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Title: Distribution dependent adaptive learning
Author: Bhatia, Vimal
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2005
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To improve the performance of adaptive algorithms, we develop algorithms adapted on the noise characteristics rather than adapting only on second order statistics. The developments in this thesis can be classified in two major works. First work is on developing a minimum bit-error rate (MBER) decision feedback equaliser (DFE) for impulsive noise modelled as a α-stable distribution. The development exploits the stable nature of the α-distribution and the concepts build on earlier work in a Gaussian noise environment. Further, a Wiender-filter-with-limiter solution is also presented and used as a performance bench mark. An improvement in convergence and BER performance is achieved by using a minimum bit error rate (MBER) cost function instead of a conventional least mean square (LMS) based design. The ability of least BER (LBER) equalisers based on a Gaussian noise assumption to operate in α-stable noise environment is also highlighted. In the second work, a block based maximum-likelihood algorithm using kernel density estimates to improve channel estimation in non-Gaussian noise environment is proposed. The likelihood pdf is assumed unknown and is estimated by using a kernel density estimator at the receiver. Thereby combining log-likelihood as a cost function with a kernel density estimator provides a robust channel estimator, which could be used for various non-Gaussian noise environments without any modification. The performance of the proposed estimator is compared with the theoretical lower bounds for associated noise distribution. The simulations for impulsive noise and co-channel interference (CCI) in presence of Gaussian noise, confirms that a better estimate can be obtained by using the proposed technique as compared to the traditional algorithms. The proposed algorithm is then applied to orthogonal frequency division multiplexing (OFDM) communication systems. A considerable performance improvement is observed when using a non-parametric channel estimator in conjunction with a symbol-by-symbol non-parametric maximum a posteriori probability (MAP) equaliser.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available