Maximum likelihood based framework for second-level adaptive prediction

A study is presented of a maximum likelihood based framework for second-level adaptive prediction which is formed from a group of predictors. It is a natural extension to first-level prediction which is formed directly from a group of pixels. The proposed framework offers a greater degree of freedom for adaptation and tackles the problem of model uncertainty that is inherent in first-level prediction methods. It is shown that the proposed methods of taking the weighted average and the weighted median of a group of predictions are alternative and competitive adaptive image prediction methods. The authors also present an extensive discussion on some related research works and theories, generalisation of proposed methods and some possible ways for further improvement.     

Maximum likelihood decoding of RLL-FEC array codes on partial response channels

A technique for performing maximum likelihood decoding (MLD) of RLL-FEC array codes on partial response channels is introduced. Attention is focused on the (1-D) channel and a (16, 9, 4) code having a maximum runlength constraint of 6. However, the idea is applicable to all partial response channels and RLL-FEC array codes.<>     

Silicon complexity for maximum likelihood MIMO detection using spherical decoding

Multiple-input multiple-output (MIMO) wireless systems increase spectral efficiency by transmitting independent signals on multiple transmit antennas in the same channel bandwidth. The key to using MIMO is in building a receiver that can decorrelate the spatial signatures on the receiver antenna array. Original MIMO detection schemes such as the vertical Bell Labs layered space-time (VBLAST) detector use a ing and cancellation process for detection that is sub-optimal as compared to constrained maximum likelihood (ML) techniques. This paper presents a silicon complexity analysis of ML search techniques for MIMO as applied to the HSDPA extension of UMTS. For MIMO constellations of 4/spl times/4 QPSK or lower, it is possible to perform an exhaustive ML search in today's silicon technologies. When the search complexity exceeds technology limits for high complexity MIMO constellations, it is possible to apply spherical decoding techniques to achieve near-ML performance. The paper presents an architecture for a 4/spl times/4 16QAM MIMO spherical decoder with soft outputs that achieves 38.8 Mb/s over a 5-MHz channel using only approximately 10 mm/sup 2/ in a 0.18-/spl mu/m CMOS process.     

Maximum likelihood soft decoding of binary block codes and decodersfor the Golay codes

Maximum-likelihood soft-decision decoding of linear block codes is addressed. A binary multiple-check generalization of the Wagner rule is presented, and two methods for its implementation, one of which resembles the suboptimal Forney-Chase algorithms, are described. Besides efficient soft decoding of small codes, the generalized rule enables utilization of subspaces of a wide variety, thereby yielding maximum-likelihood decoders with substantially reduced computational complexity for some larger binary codes. More sophisticated choice and exploitation of the structure of both a subspace and the coset representatives are demonstrated for the (24, 12) Golay code, yielding a computational gain factor of about 2 with respect to previous methods. A ternary single-check version of the Wagner rule is applied for efficient soft decoding of the (12, 6) ternary Golay code     

Maximum likelihood decoding of certain Reed - Muller codes (Corresp.)

We present an efficient maximum likelihood decoding algorithm for the punctured binary Reed-Muller code of order(m - 3)and length2^{m} - 1, M geq 3, and we give formulas for the weight distribution of coset leaders of such codes.     

Generalized likelihood ratio detection for fMRI using complex data

The majority of functional magnetic resonance imaging (fMRI) studies obtain functional information using statistical tests based on the magnitude image reconstructions. Recently, a complex correlation (CC) test was proposed based on the complex image data in order to take advantage of phase information in the signal. However, the CC test ignores additional phase information in the baseline component of the data. In this paper, a new detector for fMRI based on a generalized likelihood ratio test (GLRT) is proposed. The GLRT exploits the fact that the fMRI response signal as well as the baseline component of the data share a common phase. Theoretical analysis and Monte Carlo simulation are used to explore the performance of the new detector. At relatively low signal intensities, the GLRT outperforms both the standard magnitude data test and the CC test. At high signal intensities, the GLRT performs as well as the standard magnitude data test and significantly better than the CC test.     

Maximum likelihood decoding of uncoded and coded PSK signalsequences transmitted over Rayleigh flat-fading channels

The problem of maximum likelihood (ML) detection for uncoded and coded M-PSK signals on Rayleigh fading channels is investigated. It is shown that, if the received signal is sampled at baud-rate, a ML receiver employing per-survivor processing can be implemented. The error rate performance of this receiver is evaluated by means of computer simulations and its limitations are discussed. In addition, it is shown that, on a fast fading channel, the error floor in the BER curve can be appreciably lowered if more than one received signal sample per symbol interval is processed by the receiver algorithm, Finally, a sub-optimum two-stage receiver structure for interleaved coded PSK systems is proposed. Its error rate performance is assessed for simple trellis-coded modulation schemes and compared to that provided by other receiver structures     

Maximum likelihood detection and estimation of Bernoulli - Gaussian processes

A new detection algorithm, single most likely replacement (SMLR), for detecting randomly located impulsive events which have Gaussian-distributed amplitudes is presented. This detector is designed for the case of severely overlapping wavelets. Estimation of the probability of events also is consider. Experimental results and comparisons with other detectors, using synthetic data, are provided.     

Maximum likelihood detection for differential unitary space-time modulation with carrier frequency offset

Can conventional differential unitary space time modulation (DUSTM) be applied when there is an unknown carrier frequency offset (CFO)? This paper answers this question affirmatively and derives the necessary maximum likelihood (ML) detection rule. The asymptotic performance of the proposed ML rule is analyzed, leading to a code design criterion for DUSTM by using the modified diversity product. The resulting proposed decision rule is a new differential modulation scheme in both the temporal and spatial domains. Two sub-optimal multiple-symbol decision rules with improved performance are also proposed. For the efficient implementation of these, we derive a modified bound intersection detector (BID), a generalization of the previously derived optimal BID for the conventional DUSTM. The simulation results show that the proposed differential modulation scheme is more robust against CFO drifting than the existing double temporal differential modulation.     

Maximum likelihood sequence detection for intersymbol interference channels: A new upper bound on error probability

Maximum likelihood sequence detection of digital signaling subject to intersymbol interference and additive white Gaussian noise is considered. A new approach to the error probability analysis results in an upper bound which is tighter than the Forney bound. In addition, in the case of infinite-length intersymbol interference a locally convergent bound is shown to hold under fairly general assumptions on the signal autocorrelation.     

Comment on “Maximum likelihood decoding of uncoded and codedPSK signal sequences transmitted over Rayleigh flat-fadingchannels”

This comment points out some papers published before the paper of Vitetta and Taylor (see ibid., vol.43, no.11, p.2750-58, 1995). These early papers have dealt with the general problem of maximum-likelihood sequence estimation of coded/uncoded phase-shift keying (PSK) and quadrature amplitude modulation (QAM) signals in correlated Rician and Rayleigh-fading channels. These publications did not assume use of interleaving or use of optimal codes, designed for interleaved systems. The statistical properties of the multiplicative fading process have been included in the design of the receivers which were proposed, analyzed, and evaluated. The research contributions documented in these papers have also shown for the first time in the open literature the link between conventional detection techniques and the maximum-likelihood detection of signals in this type of fading channels     

Capacity and decoding rules for classes of arbitrarily varyingchannels

The capacity of an arbitrarily varying channel (AVC) is considered for deterministic codes with the average probability of error criterion and, typically, subject to at state constraint. First, sufficient conditions are provided that enable relatively simple decoding rules such as typicality, maximum mutual information, and minimum distance, to attain capacity. Then the (possibly noisy) OR channels and group adder channels are studied in detail. For the former the capacity is explicitly determined and shown to be attainable by minimum-distance decoding. Next, for a large class of addictive AVCs, in addition to providing an intuitively suggestive simplification of the general AVC capacity formula, it is proven that capacity can be attained by a universal decoding rule. Finally, the effect of random state selections on capacity is studied. The merits and limitations of a previous mutual information game approach are also discussed     

Complexity of decoders--I: Classes of decoding rules

Several classes of decoding rules are considered here including block decoding rules, tree decoding rules, and bounded-distance and minimum-distance decoding rules for binary parity-check codes. Under the assumption that these rules are implemented with combinational circuits and sequential machines constructed with AND gates, OR gates, INVERTERS, and binary memory cells, bounds are derived on their complexity. Complexity is measured by the number of logic elements and memory cells, and it is shown that minimum-distance and other decoders for parity-check codes can be realized with complexity proportional to the square of block length, although at the possible expense of a large decoding time. We examine tradeoffs between probability of error and complexity for the several classes of rules.     

Maximum likelihood localization of 2-D patterns in the Gauss-Laguerre transform domain: theoretic framework and preliminary results

Usual approaches to localization, i.e., joint estimation of position, orientation and scale of a bidimensional pattern employ suboptimum techniques based on invariant signatures, which allow for position estimation independent of scale and orientation. In this paper a Maximum Likelihood method for pattern localization working in the Gauss-Laguerre Transform (GLT) domain is presented. The GLT is based on an orthogonal family of Circular Harmonic Functions with specific radial profiles, which permits optimum joint estimation of position and scale/rotation parameters looking at the maxima of a "Gauss-Laguerre Likelihood Map." The Fisher information matrix for any given pattern is given and the theoretical asymptotic accuracy of the parameter estimates is calculated through the Cramer Rao Lower Bound. Application of the ML estimation method is discussed and an example is provided.     

Critical point for maximum likelihood decoding of linear block codes

In this letter, the SNR value at which the error performance curve of a soft decision maximum likelihood decoder reaches the slope corresponding to the code minimum distance is determined for a random code. Based on this value, referred to as the critical point, new insight about soft bounded distance decoding of random-like codes (and particularly Reed-Solomon codes) is provided.     

Combined simplified maximum likelihood and sphere decoding algorithm for MIMO system

In this article, a new system model for sphere decoding （SD） algorithm is introduced. For the 2 × 2 multipleinput multiple-out （MIMO） system, a simplified maximum likelihood （SML） decoding algorithm is proposed based on the new model. The SML algorithm achieves optimal maximum likelihood （ML） performance, and drastically reduces the complexity as compared to the conventional SD algorithm. The improved algorithm is presented by combining the sphere decoding algorithm based on Schnorr-Euchner strategy （SE-SD） with the SML algorithm when the number of transmit antennas exceeds 2. Compared to conventional SD, the proposed algorithm has low complexity especially at low signal to noise ratio （SNR）. It is shown by simulation that the proposed algorithm has performance very close to conventional SD.     

Maximum likelihood detection of PPM signals governed by arbitrarypoint-process plus additive Gaussian noise

The maximum likelihood decision statistic for pulse-position modulated (PPM) signals governed by an arbitrary discrete point process in the presence of additive Gaussian noise is derived. Sufficient conditions are given for determining when the optimum PPM symbol detection strategy is to choose the PPM symbol corresponding to the maximum slot statistic. In particular, it is shown that for the important case of Webb distributed avalanche photodiode output electrons in the presence of Gaussian noise, the optimum decision rule is to choose the largest slot observable     

Maximum likelihood synchronization for OFDM using a pilot symbol:analysis

A performance analysis of algorithms for maximum likelihood estimation of channel parameters for orthogonal frequency-division multiplexing packet detection and synchronization is presented. The detection and synchronization algorithms, which are based on the use of a pilot symbol, are described in Coulson (2001). The performance analyses concentrate on operation in additive white Gaussian noise channels, and the accuracy of these analyses are demonstrated using experimental and simulation data. Methods to extend these analyses to frequency flat multipath fading channels are presented     

Maximum likelihood synchronization for OFDM using a pilot symbol:algorithms

The design of a pilot symbol for orthogonal frequency-division multiplexing packet detection and synchronization is presented. Algorithms are described for packet detection and for synchronization using maximum likelihood estimation of channel frequency offset, phase offset for coherent detection, and subsample timing offset. The efficacy of the detection and synchronization algorithms is demonstrated experimentally     

Maximum likelihood estimation of K distribution parameters for SARdata

The K distribution has proven to be a promising and useful model for backscattering statistics in synthetic aperture radar (SAR) imagery. However, most studies to date have relied on a method of moments technique involving second and fourth moments to estimate the parameters of the K distribution. The variance of these parameter estimates is large in cases where the sample size is small and/or the true distribution of backscattered amplitude is highly non-Rayleigh. The present authors apply a maximum likelihood estimation method directly to the K distribution. They consider the situation for single-look SAR data as well as a simplified model for multilook data. They investigate the accuracy and uncertainties in maximum likelihood parameter estimates as functions of sample size and the parameters themselves. They also compare their results with those from a new method given by Raghavan (1991) and from a nonstandard method of moments technique; maximum likelihood parameter estimates prove to be at least as accurate as those from the other estimators in all cases tested, and are more accurate in most cases. Finally, they compare the simplified multilook model with nominally four-look SAR data acquired by the Jet Propulsion Laboratory AIRSAR over sea ice in the Beaufort Sea during March 1988. They find that the model fits data from both first-year and multiyear ice well and that backscattering statistics from each ice type are moderately non-Rayleigh. They note that the distributions for the data set differ too little between ice types to allow discrimination based on differing distribution parameters