International Journal of Circuits, Systems and Signal Processing

   
E-ISSN: 1998-4464
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021

Title of the Paper: Optimization of Sparse Learning Problem of Signals on Hybrid mm-Wave MIMO Systems using Sparse Coding based Reconstruction Learning Mechanism

Authors: Sunil Kumar M., Narayanappa C. K., Nagendra Kumar M.

Pages: 713-721 

DOI: 10.46300/9106.2021.15.79     XML

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Abstract: Researchers and industry experts are looking for the availability of large bandwidth spectrum due to high market demands and expectations for high data rates. And Millimeter Wave technology possess characteristics to fulfill these requirements. However, due to high power consumption and channel estimation requirements, massive MIMO is utilized in coordination with Millimeter Wave technology. Besides, the performance of mm-WAVE MIMO system is measured by the effective estimation of Channel State Information (CSI) which is a critical and challenging process. Therefore, a Sparse Coding based Reconstruction Learning (SCL) mechanism is presented to efficiently estimate Channel State Information (CSI) for Millimeter-WAVE massive Multiple Input Multiple Output (MIMO) system. For efficiency enhancement, joint sparse learning problem is formulated and a denoised joint sparsity learning matrix is obtained using proposed SCL mechanism. Here, optimization of joint sparse learning problem is summarized by reducing inconsistent and overfitting errors. The proposed SCL mechanism performs well under high as well as low SNR conditions. Moreover, joint sparse coding algorithm is utilized for efficient sparse signal restoration. The performance of proposed SCL mechanism is efficiently measured against several state-of-art-algorithms in terms of energy efficiency, NMSE, channel capacity etc.