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.

中文翻译：

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用于大规模多用户 MIMO 恒定包络预编码的模型驱动深度学习

恒定包络 (CE) 预编码设计对大规模多用户多输入多输出系统非常感兴趣，因为它可以显着降低硬件成本和功耗。然而，现有的CE预编码算法受到过多计算开销的阻碍。在这封信中，提出了一种新的基于模型驱动的深度学习 (DL) 网络，该网络将 DL 与共轭梯度算法相结合，用于 CE 预编码。具体来说，原始迭代算法是通过可训练变量展开和参数化的。使用所提出的架构，可以通过无监督学习方法从训练数据中有效地学习变量。因此，所提出的网络学习获得搜索步长并调整搜索方向。