Communication and computation are often viewed as separate tasks. This
approach is very effective from the perspective of engineering as isolated
optimizations can be performed. On the other hand, there are many cases where
the main interest is a function of the local information at the devices instead
of the local information itself. For such scenarios, inf
该研究提出了一种基于过空中计算的分块机器学习系统,其中神经网络的不同分区由不同的计算节点执行,并演示了该系统在无线网络中的实现和解决方案。通过数学方法将神经网络的层间连接分解为线性预编码和组合转换。同时将预编码矩阵和组合矩阵与 MIMO 信道一起作为神经网络的完全连接层。该方案可以推广到传统的神经网络和卷积神经网络中,并通过模拟实验证明其有效性。在这种分块机器学习系统中,预编码和组合矩阵被视为可训练参数,而 MIMO 信道矩阵被视为未知(隐式)参数。