Adaptive equalisation for fading digital communication channels

This thesis considers the design of new adaptive equalisers for fading digital communication channels. The role of equalisation is discussed in the context of the functions of a digital radio communication system and both conventional and more recent novel equaliser designs are described. The application of recurrent neural networks to the problem of equalisation is developed from a theoretical study of a single node structure to the design of multinode structures. These neural networks are shown to cancel intersymbol interference in a manner mimicking conventional techniques and simulations demonstrate their sensitivity to symbol estimation errors. In addition the error mechanisms of conventional maximum likelihood equalisers operating on rapidly time-varying channels are investigated and highlight the problems of channel estimation using delayed and often incorrect symbol estimates. The relative sensitivity of Bayesian equalisation techniques to errors in the channel estimate is studied and demonstrates that the structure's equalisation capability is also susceptible to such errors. Applications of multiple channel estimator methods are developed, leading to reduced complexity structures which trade performance for a smaller computational load. These novel structures are shown to provide an improvement over the conventional techniques, especially for rapidly time-varying channels, by reducing the time delay in the channel estimation process. Finally, the use of confidence measures of the equaliser's symbol estimates in order to improve channel estimation is studied and isolates the critical areas in the development of the technique — the production of reliable confidence measures by the equalisers and the statistics of symbol estimation error bursts

Development of Fuzzy System Based Channel Equalisers

Channel equalisers are used in digital communication receivers to mitigate the effects of inter symbol interference (ISI) and inter user interference in the form of co-channel interference (CCI) and adjacent channel interference (ACI) in the presence of additive white Gaussian noise (AWGN). An equaliser uses a large part of the computations involved in the receiver. Linear equalisers based on adaptive filtering techniques have long been used for this application. Recently, use of nonlinear signal processing techniques like artificial neural networks (ANN) and radial basis functions (RBF) have shown encouraging results in this application. This thesis presents the development of a nonlinear fuzzy system based equaliser for digital communication receivers. The fuzzy equaliser proposed in this thesis provides a parametric implementation of symbolby-symbol maximum a-posteriori probability (MAP) equaliser based on Bayes’s theory. This MAP equaliser is also called Bayesian equaliser. Its decision function uses an estimate of the noise free received vectors, also called channel states or channel centres. The fuzzy equaliser developed here can be implemented with lower computational complexity than the RBF implementation of the MAP equaliser by using scalar channel states instead of channel states. It also provides schemes for performance tradeoff with complexity and schemes for subset centre selection. Simulation studies presented in this thesis suggests that the fuzzy equaliser by using only 10%-20% of the Bayesian equaliser channel states can provide near optimal performance. Subsequently, this fuzzy equaliser is modified for CCI suppression and is termed fuzzy–CCI equaliser. The fuzzy–CCI equaliser provides a performance comparable to the MAP equaliser designed for channels corrupted with CCI. However the structure of this equaliser is similar to the MAP equaliser that treats CCI as AWGN. A decision feedback form of this equaliser which uses a subset of channel states based on the feedback state is derived. Simulation studies presented in this thesis demonstrate that the fuzzy–CCI equaliser can effectively remove CCI without much increase in computational complexity. This equaliser is also successful in removing interference from more than one CCI sources, where as the MAP equalisers treating CCI as AWGN fail. This fuzzy–CCI equaliser can be treated as a fuzzy equaliser with a preprocessor for CCI suppression, and the preprocessor can be removed under high signal to interference ratio condition