Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the channel state information. Current channel estimation neural networks are frequently tested by training and testing on one example channel or similar channels. However, data-driven methods often degrade on new data which they are not trained on, because they cannot extrapolate their training knowledge.

DOI Link: 
10.21227/4v9x-xe67
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[1] Dianxin Luan, "Achieving Robust Channel Estimation Neural Networks by Designed Training Data", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/4v9x-xe67. Accessed: Jan. 19, 2025.
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url = {http://dx.doi.org/10.21227/4v9x-xe67},
author = {Dianxin Luan },
publisher = {IEEE Dataport},
title = {Achieving Robust Channel Estimation Neural Networks by Designed Training Data},
year = {2024} }
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Dianxin Luan. (2024). Achieving Robust Channel Estimation Neural Networks by Designed Training Data. IEEE Dataport. http://dx.doi.org/10.21227/4v9x-xe67
Dianxin Luan, 2024. Achieving Robust Channel Estimation Neural Networks by Designed Training Data. Available at: http://dx.doi.org/10.21227/4v9x-xe67.
Dianxin Luan. (2024). "Achieving Robust Channel Estimation Neural Networks by Designed Training Data." Web.
1. Dianxin Luan. Achieving Robust Channel Estimation Neural Networks by Designed Training Data [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/4v9x-xe67
Dianxin Luan. "Achieving Robust Channel Estimation Neural Networks by Designed Training Data." doi: 10.21227/4v9x-xe67