Summary of Modeling Of Time-varying Wireless Communication Channel with Fading and Shadowing, by Lee Youngmin et al.
Modeling of Time-varying Wireless Communication Channel with Fading and Shadowing
by Lee Youngmin, Ma Xiaomin, Lang S.I.D Andrew
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach combines a deep learning neural network with a mixture density network model to derive the conditional probability density function (PDF) of receiving power given a communication distance in general wireless communication systems. This method is designed to dynamically adapt to changes in communication environments using a deep transfer learning scheme. The new approach is shown to be more statistically accurate, faster, and more robust than previous deep learning-based channel models when tested on Nakagami fading channel model and Log-normal shadowing channel model with path loss and noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless communication systems need help predicting how signals will change as they move through different channels. Right now, some models use special kinds of artificial intelligence called deep learning to predict this change. But these models aren’t very good because they don’t take into account things like coding and signal processing. This paper proposes a new way to do this using both deep learning and another type of model called a mixture density network. This new approach is better than the old ones because it’s more accurate, faster, and can adapt to changing environments. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Probability » Signal processing » Transfer learning
