Turning Adversity Into Advantage: Robust MCS Selection Utilizing the Uncertainty of Channel Prediction Neural Networks

In wireless communication systems, the selection of suitable modulation and/or coding schemes (MCS) based on predicted channel quality is vital. However, the dynamic nature of wireless channels poses a challenge, leading to persistent inaccuracies in prediction. This letter tackles this challenge by...

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Vydáno v:IEEE wireless communications letters Ročník 13; číslo 10; s. 2632 - 2636
Hlavní autoři: Li, Yangyang, Xu, Yuhua, Li, Guoxin, Wang, Ximing, Xu, Yifan, Liu, Songyi, Yue, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.10.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accuracy
Bit error rate
Modulation
modulation and coding schemes
Neural networks
prediction uncertainty
Predictions
Receivers
Robust communication
Robustness
Time series analysis
Uncertainty
Wireless communication systems
Wireless networks
ISSN:2162-2337, 2162-2345
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Shrnutí:In wireless communication systems, the selection of suitable modulation and/or coding schemes (MCS) based on predicted channel quality is vital. However, the dynamic nature of wireless channels poses a challenge, leading to persistent inaccuracies in prediction. This letter tackles this challenge by introducing a robust communication approach for MCS selection, rooted in new paradigm of prediction uncertainty quantification. The proposed method involves establishing an uncertainty pool to quantify potential prediction errors. This approach enables the assessment of the accuracy of predictions, facilitating the informed selection of MCSs. Through simulations utilizing real-world data, the integration of our proposed design into various channel prediction neural networks enhances the performance of MCS selection. The simulations show our design enhances communication robustness without compromising throughput. The enhancement of up to 53% in communication success rates, with an average improvement of around 15%.
Bibliografie:ObjectType-Article-1
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ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2024.3393939