As wireless communication systems continue to evolve, the study of Radio Frequency (RF) Power Amplifiers (PAs) has attracted growing interest. Addressing the modeling issues of PAs can provide more convenient research methods and more efficient guidance in fields such as power amplifier predistortion, power amplifier design and evaluation, and the identification of subtle characteristics of RF radiation sources. This paper investigates RF PA nonlinear modeling methods based on neural networks, using BP, Elman, and RBF neural network algorithms to model and simulate five common nonlinear models, namely Saleh, Rapp, memoryless polynomial, Volterra, and memory polynomial models. This study not only demonstrates the performance differences of various algorithms across different models but also provides important references for the selection and optimization of algorithms in practical applications.
KEYWORDS: Transmitters, Optical communications, Modulation, Signal to noise ratio, Radio optics, Optical transmission, Information security, Channel projecting optics, Telecommunications, Distortion
In order to authenticate the optical transmitter effectively and improve the transmission performance of the optical communication greatly, the paper studies a method of optical transmitter authentication based on group delay estimation. The paper extracts the bispectrum phase information of the optical channel according to the communication frame sequence of the transceiver system, and lists the phase frequency coefficient square matrix of the transmission system to estimate the transmitter group delay characteristic parameters. The simulation results show that, even when the signal to noise ratio (SNR) is 5dB, normalized mean square error (NMSE) of estimated group delay by the bispectrum algorithm can reach -18dB. Compared with the traditional method, the method introduced in this paper show that the group delay estimation method is more effective and has better performance against noise.
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