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|Title:||Single channel blind source separation for MISO communication systems|
|Keywords:||Ensemble empirical mode decomposition|
Independent component analysis
Principal component analysis
|Abstract:||Massive multiple input multiple output (MIMO) is the key technology in the 5G communication network. Blind source separation (BSS) plays an important role in MIMO communication when information about the transmitted signals or channel is not available. Independent component analysis (ICA) is one of the well-known BSS technique applied in MIMO wireless communication. Existing BSS techniques like ICA assumes number of receiving antennas to be greater than or equal to the number of transmit antennas and hence cannot be applied to multiple input single output (MISO) communication scenario, that happens during downlink massive MIMO communication. In single channel blind source separation (SCBSS) the source signals are retrieved from a single observed mixture (single channel or single receiving antenna) of the source signals. In this paper, we investigate the application of SCBSS to MISO communication system. Using ensemble empirical mode decomposition (EEMD), the MISO communication system is converted to a pseudo- MIMO system followed by the application of principal component analysis (PCA) and ICA algorithms to separate the transmitted signals. The algorithm is successfully applied to 2 � 1 MISO system and with a slight modification to n � 1 MISO system. Simulation results are provided to validate the performance of the algorithm. � 2017 IEEE.|
|Appears in Collections:||Research Publications|
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