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.

提出了一种基于联合稀疏模式学习（JSPL）的信道估计方案，适用于大规模多输入多输出（MIMO）正交时频空（OTFS）调制辅助系统。通过利用延迟-多普勒-角（DDA）领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，并采用脉冲和板条先验模型迭代估计信道矩阵的支撑集，同时引入了一个依赖于已识别支撑集的更高精度参数更新规则。然后，通过正交匹配追踪（OMP）方法估计支撑集对应的信道元素的具体值。无论减少的导频开销，我们的模拟结果和分析均表明，所提出的JSPL信道估计方案在性能上优于代表性的现有基准方案。

基于联合稀疏模式学习的大规模MIMO-OTFS系统信道估计