The hybrid precoding structure composed of analog and digital precoders has received increasing attention in mmWave massive multiple-input multiple-output (MIMO) systems because it can balance the energy consumption and spectral efficiency (SE). However, it is challenging to obtain the optimal hybrid precoding scheme by joint optimization with lower computational complexity. This letter proposes a hybrid precoding scheme based on model-driven deep learning and alternate minimization (MDL-AltMin), which is implemented by alternately solving analog precoder and digital precoder. During the alternation, we design an analog precoding network (AP-Net) to solve the phase shift network in analog precoder with the goal of maximizing SE. The digital precoder is solved by the Lagrange multiplier method. In each alternate optimization process, the criteria for convergence is to minimize the error between the hybrid precoder and the optimal fully digital precoder. The simulation results show that the SE of our proposed scheme is very close to the fully digital precoding scheme based on singular value decomposition with lower computational complexity.

中文翻译：

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MDL-AltMin：采用深度学习和交替优化的毫米波系统混合预编码方案


由模拟和数字预编码器组成的混合预编码结构由于可以平衡能耗和频谱效率（SE），在毫米波大规模多输入多输出（MIMO）系统中受到越来越多的关注。然而，通过联合优化以较低的计算复杂度获得最优的混合预编码方案具有挑战性。这封信提出了一种基于模型驱动的深度学习和交替最小化（MDL-AltMin）的混合预编码方案，通过交替求解模拟预编码器和数字预编码器来实现。在交替过程中，我们设计了一个模拟预编码网络（AP-Net）来解决模拟预编码器中的相移网络，其目标是最大化SE。数字预编码器通过拉格朗日乘子法求解。在每个替代优化过程中，收敛的标准是最小化混合预编码器和最优全数字预编码器之间的误差。仿真结果表明，我们提出的方案的SE非常接近基于奇异值分解的全数字预编码方案，并且计算复杂度较低。