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

Displaying red and black on a first date: A field study using the “First Dates” television series

Previous research has shown that displaying the color red can increase attractiveness. As a result, women display red more often when expecting to meet more attractive men in a laboratory context. Here, we carried out a field study by analyzing 546 daters from the “First Dates” television series. Each participant was filmed in a pre-date interview and during a real first date, allowing direct comparison of the clothing worn by each person in these two contexts. Analysis of ratings of the amount of red displayed showed that both men and women wore more red clothing during their dates. This pattern was even stronger for black clothing, while the amount of blue clothing did not differ across the two contexts. Our results provide the first real-world demonstration that people display more red and black clothing when meeting a possible mate for the first time, perhaps seeking to increase their attractiveness and/or reveal their intentions to potential partners

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

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)

Adaptive Minimum-BER Linear Multiuser Detection

An adaptive minimum bit error rate (MBER) linear multiuser detector (MUD) is proposed for DS-CDMA systems. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style adaptive algorithm is developed for training linear MUDs. Computer simulation results show that this adaptive MBER linear MUD outperforms two existing LMS-style adaptive MBER algorithms