Yang, Y;
Zhang, Z;
Tian, Y;
Yang, Z;
Huang, C;
Zhong, C;
Wong, KK;
(2023)
Over-the-Air Split Machine Learning in Wireless MIMO Networks.
IEEE Journal on Selected Areas in Communications
, 41
(4)
pp. 1007-1022.
10.1109/JSAC.2023.3242701.
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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. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of OAC split ML system is first provided, and 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 a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional neural networks. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.
| Type: | Article |
|---|---|
| Title: | Over-the-Air Split Machine Learning in Wireless MIMO Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/JSAC.2023.3242701 |
| Publisher version: | https://doi.org/10.1109/JSAC.2023.3242701 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | MIMO communication , Artificial neural networks , Wireless communication , Precoding , Transmitting antennas , Training , Receiving antennas |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10167300 |
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