Constructive interference (CI) precoding is a promising and efficient interference management scheme. However, the symbol-level operations required for CI precoding make this precoding scheme face a bottleneck of high computational complexity. To solve the above problem and make CI precoding applicable to high data rate transmission scenarios, in this letter, we propose a deep learning (DL)-based precoding design method driven by a CI communication model, and develop a CI neural network (CI-NN). By carefully designing a neural network with our customized loss function, the proposed scheme well meets the requirement of CI. Simultaneously, this scheme can realize user adaptive precoding according to the number of active users. The simulation results show that the proposed CI-NN can reduce time complexity effectively, while ensuring the performance of the communication model.

中文翻译：

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CI-NN：一种模型驱动的基于深度学习的建设性干扰预编码方案

相长干扰 (CI) 预编码是一种有前途且有效的干扰管理方案。但是，CI 预编码所需的符号级运算使该预编码方案面临计算复杂度高的瓶颈。为了解决上述问题，使 CI 预编码适用于高数据速率传输场景，在这封信中，我们提出了一种由 CI 通信模型驱动的基于深度学习（DL）的预编码设计方法，并开发了 CI 神经网络（CI- NN）。通过使用我们定制的损失函数精心设计神经网络，所提出的方案很好地满足了 CI 的要求。同时，该方案可以根据活跃用户数实现用户自适应预编码。仿真结果表明，所提出的 CI-NN 可以有效降低时间复杂度，