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Fuzzy-Neural MIMO Channel Modeling using Application Specific Membership and Inference Rule Generation

Kandarpa Kumar Sarma, Abhijit Mitra


Membership and inference rule generation plays a critical role in performance enhancement of fuzzy-neural (FN) modeling of Multi-Input-Multi-Output (MIMO) wireless channels. These two factors decide the ability of the fuzzy-estimator to capture the subtle variations in the input signal patterns and provide corresponding responses with optimum performance. The choice of the membership and inference set generation considerations also determine the system complexity and implementation issues while configuring a FN-based MIMO modeling method. This work proposes certain methods for formation of multiple membership generation as well as some inference rules that determine the performance of FN-MIMO modeling in terms of processing speed and precision. Experimental results establish the enhanced performance of the proposed system in comparison to statistical and other methods. The fuzzy-based methods provide advantages of faster processing time, lower bit error rate (BER) and better precision while carrying out MIMO channel modeling.


Inference set, fuzzy-neural, membership set, multiple-input multiple-output.

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