PER) performance in 4-by-4 MIMO multiplexing with 16QAM data modulation. The results also show that 1-Gbps throughput is achieved at the average received signal energy per bit-to-noise power spectrum density ratio (Eb/N0) per receiver antenna of approximately 9 dB using the ASESS algorithm in QRM-MLD associated with 16QAM modulation and Turbo coding with the coding rate of 8/9 assuming a 100-MHz bandwidth for a 12-path Rayleigh fading channel (root mean square (r.m.s.) delay spread of 0.26 µs and maximum Doppler frequency of 20 Hz).