Type of Document 
Master's Thesis 
Author 
Phillips, Kim A.

URN 
etd052699160217 
Title 
Probability Density Function Estimation Applied to Minimum Bit Error Rate Adaptive Filtering 
Degree 
Master of Science 
Department 
Electrical and Computer Engineering 
Advisory Committee 
Advisor Name 
Title 
Reed, Jeffrey Hugh 
Committee Chair 
Pratt, Timothy J. 
Committee Member 
Tranter, William H. 
Committee Member 

Keywords 
 Bit Error Rate
 Adaptive Signal Processing
 Probability Density Function
 DSP
 Equalization
 Interference Rejection
 Digital Communications
 Digital Signal Processing
 PDF
 Minimum BER Estimation
 MBE
 BER
 Adaptive Filtering

Date of Defense 
19990510 
Availability 
unrestricted 
Abstract
It is known that a matched filter is optimal for a signal corrupted by Gaussian noise. In a wireless environment, the received signal may be corrupted by Gaussian noise and a variety of other channel disturbances: cochannel interference, multiple access interference, large and smallscale fading, etc. Adaptive filtering is the usual approach to mitigating this channel distortion. Existing adaptive filtering techniques usually attempt to minimize the mean square error (MSE) of some aspect of the received signal, with respect to the desired aspect of that signal. Adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER). The main focus of this research involves estimation of the probability density function (PDF) of the received signal; this PDF estimate is used to adaptively determine a solution that minimizes BER. To this end, a new adaptive procedure called the Minimum BER Estimation (MBE) algorithm has been developed. MBE shows improvement over the Least Mean Squares (LMS) algorithm for most simulations involving interference and in some multipath situations. Furthermore, the new algorithm is more robust than LMS to changes in algorithm parameters such as stepsize and window width.

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