Title page for ETD etd-11052001-213751


Type of Document Dissertation
Author Biedka, Thomas E
URN etd-11052001-213751
Title Analysis and Development of Blind Adaptive Beamforming Algorithms
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Reed, Jeffrey Hugh Committee Chair
Boyle, Robert J. Committee Member
Saunders, William R. Committee Member
Stutzman, Warren L. Committee Member
Tranter, William H. Committee Member
Keywords
  • propagation diversity nonlinear optimization
Date of Defense 2001-10-18
Availability unrestricted
Abstract
This dissertation presents a new framework for the development and analysis of blind adaptive algorithms. An adaptive algorithm is said to be 'blind' if it does not require a known training sequence. The main focus is on application of these algorithms to adaptive antenna arrays in mobile radio communications. Adaptive antenna arrays can reduce the effects of cochannel interference, multipath fading, and background noise as compared to more conventional antenna systems. For these reasons, the use of adaptive antennas in wireless communication has received a great deal of attention in the literature.

There are several reasons why the study of blind adaptive algorithms is important. First, it is common practice to switch to a blind mode once the training sequence has been processed in order to track a changing environment. Furthermore, the use of a blind algorithm can completely eliminate the need for a training sequence. This is desirable since the use of a training sequence reduces the number of bits available for transmitting information.

The analysis framework introduced here is shown to include the well-known Constant Modulus Algorithm (CMA) and decision directed algorithm (DDA). New results on the behavior of the CMA and DDA are presented here, including analytic results on the convergence rate. Previous results have relied on Monte Carlo simulation. This framework is also used to propose a new class of blind adaptive algorithms that offer the potential for improved convergence rate.

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