Over-the-Air Split Machine Learning in Wireless MIMO Networks
- URL: http://arxiv.org/abs/2210.04742v1
- Date: Fri, 7 Oct 2022 15:39:11 GMT
- Title: Over-the-Air Split Machine Learning in Wireless MIMO Networks
- Authors: Yuzhi Yang, Zhaoyang Zhang, Yuqing Tian, Zhaohui Yang, Chongwen Huang,
Caijun Zhong, and Kai-Kit Wong
- Abstract summary: In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes.
To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication.
- Score: 56.27831295707334
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In split machine learning (ML), different partitions of a neural network (NN)
are executed by different computing nodes, requiring a large amount of
communication cost. To ease communication burden, over-the-air computation
(OAC) can efficiently implement all or part of the computation at the same time
of communication. Based on the proposed system, the system implementation over
wireless network is introduced and we provide the problem formulation. In
particular, we show that the inter-layer connection in a NN of any size can be
mathematically decomposed into a set of linear precoding and combining
transformations over MIMO channels. Therefore, the precoding matrix at the
transmitter and the combining matrix at the receiver of each MIMO link, as well
as the channel matrix itself, can jointly serve as a fully connected layer of
the NN. The generalization of the proposed scheme to the conventional NNs is
also introduced. Finally, we extend the proposed scheme to the widely used
convolutional neural networks and demonstrate its effectiveness under both the
static and quasi-static memory channel conditions with comprehensive
simulations. In such a split ML system, the precoding and combining matrices
are regarded as trainable parameters, while MIMO channel matrix is regarded as
unknown (implicit) parameters.
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