Type of Document	Thesis
Author	Connor, Jeffrey David
Author's Email Address	connoje@eng.fsu.edu
URN	etd-04182005-144227
Title	A Study of Despread-Respread Multitarget Adaptive Algorithms In An AWGN Channel
Degree	Master of Science
Department	Electrical and Computer Engineering, Department of
Advisory Committee	Advisor Name Title Frank B. Gross Committee Chair Bing W. Kwan Committee Member Simon Foo Committee Member
Keywords	Frequency Division Multiple Access FDMA Frequency Reuse Frequency Planning Gold Sequence Handoff High Sensitivity Reception Kalman Filter Least Squares Least Mean Squares LMS LS-CMA LS-DRMTA LS-DRMTCMA Macro-Cell Maximal Length Sequences Mean Square Error Micro-Cell Mobile User Multiple Access Multiple Access Interference Narrowband Input Data Model Phased Arrays Psuedo-Noise Psuedo Random Noise PN Sequence Recursive Least Squares RLS Sample Matrix Inversion RLS-CMA SMI Signal-To-Interference-Plus-Noise Ratio SINR Smart Antennas SDMA Space Division Multiple Access Spatial Filtering Spatial Filtering For Interference Reduction Spread Spectrum Steepest Descent Time Division Multiple Access TDMA Two Dimensional Beamformer Uniform Linear Array ULA Walsh Function Weiner Array Factor Adaptive Antenna Arrays Element Space Beamformer DR-KPMTA Despread-Respread Kalman Predictor Multitarget Ar Direct Sequence Spread Spectrum Despread-Respread Directing CDW Convergence Degree Of Weight Constant Modulus CDMA Code Division Multiple Access Co-Channel Interference Channel Cellular Concept Butler Matrix BRLS-DRMTA Beamforming Matrix Beamforming Network Beamsteering Bit Error Rate Block RLS Despread-Respread Multitarget Array Beamforming Base Station
Date of Defense	2005-04-12
Availability	unrestricted
Abstract Typical adaptive algorithms attempt to exploit some characteristic of a desired mobile user’s signal incident upon an array of antenna elements to form a blind estimate of the user’s signal, wherein this estimate is used to update weights added to each element of the array in order to perform beamsteering. Generally, when mobile user’s operate in a CDMA mobile environment two particular characteristics are exploited: 1.) Minimizing the Mean Square Error (MSE) between the array output and the blind estimate of the desired user. 2.) Restoring the constant modulus to the output of the adaptive array corrupted by noise in the channel. These typical adaptive algorithms do not utilize knowledge of the spreading sequences used in a CDMA system, which separate users occupying the same frequency and time channels. However, this knowledge is exploited by Despread-Respread Multitarget Arrays (DRMTA). The four DRMTA algorithms which currently exist are: 1.) Least Squares Despread-Respread Multitarget Constant Modulus Array (LS-DRMTCMA) 2.) Least Squares Despread-Respread Multitarget Array (LS-DRMTA) 3.) Block Based RLS Despread-Respread Multitarget Array (BRLS-DRMTA) 4.) Despread-Respread Kalman Predictor Multitarget Array (DR-KPMTA) The objective of this thesis is to develop a comparison between these four algorithms for a stationary, additive white Gaussian noise (AWGN) channel in a CDMA mobile environment using MATLAB computer simulations for the following metrics: 1.) Analyzing Array Factor Patterns (Beampatterns) 2.) Signal-to-Interference-plus-Noise Ratio (SINR) 3.) Convergence Degree of Weight (CDW) 4.) Bit Error Rate (BER) These comparisons are performed for several different scenarios: - Highly corruptive AWGN channel. - Low SINR environment. - Response to poor initial conditions. - Measuring Convergence characteristics. - Number of users greater than or equal to number of elements in array. - Response to a sudden increase in total number of users in environment - Reduced orthogonality of spreading sequences. - Minimizing MSE by maximizing CDW.
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