We report a new approach to analyse the effects of low noise amplifier (LNA) non-linear distortions in the code division multiple access (CDMA) wireless communication systems using spatio-temporal analysis and Volterr...
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We report a new approach to analyse the effects of low noise amplifier (LNA) non-linear distortions in the code division multiple access (CDMA) wireless communication systems using spatio-temporal analysis and Volterra series theory. For this purpose, the bit error rate (BER) performance of three blindalgorithms is studied based on post correlated model of received signal, and a time-varying multiple vector channel model which is an extended form of the Gaussian wide sense stationary uncorrelated scattering (GWSSUS) channel. By using the Volterra series theory, an analytical expression for amplitude modulation to phase modulation (AM-PM) conversion is determined as a phase statement of LNA compression. In this approach, by combining the analytical expression for AM-PM conversion and CDMA blindbeamforming techniques, we evaluate the AM-PM distortion effects on BER performance of a CDMA system originated from multiple non-linear LNA blocks. Simulation results show that conditions are found to minimize AMPM conversion introduced by multiple non-linear blocks in the system leading to low BER. Copyright (c) 2006 John Wiley & Sons, Ltd.
作者：
He, ZChen, YDSP Division
Department of Radio Engineering Southeast University Nanjing People's Republic of China
Many blind beamforming algorithms, such as C-CAB, use cyclostationarity to estimate the steering vector and adaptively obtain the LCMV optimum solution. However, LCMV methods are sensitive to the mismatch caused by th...
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Many blind beamforming algorithms, such as C-CAB, use cyclostationarity to estimate the steering vector and adaptively obtain the LCMV optimum solution. However, LCMV methods are sensitive to the mismatch caused by the uncalibration array or estimate error. After discussion of this mismatch, a robust blindbeamforming algorithm is presented in the paper. Implemented as a neural network, this algorithm reduces computational complexity for real-time use. Computer simulations verify the analysis.
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