Independent Vector Analysis for Blind Deconvolving of Digital Modulated Communication Signals

Electronics

Zhongqiang Luo¹  Ruiming Guo¹  Chengjie Li² 

[1] School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644000, China;School of Computer Science and Technology, Southwest Minzu University, Chengdu 610041, China;

关键词: independent vector analysis;    independent component analysis;    permutation ambiguity;    digital modulation;    wireless communications;   

DOI  :  10.3390/electronics11091460

来源: DOAJ

【 摘 要 】

For the purpose of overcoming the random permutation ambiguity of the frequency-domain-independent component analysis (FDICA) for blind separation of convolutive mixtures, this paper proposes an independent vector analysis (IVA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The foundation of IVA is through jointly carrying out separation work for different frequency bin data fusion, and the dependencies of frequency bins are exploited in solving the random permutation problem of separation signals. In addition, IVA uses multivariate prior distributions instead of the univariate distribution used in FDICA. Multivariate prior distribution is employed to preserve the interfrequency dependencies for individual sources, which can give rise to separation performance enhancement. Simulation results and analysis corroborate the effectiveness of the proposed detection method.

【 授权许可】

Unknown