Summary of A Machine Learning Approach For Simultaneous Demapping Of Qam and Apsk Constellations, by Arwin Gansekoele et al.
A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations
by Arwin Gansekoele, Alexios Balatsoukas-Stimming, Tom Brusse, Mark Hoogendoorn, Sandjai Bhulai, Rob van der Mei
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT)
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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 probabilistic framework enables a single deep neural network (DNN) demapper to simultaneously demap multiple QAM and APSK constellations, exploiting hierarchical relationships between them. This approach reduces the number of neural network outputs required to encode a given function without increasing the Bit Error Rate (BER). Simulation results demonstrate that the method approaches the optimal demodulation error bound under an Additive White Gaussian Noise (AWGN) channel for multiple constellations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is found to make deep neural networks (DNNs) better for real-world use as receivers. This idea uses a special kind of math called probability to help DNNs work with many different types of signals at the same time. This makes it possible to use fewer DNN outputs without making mistakes. Tests show that this approach can be very good, almost as good as the best way we know how to demodulate (change) signals. |
Keywords
» Artificial intelligence » Neural network » Probability
