Summary of Complexity Reduction in Machine Learning-based Wireless Positioning: Minimum Description Features, by Myeung Suk Oh et al.
Complexity Reduction in Machine Learning-Based Wireless Positioning: Minimum Description Features
by Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
First submitted to arxiv on: 14 Feb 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 In this paper, researchers tackle the challenge of wireless positioning using deep learning approaches. While these algorithms have shown high accuracy and robustness, they require processing high-dimensional features, which can be problematic for mobile applications. The authors propose a positioning neural network (P-NN) that reduces complexity by selecting minimum description features based on maximum power measurements and temporal locations. A novel methodology is also introduced to adaptively select the feature space size, optimizing the balance between useful information and classification capability. The results demonstrate P-NN’s significant advantage in performance-complexity tradeoff over deep learning baselines that utilize the full power delay profile. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wireless positioning is a way for devices to figure out where they are without using GPS or other special tools. Some algorithms do this well, but they need lots of information and can be too slow for use on phones. In this research, scientists created a new kind of neural network that uses less information and works faster. They picked the most important details from all the data and used those to make decisions about where things are. This new approach is better than old ones at finding the right balance between getting accurate results and using a lot of power. |
Keywords
* Artificial intelligence * Classification * Deep learning * Neural network
