In application to time convolutive mixing model or frequency domain blind separation model for wireless receiving applications, frequency domain in-dependent component analysis (FDICA) has been a very popular method but with adverse random per-mutation ambiguity influence. In order to solve this inherent problem in FDICA assisted multiple-input multiple-output orthogonal frequency-division multi-plexing (MIMO-OFDM) based the Internet of Thing (IoT) systems, this paper proposes an new detection mechanism, named independent vector analysis (IVA), for realizing blind adaptive signal recovery in MIMO IoT green communication to reduce inter-carrier inter-ference (ICI) and multiple access interference (MAI). IVA jointly implements separation work for different frequency bin data while the FDICA deals with it sep-arately. In IVA, the dependencies of frequency bins can be exploited in mitigating the random permutation problem. In addition, multivariate prior distributions are employed to preserve the inter-frequency depen-dencies for individual sources, which can result in sep-aration performance enhancement. Simulation results and analysis corroborate the effectiveness of the pro-posed method.

Zhongqiang Luo;Mingchun Li;Chengjie Li

School of Automation and Information Engineering,Sichuan University of Science and Engineering,Yibin 644000,China;Artificial Intelligence Key Laboratory of Sichuan Province,Sichuan University of Science and Engineering,Yibin 644000,China;School of School of Computer Science and Technology,Southwest Minzu University,Chengdu 610041,China

中国通信（英文版）

[1]Zhongqiang Luo;Mingchun Li;Chengjie Li-.Independent Vector Analysis Based Blind Interference Reduction and Signal Recovery for MIMO IoT Green Communications)[J].中国通信（英文版）,2022(07):79-88

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