Driven Non-linear Digital Self Interference Cancellation for In-Band Full Duplex Systems Based on Convolution Long Short-term Memory Deep Neural Network

LU Lei; CHU Jianjun; TANG Yanqun; TAO Yerong; WU Zheshun; ZHENG Chengwu; CHEN Qi

doi:10.11999/JEIT220110

Volume 44 Issue 11

Nov. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(11): 3874-3881

LU Lei, CHU Jianjun, TANG Yanqun, TAO Yerong, WU Zheshun, ZHENG Chengwu, CHEN Qi. Driven Non-linear Digital Self Interference Cancellation for In-Band Full Duplex Systems Based on Convolution Long Short-term Memory Deep Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3874-3881. doi: 10.11999/JEIT220110

Citation:

LU Lei, CHU Jianjun, TANG Yanqun, TAO Yerong, WU Zheshun, ZHENG Chengwu, CHEN Qi. Driven Non-linear Digital Self Interference Cancellation for In-Band Full Duplex Systems Based on Convolution Long Short-term Memory Deep Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3874-3881. doi: 10.11999/JEIT220110

Citation:

LU Lei, CHU Jianjun, TANG Yanqun, TAO Yerong, WU Zheshun, ZHENG Chengwu, CHEN Qi. Driven Non-linear Digital Self Interference Cancellation for In-Band Full Duplex Systems Based on Convolution Long Short-term Memory Deep Neural Network[J]. Journal of Electronics & Information Technology, 2022, 44(11): 3874-3881. doi: 10.11999/JEIT220110

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Driven Non-linear Digital Self Interference Cancellation for In-Band Full Duplex Systems Based on Convolution Long Short-term Memory Deep Neural Network

doi: 10.11999/JEIT220110

1.
The 36th Research Institute of China Electronics Technology Group Corporation, Jiaxing 314033, China
2.
School of Electronics and Communication Engineering, Sun Yat-Sen University, Shenzhen 518107, China
3.
Unit 63891 of Chinese People’s Liberation Army, Luoyang 471000, China
4.
School of Systems Science and Engineering, Sun Yat-Sen University, Guangzhou 510275, China
5.
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621000, China

Funds: Guangdong Province Natural Science Foundation (2019A1515011622), The Natural Science Fonundation of Southwest University of Science and Technology (21zx7126)

Received Date: 2022-01-27
Rev Recd Date: 2022-06-29

Available Online: 2022-07-06

Publish Date: 2022-11-14

Abstract

Abstract

In-Band Full Duplex (IBFD) technology can effectively improve the spectral efficiency of wireless communication system, which has attracted extensive attention in recent years. However, the linear and nonlinear self interference caused by simultaneous transmission and reception brings great challenges to IBFD. The traditional nonlinear self interference cancellation is mainly based on polynomial model and Deep Neural Network(DNN). Polynomial-model-based method has the risk of deterioration of self-interference effect caused by model mismatch, while DNN-based method can not deal with the unique characteristics of space-frequency correlation and time correlation of high-dimensional data. Based on Convolution Long short-term memory Deep Neural Network(CLDNN), two network structures for reconstructing self-interference signals, Two-Dimensional CLDNN(2D-CLDNN) and Complex-Value-CLDNN(CV-CLDNN), are designed by introducing three-dimensional tensor in the input layer and setting complex convolution layer structure in the convolution layer, which makes full use of the advantages of local perception and weight sharing of convolutional neural network, so as to learn more abstract low-dimensional features from high-dimensional features, so as to improve the effect of self-interference cancellation. The evaluation results of the data obtained in the actual scene show that, when the memory length M of power amplifier and the multipath length L of self interference channel meet M+L=13, through a total of 60 training epochs, the structure proposed in this paper can achieve at least 26% improvement in nonlinear self-interference cancellation compared to the traditional DNN method, the training period is also significantly reduced.

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References(17)

References

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