论文标题
模型驱动的深度学习，用于大规模多源MIMO常数信封预编码
Model-Driven Deep Learning for Massive Multiuser MIMO Constant Envelope Precoding
论文作者
论文摘要
对于大量的多源多输入多输出系统，恒定的信封（CE）预编码设计引起了极大的兴趣，因为它可以大大降低硬件成本和功耗。但是，现有的CE预码算法受到过多的计算开销的阻碍。在这封信中，提出了将DL与共轭梯度算法相结合的新型模型驱动的深度学习（DL）网络。具体而言，原始迭代算法通过可训练的变量展开和参数化。借助提出的体系结构，可以通过无监督的学习方法从培训数据中有效地学习变量。因此，提议的网络学会获取搜索步骤大小并调整搜索方向。仿真结果证明了所提出的网络在多源干扰抑制能力和计算开销方面具有优势。
Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that combines DL with conjugate gradient algorithm is proposed for CE precoding. Specifically, the original iterative algorithm is unfolded and parameterized by trainable variables. With the proposed architecture, the variables can be learned efficiently from training data through unsupervised learning approach. Thus, the proposed network learns to obtain the search step size and adjust the search direction. Simulation results demonstrate the superiority of the proposed network in terms of multiuser interference suppression capability and computational overhead.
