  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.
