Title page for ETD etd-052699-160217

Type of Document Master's Thesis
Author Phillips, Kim A.
URN etd-052699-160217
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
  • 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 1999-05-10
Availability unrestricted
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 small-scale 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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