提出MOMPnet框架，解决大规模MIMO中稀疏恢复的可靠性与复杂度问题。
Physically constrained unfolded multi-dimensional OMP for large MIMO systems
- 融合深度展开与数据驱动字典学习，提升物理模型适应性。
- 采用多维小字典设计，降低算法复杂度，实测性能优于多个基线。
- 适合通信系统中通道估计与定位任务，尤其适用于硬件不完美场景。
稀疏恢复方法在现代通信系统中的信道估计和定位中至关重要，但其可靠性依赖于精确的物理模型，而这些模型通常无法完全确定。同时，在大规模MIMO系统中，字典维度增加导致计算复杂度急剧上升。本文提出MOMPnet，一种新型的展开式稀疏恢复框架，旨在同时解决传统方法在可靠性和复杂度方面的挑战。通过将深度展开与数据驱动字典学习相结合，MOMPnet可缓解硬件损伤并保持可解释性。不同于单一的大字典，该方法采用多个独立的小字典，支持低复杂度的多维正交匹配追踪算法。所提出的展开网络在真实信道数据上与多个基线进行对比评估，表现出优异性能，展现出巨大应用潜力。
原文摘要 · Abstract (English)
Sparse recovery methods are essential for channel estimation and localization in modern communication systems, but their reliability relies on accurate physical models, which are rarely perfectly known. Their computational complexity also grows rapidly with the dictionary dimensions in large MIMO systems. In this paper, we propose MOMPnet, a novel unfolded sparse recovery framework that addresses both the reliability and complexity challenges of traditional methods. By integrating deep unfolding with data-driven dictionary learning, MOMPnet mitigates hardware impairments while preserving interpretability. Instead of a single large dictionary, multiple smaller, independent dictionaries are employed, enabling a low-complexity multidimensional Orthogonal Matching Pursuit algorithm. The proposed unfolded network is evaluated on realistic channel data against multiple baselines, demonstrating its strong performance and potential.
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