# Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems

arXiv: https://arxiv.org/abs/2403.03771 · HTML: https://mlsanity.com/paper/2403.03771/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 2 of 2)

- [Uplink-aided High Mobility Downlink Channel Estimation over Massive MIMO-OTFS System](https://mlsanity.com/paper/2003.07045/) (2020)
- [OTFS Channel Estimation And Data Detection Designs With Superimposed Pilots](https://mlsanity.com/paper/2010.15066/) (2020)

## Abstract (arXiv)

We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.