The classical Viterbi algorithm (ML sequence estimation) can be computed using a forward-backward structure, similar to that of the classical hidden Markov model forward-backward algorithm (MAP state estimation). This similarity is exploited to develop a hybrid algorithm which provides a mathematical connection between ML sequence estimation and MAP state estimation.

A hidden Markov model method for estimating an a posterior distribution of the amplitude of QAM communications signals is presented. As the signal to noise ratio decreases the hidden Markov model method is shown to perform significantly better than a conventional histogram method for characterising the amplitude distribution. The HMM estimation is performed within a expectation maximisation method in order to improve the estimates of the transition probabilities used in the HMM and the resulting estimated amplitude distribution.

This paper develops a reduced-complexity online state sequence and parameter estimator for superimposed convolutional coded signals. Joint state sequence and parameter estimation is achieved by iteratively estimating the state sequence via a variable reduced-complexity Viterbi algorithm (VRCVA) and the model parameters via a recursive expectation maximization (EM) approach. The VRCVA is developed from a fixed reduced-complexity Viterbi algorithm (FRCVA). The FRCVA is a special case of the delayed decision-feedback sequence estimation (DDFSE) algorithm. The performance of online versions of the FRCVA, VRCVA, and the standard Viterbi algorithm (VA) are compared when they are used to estimate the state sequence as part of the reduced-complexity online state sequence and parameter estimator.

In this paper, a method for simultaneously demodulating and estimating the parameters of a number of convolutional coded communication signals incident on an antenna array is presented. The method has the potential to increase the throughput of current multiple-access channel systems, e.g., satellite communications and digital mobile cellular phones, by using an antenna array. The contribution of this paper is the use of sequence estimation combined jointly with parameter estimation in array processing problems. A hidden Markov-model-based technique, the segmental k-means algorithm, is applied to the problem. This algorithm is an iterative procedure with two steps per iteration. The first step involves computing the most likely state sequence for each of the signals (demodulating the signals) given estimates of the signals' parameters. The second step refines the parameter estimates using the signals' mostly likely state sequence estimates. In the simulations presented, it is shown that a significant improvement in the accuracy of the demodulated signals and in the estimation of the signals' angle of arrivals is obtained when compared to a deterministic maximum likelihood estimation method.

A method of jointly estimating the parameters of a number of superimposed convolutional coded communication signals incident on an antenna array and demodulating these signals is presented. The method would allow simpler arrays to be designed due to the threshold extension obtained by this method. It also has the potential to increase the throughput of current multiple access channel systems, for example, satellite communications and digital mobile cellular phones, by using an antenna array. The concept is based on the use of sequence estimation combined jointly with parameter estimation in array processing problems. In the simulations it is shown that a significant improvement in the accuracy of the demodulated signals and in the estimation of the signals angle of arrivals is obtained compared to a deterministic maximum likelihood estimation method.

A method of determining the constraint length and generating polynomials of rate 1/L convolutional coded data using only the encoded data is presented.>