论文标题
基于MIMO的神经网络和基于星座的激活，无线拆分学习
Over-the-Air Split Learning with MIMO-Based Neural Network and Constellation-Based Activation
论文作者
论文摘要
本文研究了多输入多输出（MIMO）通信系统的通信效率分裂学习（SL）。特别是，我们使用数学分解了神经网络（NN）与使用空中计算（OAC）的一系列线性预编码和组合转换的层间连接，该计算在NNS中协同形成线性层。在该系统中，预编码和组合矩阵是可训练的参数，而MIMO通道是隐式的。拟议的系统通过利用频道互惠并正确地施放反向传播过程，消除了隐式渠道估计，从而大大节省了系统成本并进一步提高了整体效率。实际的星座图用作激活函数，以避免像传统的OAC系统一样发送任意模拟信号。说明了数值结果以证明所提出的方案的有效性。
This paper investigates a communication-efficient split learning (SL) over multiple-input multiple-output (MIMO) communication system. In particular, we mathematically decompose the inter-layer connection of a neural network (NN) to a series of linear precoding and combining transformations using over-the-air computation (OAC), which synergistically form a linear layer in NNs. The precoding and combining matrices are trainable parameters in such a system, whereas the MIMO channel is implicit. The proposed system eliminates the implicit channel estimation through exploiting the channel reciprocity and properly casting the backpropagation process, significantly saving the system costs and further improving the overall efficiency. The practical constellation diagrams are used as the activation function to avoid sending arbitrary analog signals as in the traditional OAC system. Numerical results are illustrated to demonstrate the effectiveness of the proposed scheme.
