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Variational Channel Estimation with Tempering: An Artificial Intelligence Algorithm for Wireless Intelligent
Jia Liu1, Mingchu Li1, Yuanfang Chen2
1School of Software Technology and Key Laboratory for Ubiquitous Network and Service Software, Dalian University of Technology, Dalian 116620, China.
Sensors (Basel, Switzerland)
|October 24, 2020
Summary
This study introduces a new Channel Estimation Variational Tempering Inference (CEVTI) algorithm for wireless sensor networks. CEVTI offers reduced complexity and improved accuracy for channel estimation, outperforming existing methods.
Area of Science:
- Wireless Sensor Networks (WSNs)
- Signal Processing
- Machine Learning
Background:
- Accurate state acquisition is crucial for WSN applications like monitoring and tracking.
- Existing channel estimation algorithms suffer from high complexity, poor scalability, and slow convergence.
Purpose of the Study:
- To develop a novel, efficient, and reliable algorithm for channel estimation in WSNs.
- To address the limitations of current channel estimation techniques.
Main Methods:
- Utilized variational inference (VI) with tempering to model channel estimation as a probabilistic graphical model.
- Developed the Channel Estimation Variational Tempering Inference (CEVTI) approach.
- Formulated channel estimation to include pilot signals and channel coefficients.
Main Results:
- CEVTI demonstrates lower complexity and guarantees convergence to a local optimum.
- The algorithm shows higher accuracy compared to state-of-the-art methods across various noise levels.
- Faster convergence rates and lower bit error rates were observed with increased parameters per iteration.
Conclusions:
- CEVTI is an efficient, simple, and reliable algorithm for channel estimation in WSNs.
- The method offers advantages in complexity, scalability, and convergence guarantees.
- CEVTI is adaptable for Code Division Multiple Access (CDMA) and massive MIMO systems.
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