Effects of blind channel equalization using the regressive accelerator algorithm version ɣ

  • Johanna Andrea Hurtado Sánchez Universidad del Cauca, Popayán
  • Pablo Emilio Jojoa Gómez Universidad del Cauca, Popayán
Keywords: Blind equalization; adaptive algorithms; convergence speed; data estimation.

Abstract

We present a blind channel equalization scheme, applied to ɣ version regressive acceleration algorithm, which uses self-taught equalization techniques to study the characteristics of both, the second and the higher order moments for the transmitted signal, used to calculate the signal of error and thus, to make an optimal estimation of the transmitted symbols. This way, simulations of the obtained results are done in comparison with the algorithms based on the stochastic gradient and with the Bussgang algorithms. The results of that simulations show how, using the regressive acceleration algorithm version ɣ, a better detection of transmitted bits and higher convergence speeds are obtained, with a minimum mean square error.

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Author Biographies

Johanna Andrea Hurtado Sánchez, Universidad del Cauca, Popayán

Electronics and Telecommunications Engineer from the Universidad del Cauca (Popayan, Colombia), cursing a Master in Science (M.Sc.) in Electronics and Telecommunications Engineering in the same university. She is an assistant professor of the Telematics Department of the Electronics and Telecommunications Engineering Faculty. Her professional interest areas are digital signal processing and digital systems.

Pablo Emilio Jojoa Gómez, Universidad del Cauca, Popayán

Electronics and Telecommunications Engineer from the Universidad del Cauca (Popayan, Colombia), with Master in Science (M.Sc.) and Doctorate (Ph.D.) in Electrics Engineering with emphasis in Electronic Systems from the Universidad de São Paulo. He is an associated professor of the Telecommunications Department in the Electronics and Telecommunications Engineering Faculty. He is also the coordinator of the research group in new telecommunication technologies [GNTT, Grupo de Nuevas Tecnologías en Telecomunicaciones]. His largest research interest area is the digital signal processing.

References

Aquino, F. (2012). Uso de un procesamiento largamente en un equalizador fraccionalmente espaciado aplicado a canales de comunicación selectivos en frecuencia. Holos, 4, 113-125.

Benveniste, A., Goursat, M., & Ruget, G. (1980). Robust identification of a nonminimum phase system: Blind adjustment of a linear equalizer in data communications. IEEE Transactions on Automatic Control, 25(3), 385-399.

Erdogmus, D., & Principe, J. C. (2002). An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems. IEEE Transactions on Signal Processing, 50(7), 1780-1786.

Jojoa, P. (2003). Um algoritmo acelerador de parametros [Ph.D. thesis]. Escola Politécnica de São Paulo: Brasil.

Lathi, B. (1998). Modern digital and analog communication system [3rd ed.). New York, NY: Oxford University.

Lucky, R. W. (1966). Techniques for adaptive equalization of digital communication systems. Bell Labs Technical Journal, 45(2), 255-286.

Lugannani, R. (1969). Intersymbol interference and probability of error in digital systems. IEEE Transactions on Information Theory, 15(6), 682-688.

Madeira., T. (2005). Un estudio sobre técnicas de ecualización autodidacta [Ph.D. thesis]. Escola Politécnica de São Paulo: Brasil..

Madeira., T. (2013). Ecualización autodidacta basada en combinación de filtros adaptativos. [MSc. thesis]. Escola Politécnica de São Paulo: Brasil.

Neves, A., Attux, R., Suyama, R., Miranda, M., Romano, J. (2006). Sobre criterios para ecualización no supervisada. Revista Controle & Automação, 17(3), 278-299.

Rocha, P. (2005). Desarrollo de algoritmos de procesamiento digital de señales para la reconstrucción de imágenes usando biespectro. [MSc. thesis]. Centro Nacional de Investigación y Desarrollo Tecnológico: Cuernavaca, México.

Rolim, C. (2005). Ecualización adaptativa y autodidacta de canalaes lineales y no lineales utilizando el algoritmo de módulo constante [MSc. thesis]. Universidad en la Arquidiócesis de Fortaleza: Brasil.
Romano, J. M. T., Attux, R., Cavalcante, C. C., & Suyama, R. (2016). Unsupervised signal processing: channel equalization and source separation. Boca Raton, FL: CRC.

Saltzberg, B. (1968). Intersymbol interference error bounds with application to ideal bandlimited signaling. IEEE Transactions on Information Theory, 14(4), 563-568.

Sato, Y. (1975). A method of self-recovering equalization for multilevel amplitude-modulation systems. IEEE Transactions on communications, 23(6), 679-682.

Shalvi, O., & Weinstein, E. (1990). New criteria for blind deconvolution of nonminimum phase systems (channels). IEEE Transactions on information theory, 36(2), 312-321.

Solarte, V. (Noviembre de 2012). El algoritmo acelerador regresivo versión γ (ARγ) y los efectos de cuantificación. Revista Universitaria en Telecomunicaciones Informática y Control., 1(2), 9.

Widrow, B. & Hoff, M. (1960). Adaptative switching circuits. In 1960 IRE WESCON Convention Record, Part 4, (pp 96-104). New York, NY: Institute of Radio Engineers.
Published
2018-06-27
Section
Original Research