A Digital Pre-Distortion Scheme for CMMB Repeater System Based on Dynamic Neural Networks

Ze Biao Lin; Chun Hui Huang

doi:10.4028/www.scientific.net/AMM.336-338.1738

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 336-338A Digital Pre-Distortion Scheme for CMMB Repeater...

A Digital Pre-Distortion Scheme for CMMB Repeater System Based on Dynamic Neural Networks

Abstract:

CMMB technology employs Orthogonal Frequency Division Multiplexing (OFDM) modulation with 4096 subcarriers for 8MHz bandwidth mode, HPA in the CMMB repeater will cause significant distortion and spectral extension. To compensate HPA nonlinearity and memory effect in a CMMB repeater system, this paper proposed a pre-distortion scheme based on RNN and Bayesian Regularization for the first time. Computer simulation is used to confirm the validation of the proposed scheme and an adjacent channel leakage ratio (ACLR) improvement of 20 dB is obtained in the paper.

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Periodical:

Applied Mechanics and Materials (Volumes 336-338)

Pages:

1738-1743

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.336-338.1738

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Online since:

July 2013

Authors:

Ze Biao Lin*, Chun Hui Huang

Keywords:

Bayesian Regularization, China Mobile Multimedia Broadcasting (CMMB), High-Power Amplifier (HPA), Memory Effect, Recurrent Neural Network (RNN)

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* - Corresponding Author

References

[1] Lei Guoping, Dai Minlu, Tan Zefu, Wang Yan: Wireless Communications, Networking and Mobile Computing (WiCOM), 2011 7th International Conference on (2011), pp.1-4.

Google Scholar

[2] W. Woo, M. D. Miller, and J. S. Kenney: IEEE Trans. Microw. Theory Tech. , vol. 53, no. 1, (2005), p.229–237.

Google Scholar

[3] A. Zhu and J. C. Pedro: IEEE MTT-S Int. Microw. Symp. Dig. , (2007), p.965–968.

Google Scholar

[4] D. R. Morgan, M. Zhenngxiang, L. Kim, M. G. Zierdt, and I. Pastalan: IEEE Trans. Signal Process. , vol. 54, no. 10, (2006) p.3852–3860.

DOI: 10.1109/tsp.2006.879264

Google Scholar

[5] Y. Quian and F. Liu: in 1st Int. Commun. Networking in China Conf. , (2006) p.1–5.

Google Scholar

[6] D. Luongyinh and Y. Kwon: IEEE MTT-S Int. Microw. Symp. Dig. , (2005) p.1979–(1982).

Google Scholar

[7] T. Liu, S. Boumaiza, and F. Ghannouchi: IEEE Trans. Microw. Theory Tech. , vol. 52, no. 3, (2004) p.1025–1033.

Google Scholar

[8] J. Pochmara: in Proceedings of the 15th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC '04), vol. 1, (2004), p.180–185.

DOI: 10.1109/pimrc.2004.1368244

Google Scholar

[9] P. Jardin and G. Baudoin: IEEE Transaction on Vehicular Technolog y, vol. 56, no. 3, (2007), p.1076–1087.

Google Scholar