Semantic Scholar Open Access 2018 656 sitasi

Deep Learning-Based Channel Estimation

Mehran Soltani V. Pourahmadi A. Mirzaei H. Sheikhzadeh

Lihat Sumber DOI

Abstrak

In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

Topik & Kata Kunci

Computer Science Mathematics

Penulis (4)

Mehran Soltani

V. Pourahmadi

A. Mirzaei

H. Sheikhzadeh

Format Sitasi

APA MLA BibTeX

Soltani, M., Pourahmadi, V., Mirzaei, A., Sheikhzadeh, H. (2018). Deep Learning-Based Channel Estimation. https://doi.org/10.1109/LCOMM.2019.2898944

Akses Cepat

Lihat di Sumber doi.org/10.1109/LCOMM.2019.2898944

Informasi Jurnal

Tahun Terbit: 2018
Bahasa: en
Total Sitasi: 656×
Sumber Database: Semantic Scholar
DOI: 10.1109/LCOMM.2019.2898944
Akses: Open Access ✓