Adaptive Bayesian decision feedback equalizer for dispersive mobile radio channels

The paper investigates adaptive equalization of time dispersive mobile ratio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE

Multiple hyperplane detector for implementing the asymptotic Bayesian decision feedback equalizer

A detector based on multiple-hyperplane partitioning of the signal space is derived for realizing the Bayesian decision feedback equaliser (DFE). It is known that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal to noise ratio (SNR) tends to infinity. The proposed technique determines these hyperplanes and uses them to partition the observation space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision complexity

Adaptive Least Error Rate Algorithm for Neural Network Classifiers

We consider sample-by-sample adaptive training of two-class neural network classifiers. Specific applications that we have in mind are communication channel equalization and code-division multiple-access (CDMA) multiuser detection. Typically, training of such neural network classifiers is done using some stochastic gradient algorithm that tries to minimize the mean square error (MSE). Since the goal should really be minimizing the error probability, the MSE is a "wrong" criterion to use and may lead to a poor performance. We propose a stochastic gradient adaptive minimum error rate (MER) algorithm called the least error rate (LER) for training neural network classifiers

Improved SAR Imaging Via Cross-Learning from Camera Images

In this paper, we propose a novel concept of cross-learning, in order to improve SAR images by learning from the camera images. We use a multi-level abstraction approach to materialise knowledge transfer between the two modalities. We also compare the performance of other possible approaches. We provide experimental results on real data to validate the proposed concept

Stochastic least-symbol-error-rate adaptive equalization for pulse-amplitude modulation

The paper derives a stochastic-gradient minimum symbol-error-rate (MSER) algorithm, called the least symbol error rate (LSER), for training the linear equalizer and linear-combiner decision feedback equalizer (DFE) with $M$-PAM signalling. This LSER algorithm has some performance advantages, in terms of faster convergence rate and smaller steady-state symbol error rate (SER) misadjustment, over an existing simpler stochastic-gradient adaptive MSER algorithm called the approximate MSER (AMSER)