KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#dsh #dsh-plugin #codex #typescript #developer-tools #ai-agents #deepseek-harness #claude-code #ai-agent #cli #react #mcp

Interpolation-ResNet

★ 28 Open GitHub ↗

Invited paper for WSA 2021

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Interpolation-ResNet
Invited paper for WSA 2021, called 'Low Complexity Channel estimation with Neural Network Solutions'.

%%
Cite as: 
  
  @INPROCEEDINGS{9739166,
  author={Luan, Dianxin and Thompson, John},
  booktitle={WSA 2021; 25th International ITG Workshop on Smart Antennas}, 
  title={Low Complexity Channel estimation with Neural Network Solutions}, 
  year={2021},
  volume={},
  number={},
  pages={1-6},
  doi={}}
%%

Low complexity residual convolutional neural network for channel estimation

Conpared with the ReEsNet from the repo Residual_CNN, it has slightly improved performance and the number of parameters is reduced by 82% (when pruning is not applied). I planed to release the code when I sorted out the files.

Run Demonstration_of_H_regression_48_CommuRayleigh.m to test.

Run ResNN_pilot_regression.m for training the neural network.

One thing you need to keep in mind is that, the pruning is applied without retraining because it aims to minimize the computations/latency for low-complexity solutions. Typically, after pruning there should be complex procedures (Learning both weights and connections for efficient neural networks, arXivpreprint) to compensate, but that is not realistic for low-latency and low-complexity online implementation. For sure, that pruning degrades the performance significantly. I just remove the retraining and feedback loop.

Interpolation-ResNet

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README