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

TL;DR

This work proposes 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 that achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.

Abstract

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.

Figures (6)

• Figure 1: Sparsity pattern of the DDA domain channel model.
• Figure 2: Three cases of the set of the nearest elements.
• Figure 3: The JSPL recovery (on the right) and the true channel (on the left) vs. the sparsity degree $\lambda_{n}$ of the Doppler-angle domain channel matrix.
• Figure 4: NMSE of our JSPL and the baseline schemes at different speeds.
• Figure 5: NMSE of our JSPL and the baseline schemes with different pilot overhead $\sigma = \frac{\textrm{Number of resource blocks occupied by pilots}}{MN}$.