It is a challenge to apply the precoder design maximizing the mutual information with finite-alphabet inputs to practical multiple-input multiple-output (MIMO) systems, because it needs to iteratively solve an optimization problem, which is difficult to satisfy the requirement of real time. This letter develops a deep learning based precoding scheme, which employs the property of deep neural network (DNN) as approximator of functions. Simulation results show that a DNN can accurately learn the input-output relationship of a nearly optimal precoder achieved by the traditional interior-point method (IPM); moreover, in different MIMO scenarios, a trained DNN of small size offers almost the same performance as the nearly optimal precoder, but with huge improvement in efficiency, especially in cases of higher modulation and more antennas. The improved efficiency makes it possible to be applied to practical communication systems.

中文翻译：

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具有有限字母输入的 MIMO 系统中基于深度学习的预编码器设计

将具有有限字母输入的互信息最大化的预编码器设计应用于实际的多输入多输出 (MIMO) 系统是一个挑战，因为它需要迭代解决一个优化问题，难以满足真实的需求。时间。这封信开发了一种基于深度学习的预编码方案，该方案利用深度神经网络 (DNN) 的特性作为函数的逼近器。仿真结果表明，DNN可以准确地学习传统内点法（IPM）实现的近乎最优预编码器的输入输出关系；此外，在不同的 MIMO 场景中，经过训练的小规模 DNN 提供与近乎最优的预编码器几乎相同的性能，但效率有了巨大的提高，尤其是在更高调制和更多天线的情况下。