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Modulation format recognition in a UVLC system based on an ultra-lightweight model with communication-informed
Optics Express
|June 11, 2024
Summary
This study introduces a communication-informed knowledge distillation (CIKD) method for efficient modulation format recognition (MFR) in underwater visible light communication (UVLC). The CIKD method enables an ultra-lightweight model to achieve high accuracy and low latency, crucial for practical UVLC systems.
Area of Science:
- Optical Communications
- Machine Learning
Background:
- Modulation format recognition (MFR) is vital for adaptive optical systems.
- Underwater visible light communication (UVLC) presents unique challenges for MFR due to complex channel environments.
- Deep learning models offer powerful feature extraction but suffer from high computational complexity, limiting their use in UVLC.
Purpose of the Study:
- To propose a communication-informed knowledge distillation (CIKD) method for high-precision, low-latency MFR in UVLC systems.
- To develop an ultra-lightweight student model for practical deployment in UVLC.
- To enable efficient MFR across eight distinct modulation formats.
Main Methods:
- A communication-informed knowledge distillation (CIKD) approach was employed.
- An ultra-lightweight student neural network model (single linear dense layer) was trained using a high-complexity teacher model.
- The MFR task encompassed eight modulation formats: PAM4, QPSK, 8QAM-CIR, 8QAM-DIA, 16QAM, 16APSK, 32QAM, and 32APSK.
Main Results:
- The CIKD-based student model achieved accuracy comparable to the teacher model.
- Prediction accuracy of the student model improved by up to 87% after knowledge transfer.
- The student model demonstrated inference accuracy reaching up to 100% and utilized only 18% of the teacher model's parameters.
Conclusions:
- CIKD enables high-precision and low-latency MFR using an ultra-lightweight model for UVLC.
- The proposed method significantly reduces computational complexity, facilitating hardware deployment and online processing in UVLC systems.
- CIKD effectively transfers knowledge, enhancing the performance of lightweight models for MFR in challenging underwater environments.
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