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Constellation Classification Based on Sequential Monte Carlo for Intersymbol Interference Channels

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Abstract

Constellation classification for phase-amplitude-modulated signals transmitted through unknown intersymbol interference channels is proposed based on the sequential Monte Carlo (SMC) framework under both stochastic and deterministic settings. The stochastic SMC-based constellation classification (SMC-CC) sampler generates constellation symbol samples based on importance sampling and resampling techniques, whereas the deterministic SMC-CC approach recursively performs exploration and selection steps in a greedy manner. Then the constellation classification is achieved according to the distribution of the drawn samples in both the stochastic SMC-CC and the deterministic SMC-CC. Moreover, both the proposed methods are achieved along with joint estimation of transmitted data symbols and channel taps. Simulations show that the proposed methods perform well on various constellations with different cardinalities, as well as constellations with symbols.

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Authors and Affiliations

  1. The State Key Lab. of Integrated Services Networks, Xidian University, 710071, Xi’an, China

    Jianping Zheng, Baoming Bai & Ying Li

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  1. Jianping Zheng
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  2. Baoming Bai
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  3. Ying Li
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Correspondence to Jianping Zheng.

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Zheng, J., Bai, B. & Li, Y. Constellation Classification Based on Sequential Monte Carlo for Intersymbol Interference Channels. Wireless Pers Commun 55, 135–146 (2010). https://doi.org/10.1007/s11277-009-9781-5

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  • DOI: https://doi.org/10.1007/s11277-009-9781-5

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