دورية أكاديمية
Deep-Learning-Based Adaptive Symbol Decision for Visual MIMO System with Variable Channel Modeling
| العنوان: | Deep-Learning-Based Adaptive Symbol Decision for Visual MIMO System with Variable Channel Modeling |
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| المؤلفون: | Jai-Eun Kim, Tae-Ho Kwon, Ki-Doo Kim |
| المصدر: | Sensors, Vol 22, Iss 19, p 7176 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Chemical technology |
| مصطلحات موضوعية: | adaptive symbol decision, channel modeling, deep learning, visual MIMO, generalized color modulation (GCM), Chemical technology, TP1-1185 |
| الوصف: | A channel modeling method and deep-learning-based symbol decision method are proposed to improve the performance of a visual MIMO system for communication between a variable-color LED array and camera. Although image processing algorithms using color clustering are available to correct distorted color information in a channel, color-similarity-based approaches are limited by real-world distortions; to overcome such limitations, symbol decision is defined as a multiclass classification problem. Further, to learn a robust classifier against channel distortion, a deep neural network learning technique is applied to adaptively determine symbols from channel distortion. The network designed herein comprises the channel identification and symbol decision modules; the channel identification module extracts a channel identification vector for symbol determination from an input image using a two-dimensional deep convolutional neural network (CNN); the symbol decision module then generates a feature map by combining the channel identification vector and information on adjacent symbols to determine the symbol via learning correlations between adjacent symbols using a one-dimensional CNN. The two modules are connected together and learned simultaneously in an end-to-end manner. We also propose a new channel modeling method that intuitively reflects real-world distortion factors rather than the conventional additive white Gaussian noise channel to efficiently train deep-learning networks. Lastly, in the proposed channel distortion environment, the proposed method shows performance improvement by an average of about 41.8% (up to about 54.8%) compared to the existing Euclidean distance method, and about 6.3% (up to about 9.2%) on average compared to the SVM method. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 22197176 1424-8220 |
| Relation: | https://www.mdpi.com/1424-8220/22/19/7176; https://doaj.org/toc/1424-8220 |
| DOI: | 10.3390/s22197176 |
| URL الوصول: | https://doaj.org/article/a557fef65d22406e83b291f50b09dc10 |
| رقم الأكسشن: | edsdoj.557fef65d22406e83b291f50b09dc10 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22197176 14248220 |
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| DOI: | 10.3390/s22197176 |