# Channel Estimation for Orthogonal Time Frequency Space (OTFS) Massive MIMO

## TL;DR

This paper introduces a 3D structured orthogonal matching pursuit algorithm for downlink channel estimation in OTFS massive MIMO systems, effectively leveraging channel sparsity to improve accuracy and reduce pilot overhead in high-mobility scenarios.

## Contribution

It proposes a novel 3D sparsity-based channel estimation method tailored for OTFS massive MIMO, addressing the challenge of large antenna arrays in high-mobility environments.

## Key findings

- Achieves accurate channel estimation with low pilot overhead.
- Exploits 3D structured sparsity in OTFS MIMO channels.
- Outperforms traditional methods in high-mobility scenarios.

## Abstract

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.