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.

翻译：本文提出了一种基于联合稀疏模式学习（JSPL）的信道估计方案，用于大规模多输入多输出（MIMO）正交时频空间（OTFS）调制辅助系统。通过挖掘时延-多普勒-角度（DDA）域信道的潜在联合稀疏性，将信道估计问题转化为稀疏恢复问题。为解决该问题，我们首先采用尖峰-厚尾先验模型迭代估计信道矩阵的支撑集，并在迭代过程中引入基于已识别支撑集的高精度参数更新规则。随后，利用正交匹配追踪（OMP）方法估计支撑集对应信道元素的具体数值。仿真结果与分析均表明，所提出的JSPL信道估计方案在降低导频开销的同时，相较于当前代表性基准方案实现了更优的性能。