信道估计 OTFS.zip

This simulation code package is mainly used to reproduce the results of the following paper [1]: [1] W. Shen, L. Dai, J. An, P. Fan, and R. W. Heath, ��Channel estimation for orthogonal time frequency space (OTFS) massive MIMO,�� IEEE Trans. Signal Process., 2019. ********************************************************************************************************************************* If you use this simulation code package in any way, please cite the original paper [1] above. The author in charge of this simulation code pacakge is: Wenqian Shen (email: shenwq1989@gmail.com). Reference: We highly respect reproducible research, so we try to provide the simulation codes for our published papers (more information can be found at: http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.html). Please note that the MATLAB R2014b is used for this simulation code package, and there may be some imcompatibility problems among different MATLAB versions. Copyright reserved by the Broadband Communications and Signal Processing Laboratory (led by Dr. Linglong Dai), Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. ********************************************************************************************************************************* Abstract of the paper: Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base station antennas. In this paper, we propose a 3D structured orthogonal matching pursuit algorithm based channel estimation technique to solve this problem. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed algorithm can achieve accurate channel state information with low pilot overhead. ********************************************************************************************************************************* Enjoy the reproducible research!