A channel estimation method using denoising autoencoder for large-scale asymmetric backscatter systems

• Published in: ICT express Vol. 10; no. 2; pp. 400 - 405
• Main Authors: Jung, Chae Yoon, Kang, Jae-Mo, Kim, Dong In
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2024; Elsevier; 한국통신학회
• Subjects: Backscatter communication; Beamforming; Channel estimation; Deep learning; Denoising autoencoder; 전자/정보통신공학; Deep learning; Denoising autoencoder; Beamforming; Channel estimation; Backscatter communication
• ISSN: 2405-9595; 2405-9595
• DOI: 10.1016/j.icte.2023.09.002

A novel channel estimation method based on deep learning algorithm is proposed for large-scale IoT networks. We consider asymmetric backscatter communication system to maintain low-power at sensor nodes. In order to obtain channel data, we design denoising autoencoder which consists of encoder with Feedforward Neural Network (FNN) and decoder with Convolutional Neural Network (CNN). Finally, the channel estimation error is minimized, while the pilots are optimized. Especially, we adopt beamforming technique that relies only on cascaded channel data to reduce complexity in multi-sensor system. It is shown that the accuracy is slightly degraded while the complexity is greatly reduced.