A Study on the Detection Algorithm of QPSK Signal Using TDNN
Mobile communications and digital wireless communications are requested high frequency use-rates, more efficient data transmission with limited signal power, frequency band. As multiple users share the same frequency in the mobile communications environment, the spectrum efficiency is getting higher. Moreover, as the effect of the velocity of the mobile object and the terrain surroundings get higher, the digital modulation method is required that the character of linear constant amplitude. In this paper, to restore simply and correctly the received signal of quadrature phase shift keying (QPSK) signal in digital wireless communications, we suggest and simulate an algorithm for detection of QPSK signal using time delay neural networks (TDNN). As the results of simulation, the suggested method is confirmed that the phase information of the QPSK signal is recovered simply and correctly in the mobile communications and digital wireless communications.
KeywordsSpectrum Efficiency Mobile Object Symbol Error Rate Inter Symbol Interference Symbol Error Probability
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- 1.Proakis, J.G.: Digital Communications. McGraw-Hill Book Company, New York (1989)Google Scholar
- 2.Lee, E.A., Messerschmitt, D.G.: Digital Communication, Norwell. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
- 3.Diederich, J.: Artificial Neural Networks Concept Learning. In: IEEE Computer Society Neural Networks Technology Series. IEEE Computer Society, Los Alamitos (1990)Google Scholar
- 4.Zurada, J.M.: Introduction to Artificial Neural Systems. West Publishing Company, St. Paul (1992)Google Scholar
- 6.Lippmann, R.P.: An Introduction to Computing with Neural Nets. IEEE Acoustics, Speech and Signal Processing Magazine 4, 4–22 (1987)Google Scholar
- 8.Provence, J.D.: Neural Network Implementation for Maximum-Likelihood Sequence Estimation of Binary Signals in Gaussian Noise. In: Proceedings of IEEE Int. Conf. on Neural Networks, pp. 703–714. IEEE, Los Alamitos (1987)Google Scholar
- 10.Bessai, H.J., Loker, K.: Artificial Neural Networks For Smart Detection of Digitally Modula-tion Signals. In: Proceedings of IEEE Int. Conf. GLOBECOM 2004, vol. 2, pp. 1029–1033 (1994)Google Scholar
- 11.Hayes, P.V., Uhey, J.R., Sayegh, S.I.: Adaptive FSK Decoding with an Artificial Neural Network. In: Proceedings of IEEE Int. Tactical Communications Conf., vol. 1, pp. 197–208 (1994)Google Scholar
- 12.Rui, J.P., de Figueiredo: Dynamical Functional Artificial Neural Networks(D-FANNS) for Intelligent Signal Processing. In: IEEE Symposium on Advances in Digital Filtering and Signal Processing, vol. 1, pp. 1–4 (1998)Google Scholar