Optimization of Sparse Learning Problem of Signals on Hybrid mm-Wave MIMO Systems using Sparse Coding based Reconstruction Learning Mechanism
| Autor: | M., Sunil Kumar, C. K., Narayanappa, M. Nagendra Kumar |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021103 operations research
Signal Processing 0211 other engineering and technologies 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 02 engineering and technology Electrical and Electronic Engineering Computer Science::Information Theory |
| Zdroj: | International Journal of Circuits, Systems and Signal Processing. 15:713-721 |
| ISSN: | 1998-4464 |
| DOI: | 10.46300/9106.2021.15.79 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|