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Entropy 2013, 15(5), 1705-1725; doi:10.3390/e15051705

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

School of Mechatronics Engineering and Automation, National University of Defense Technology, Deya Road, Changsha 410073, Hunan Province, China
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Received: 18 March 2013 / Revised: 24 April 2013 / Accepted: 1 May 2013 / Published: 8 May 2013
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Abstract

A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN) channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm. View Full-Text
Keywords: information dispersion entropy; blind recognition; channel coding; BCH codes; adaptive coding and modulation (ACM) information dispersion entropy; blind recognition; channel coding; BCH codes; adaptive coding and modulation (ACM)
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MDPI and ACS Style

Zhou, J.; Huang, Z.; Liu, C.; Su, S.; Zhang, Y. Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations. Entropy 2013, 15, 1705-1725.

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