Channel Equalization Problem Research Articles

Multilayer perceptron based decision feedback equalisers for channels with intersymbol interference

The paper describes the application of multilayer perceptrons to the problem of adaptive channel equalisation in digital communications systems. In particular, the use of decision feedback structures is investigated for time-invariant and time-variant bandlimited channels. Simulation results show that the equaliser based on the multilayer perceptron provides better bit error rate performance compared with the conventional decision feedback equaliser if the equaliser length corresponds to the time spread of the channel. Increasing the equaliser length leads to equivalent bit error rates for the decision feedback and the multilayer perceptron based equaliser. Because of the more complex structure of the multilayer perceptron its adaptive behaviour for time-variant channels is inferior compared with the decision feedback equaliser. Nonlinearities on the transmission channel can be equalised much better by the multilayer perceptron based equaliser.

Fuzzy adaptive filters, with application to nonlinear channel equalization

Two fuzzy adaptive filters are developed: one uses a recursive-least-squares (RLS) adaptation algorithm, and the other uses a least-mean-square (LMS) adaptation algorithm. The RLS fuzzy adaptive filter is constructed through the following four steps: (1) define fuzzy sets in the filter input space Rn whose membership functions cover U; (2) construct a set of fuzzy IF-THEN rules which either come from human experts or are determined during the adaptation procedure by matching input-output data pairs; (3) construct a filter based on the set of rules; and (4) update the free parameters of the filter using the RLS algorithm. The design procedure for the LMS fuzzy adaptive filter is similar. The most important advantage of the fuzzy adaptive filters is that linguistic information (in the form of fuzzy IF-THEN rules) and numerical information (in the form of input-output pairs) can be combined in the filters in a uniform fashion. The filters are applied to nonlinear communication channel equalization problems. >

Adaptive channel equalisation using a polynomial-perceptron structure

The paper investigates the application of a simple nonlinear structure to the problem of adaptive channel equalisation. Based on the Bayes decision rule, it is shown that the optimal equalisation solution is an inherently nonlinear problem and, therefore, it is desired to incorporate some degree of nonlinearity in the design of equaliser structure. The approximate realisation of the optimal equalisation solution is implemented using a polynomial-perceptron architecture and simulation results are included to support the theoretical analysis.

Real-time self-tuning deconvolution filter and smoother

A new self-tuning deconvolution filter/smoother is presented. The algorithm runs in real time on a TMS32010 microprocessor. The filter/smoother employs a moving average of the innovations sequence to estimate the required signal. An application to speech processing and to the channel equalization problem is described.

Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems

Conventional equalization and carrier recovery algorithms for minimizing mean-square error in digital communication systems generally require an initial training period during which a known data sequence is transmitted and properly synchronized at the receiver. This paper solves the general problem of adaptive channel equalization without resorting to a known training sequence or to conditions of limited distortion. The criterion for equalizer adaptation is the minimization of a new class of nonconvex cost functions which are shown to characterize intersymbol interference independently of carrier phase and of the data symbol constellation used in the transmission system. Equalizer convergence does not require carrier recovery, so that carrier phase tracking can be carried out at the equalizer output in a decision-directed mode. The convergence properties of the self-recovering algorithms are analyzed mathematically and confirmed by computer simulation.

Copper wire tables

In this paper, a dynamic fuzzy neural network (DFNN) is applying for communication channel equalization problem. By combining fuzzy rules with the learning ability of neural networks, DFNN can achieve the advantages of both fuzzy logic and neural networks. The simulation results show that DFNN equalizer is superior to other equalizers such as recurrent neural network (RNN) and minimal resource allocation networks (MRAN) in terms of bit error rate (BER).