论文标题
无细胞的大型MIMO系统中稀疏的大规模褪色解码
Sparse Large-Scale Fading Decoding in Cell-Free Massive MIMO Systems
论文作者
论文摘要
无单元的大规模多输入多输出（CF MMIMO）系统的特征是具有比用户设备（UES）更多的访问点（AP）。一个关键的挑战是确定哪些AP应该服务于哪些UES。以前的工作已经通过启发式解决了这一组合问题。本文提出了CF MMIMO的稀疏大规模褪色解码（LSFD）设计，以共同优化关联和LSFD。我们制定了组的稀疏问题，然后使用具有区块坐标下降的近端算法来解决它。数值结果表明，稀疏的LSFD达到的光谱效率几乎与最佳LSFD相同，因此由于降低了处理和信号，因此达到了更高的能效。
Cell-free massive multiple-input multiple-output (CF mMIMO) systems are characterized by having many more access points (APs) than user equipments (UEs). A key challenge is to determine which APs should serve which UEs. Previous work has tackled this combinatorial problem heuristically. This paper proposes a sparse large-scale fading decoding (LSFD) design for CF mMIMO to jointly optimize the association and LSFD. We formulate a group sparsity problem and then solve it using a proximal algorithm with block-coordinate descent. Numerical results show that sparse LSFD achieves almost the same spectral efficiency as optimal LSFD, thus achieving a higher energy efficiency since the processing and signaling are reduced.
