Likelihood-Based Modulation Classification for Multiple-Antenna Receivers
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Authors
Ramezani-Kebrya, Ali
Date
2012-09-21
Type
thesis
Language
eng
Keyword
Modulation Classification , Estimation Theory , Cramer Rao Lower Bound
Alternative Title
Abstract
Prior to signal demodulation, blind recognition of the modulation
scheme of the received signal is an important task for intelligent
radios in various commercial and military applications such as
spectrum management, surveillance of broadcasting activities and adaptive
transmission. Antenna arrays provide spatial diversity and increase channel
capacity. This thesis focuses on the algorithms and performance analysis of
the blind modulation classification (MC) for a multiple antenna receiver configuration.
For a single-input-multiple-output (SIMO) configuration with unknown channel amplitude, phase, and noise variance, we
investigate likelihood-based algorithms for linear digital MC. The existing
algorithms are presented and extended to SIMO. Using recently proposed blind estimates of the unknown parameters, a
new algorithm is developed. In addition, two upper bounds on the classification performance of MC
algorithms are provided. We derive the exact Cramer-Rao Lower Bounds (CRLBs) of joint estimates of the unknown parameters for one- and two-dimensional amplitude modulations. The asymptotic behaviors of the CRLBs are obtained for the high signal-to-noise-ratio (SNR) region. Numerical results demonstrate the accuracy of the CRLB expressions and confirm that the expressions in the literature are special cases of our results. The classification performance of the proposed algorithm is compared with the existing algorithm and upper bounds. It is shown that the proposed algorithm outperforms the existing one significantly with reasonable computational complexity.
The proposed algorithm in this thesis can be used in modern intelligent radios equipped with multiple antenna receivers
and the provided performance analysis, i.e., the CRLB expressions, can be employed to design practical systems involving estimation of the unknown parameters
and is not limited to MC.
Description
Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2012-09-21 00:51:43.938
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This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.