Compression Algorithm for End-to-End Communication using CNN

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Autoencoder has been applied in end-to-end physical layer communication, and its performance exceeds that of traditional communication systems in some scenarios. However, autoencoder has very high computational complexity and requires a lot of storage resources. This makes them difficult to deploy on embedded systems with limited hardware resources. In this paper, we introduce model compression algorithm for the end-to-end communication system composed of convolutional neural networks (CNNs). Firstly, the pruning algorithm for end-to-end communication system is designed to delete the weight coefficients that are not important to the performance. Secondly, according to the characteristics of autoencoder, we design a codebook-based quantization scheme to further reduce the memory consumption. In the case of Gaussian, Rayleigh and bursty fading channels, the experimental results show that the number of weight coefficients and the required resources are greatly reduced after compression, without performance degradation in low Eb/N0 and negligible performance degradation in high Eb/N0.

原文English
主出版物標題2021 7th International Conference on Computer and Communications, ICCC 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面318-323
頁數6
ISBN(電子)9781665409506
DOIs
出版狀態Published - 2021
事件7th International Conference on Computer and Communications, ICCC 2021 - Chengdu, China
持續時間: 10 12月 202113 12月 2021

出版系列

名字2021 7th International Conference on Computer and Communications, ICCC 2021

Conference

Conference7th International Conference on Computer and Communications, ICCC 2021
國家/地區China
城市Chengdu
期間10/12/2113/12/21

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