论文标题
自适应调制的符号级预码和检测的端到端学习
End-to-End Learning for Symbol-Level Precoding and Detection with Adaptive Modulation
论文作者
论文摘要
常规的符号级预码（SLP）设计在接收器上假设固定的调制规则和检测规则，以简化发送预编码的优化，这极大地限制了SLP的灵活性和通信质量服务质量（QOS）。为了克服这些方法的性能瓶颈，在这封信中，我们提出了一种基于端到端学习的方法，以共同优化调制订单，发送预编码和SLP通信系统的接收检测。开发了由调制顺序预测（MOP-NN）模块和符号级的预编码和检测（SLPD-NN）模块组成的神经网络，以解决此数学上棘手的问题。模拟验证了拟议的端到端学习方法带来的显着性能改善。
Conventional symbol-level precoding (SLP) designs assume fixed modulations and detection rules at the receivers for simplifying the transmit precoding optimizations, which greatly limits the flexibility of SLP and the communication quality-of-service (QoS). To overcome the performance bottleneck of these approaches, in this letter we propose an end-to-end learning based approach to jointly optimize the modulation orders, the transmit precoding and the receive detection for an SLP communication system. A neural network composed of the modulation order prediction (MOP-NN) module and the symbol-level precoding and detection (SLPD-NN) module is developed to solve this mathematically intractable problem. Simulations verify the notable performance improvement brought by the proposed end-to-end learning approach.
