Type of Document Thesis Author Connor, Jeffrey David Author's Email Address email@example.com 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
- Frequency Reuse
- Frequency Planning
- Gold Sequence
- High Sensitivity Reception
- Kalman Filter
- Least Squares
- Least Mean Squares
- Maximal Length Sequences
- Mean Square Error
- Mobile User
- Multiple Access
- Multiple Access Interference
- Narrowband Input Data Model
- Phased Arrays
- Psuedo Random Noise
- PN Sequence
- Recursive Least Squares
- Sample Matrix Inversion
- Signal-To-Interference-Plus-Noise Ratio
- Smart Antennas
- Space Division Multiple Access
- Spatial Filtering
- Spatial Filtering For Interference Reduction
- Spread Spectrum
- Steepest Descent
- Time Division Multiple Access
- Two Dimensional Beamformer
- Uniform Linear Array
- Walsh Function
- Array Factor
- Adaptive Antenna Arrays
- Element Space Beamformer
- Despread-Respread Kalman Predictor Multitarget Ar
- Direct Sequence Spread Spectrum
- Despread-Respread Directing
- Convergence Degree Of Weight
- Constant Modulus
- Code Division Multiple Access
- Co-Channel Interference
- Cellular Concept
- Butler Matrix
- Beamforming Matrix
- Beamforming Network
- Bit Error Rate
- Block RLS Despread-Respread Multitarget Array
- Base Station
Date of Defense 2005-04-12 Availability unrestricted AbstractTypical adaptive algorithms attempt to exploit some characteristic of a desired mobile userís signal incident upon an array of antenna elements to form a blind estimate of the userís signal, wherein this estimate is used to update weights added to each element of the array in order to perform beamsteering. Generally, when mobile userís 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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