Adaptive beamforming techniques for mitigating interference in wireless networks
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Authors
Mylonas, Ioannis
Subjects
Noise
Interference
Adaptive Beamforming
Matched Filter (MF),Single-Input-Multiple-Output (SIMO)
Multiple-Input-Multiple-Output (MIMO)
Maximal Ratio Combining (MRC)
Generalized Selection Combining (GSC)
Random Variable (RV)
Probability Density Function (pdf)
Probability of signal detection (Pd)
Probability of Bit error (BER or Pb)
Signal-to-Noise Ratio (SNR)
Signal-to-Interference-and-Noise Ratio (SINR)
Interference
Adaptive Beamforming
Matched Filter (MF),Single-Input-Multiple-Output (SIMO)
Multiple-Input-Multiple-Output (MIMO)
Maximal Ratio Combining (MRC)
Generalized Selection Combining (GSC)
Random Variable (RV)
Probability Density Function (pdf)
Probability of signal detection (Pd)
Probability of Bit error (BER or Pb)
Signal-to-Noise Ratio (SNR)
Signal-to-Interference-and-Noise Ratio (SINR)
Advisors
Romero, Ric Α.
Ha, Tri T.
Date of Issue
2013-09
Date
Sep-13
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
In this thesis, a single-input-multiple-output (SIMO) wireless antenna network is investigated in terms of its performance. We analyze the system with regard to the number of receive-transmit antennas (Rx-Tx) and other network parameters such as noise, interferences, channel taps and gain factors. We derive the diversity signal-to-interference-and-noise ratio (SINR) expression initially for each individual Rx and then for the whole system. Afterwards, four distributive adaptive beamforming techniques are proposed and described in detail. Each one uses a different gain selection combining (SC) rule, and we propose one of them as the most efficient, a technique that provides a satisfactory combined SINR (SINRC) with the least number of channel taps used. Then we use detection fundamentals and probability theory to derive an expression for the average probability of signal detection (dP) for asingle Rx. Considering two of our parameters (the channel tap vector and the gain factor) as being random variables (RVs), we perform a double averaging procedure over the two RVs’ probability density functions to derive dPformulas. Two cases are studied followed by their respective dP plots and comparisons. Finally, two modulation techniques are employed, and following the same averaging procedure, we derive bit error ratio (BER) formulas and plot various BER curves. This analysis provides quantitative results with regard to BER improvement techniques.
Type
Thesis
Description
Series/Report No
Department
Electrical and Computer Engineering
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
Copyright is reserved by the copyright owner.
Copyright is reserved by the copyright owner.