VLCMnet-Based Modulation Format Recognition for Indoor Visible Light Communication Systems
Autor: | Xin Zheng, Ying He, Chong Zhang, Pu Miao |
---|---|
Jazyk: | angličtina |
Rok vydání: | 2024 |
Předmět: | |
Zdroj: | Photonics, Vol 11, Iss 5, p 403 (2024) |
Druh dokumentu: | article |
ISSN: | 11050403 2304-6732 |
DOI: | 10.3390/photonics11050403 |
Popis: | In indoor visible light communication (VLC), the received signals are subject to severe interference due to factors such as high-brightness backgrounds, long-distance transmissions, and indoor obstructions. This results in an increase in misclassification for modulation format recognition. We propose a novel model called VLCMnet. Within this model, a temporal convolutional network and a long short-term memory (TCN-LSTM) module are utilized for direct channel equalization, effectively enhancing the quality of the constellation diagrams for modulated signals. A multi-mixed attention network (MMAnet) module integrates single- and mixed-attention mechanisms within a convolutional neural network (CNN) framework specifically for constellation image classification. This allows the model to capture fine-grained spatial structure features and channel features within constellation diagrams, particularly those associated with high-order modulation signals. Experimental results obtained demonstrate that, compared to a CNN model without attention mechanisms, the proposed model increases the recognition accuracy by 19.2%. Under severe channel distortion conditions, our proposed model exhibits robustness and maintains a high level of accuracy. |
Databáze: | Directory of Open Access Journals |
Externí odkaz: |