Channel Estimation for MIMO-OTFS Satellite Communication: a Deep Learning-Based Approach

Yuan Gao, Xinchen Xu, Bintao Hu, Jianbo Du, Wenrui Yang, Yanliang Jin

Abstract: The orthogonal time frequency space (OTFS) modulation has garnered significant attention due to its potential to combat the frequency Doppler effect in high-mobility scenarios, especially in the low earth orbit (LEO) satellite communication. However, facilitating the OTFS in multiple-input and multipleoutput (MIMO) satellite communication requires accurate channel state information, which is a challenging task. To this end, we propose a novel deep-learning-based framework to enhance the channel estimation accuracy in a MIMO satellite communication network by exploiting the channel correlation of the OTFS-MIMO channels. Through extensive simulations, our proposed framework shows an impressive capability to predict MIMO-OTFS channels with remarkable accuracy enhancement. The effect of dataset and data distribution on the channel estimation accuracy and generalization are also revealed.

External IDs:dblp:conf/icccn/GaoXHDYJ24