A Transfer Learning Approach for Compressed Sensing in 6G-IoT
Jing Liang, Lanjun Li, Chenkai Zhao
Abstract
The data in the Internet of Things (IoT) and the sixth-generation (6G) wireless networks increase dramatically with higher dimensions compared to the traditional wireless networks. Compressed sensing (CS) has been adopted to effectively reduce the amount of transmitting signal with sparsity and recover accurately at the receiver. It has been proved that better recovery performance can be achieved via deep learning-based CS approaches. However, these methods require a mass of relevant data to train neural networks (NNs), not adapted for the case of small sample data. In this article, a convolution-based transfer learning CS (CTCS) model is proposed to reconstruct the compressed signal based on transfer learning. Ultrawide band (UWB) radar echo signal and Mnist hand-written data set are selected to evaluate the performance of CTCS. It is verified that the proposed model outperforms other traditional reconstruction algorithms in 6G-IoT under different noise levels, measurement numbers, and signal sparsities.