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Optimizing MIMO Antenna Performance Using Fuzzy Logic Algorithms |
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PP: 349-364 |
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doi:10.18576/amis/190211
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Author(s) |
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Suleiman Ibrahim Mohammad,
N. Yogeesh,
N. Raja,
R. Chetana,
M. S. Ramesha,
Asokan Vasudevan,
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Abstract |
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| This work proposes a new fuzzy logic based optimization framework that can improve Multiple-Input, Multiple-Output (MIMO) antenna system performance in varying wireless communication environments. Conventional optimization techniques for MIMO, like Zero-Forcing and Minimum Mean Square Error, often rely on precise channel state information and struggle to adapt to changing channel conditions. On the other hand, fuzzy logic implements a flexible, rules-based handling that accounts for uncertainty and makes use of the real-time response of the SNR, levels of interference and power allocation. This work establishes a fuzzy inference engine that automatically tunes MIMO parameters according to the main performance metrics: system capacity, SNR, BER, interference management, and power economy. Based on simulations and hypothetical data analysis, the fuzzy logic-based optimization approach was able to achieve up to a 20% increase in system capacity, a 17.5% increase in SNR, a noticeable decrease in BER, a 40% reduction in interference power, and a 25% increase in power efficiency when compared to baseline methods. The fuzzy optimization framework effectively maximizes the performance of the MIMO system while maintaining energy efficiency, an advantage that is particularly advantageous in applications for 5G, internet of things (IoT), and highly dense urban networks. Research directions in this area include adaptive fuzzy systems, hybrid models with machine learning, and scalable solutions for massive MIMO in next-generation networks.
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