Research on Resource Scheduling of Distributed CNN Inference System Based on AirComp

LIU Qiaoshou; DENG Yifeng; HU Haonan; YANG Zhenwei

doi:10.11999/JEIT241022

Volume 47 Issue 7

Jul. 2025

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LIU Qiaoshou, DENG Yifeng, HU Haonan, YANG Zhenwei. Research on Resource Scheduling of Distributed CNN Inference System Based on AirComp[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2263-2272. doi: 10.11999/JEIT241022

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LIU Qiaoshou, DENG Yifeng, HU Haonan, YANG Zhenwei. Research on Resource Scheduling of Distributed CNN Inference System Based on AirComp[J]. Journal of Electronics & Information Technology, 2025, 47(7): 2263-2272. doi: 10.11999/JEIT241022

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Research on Resource Scheduling of Distributed CNN Inference System Based on AirComp

doi: 10.11999/JEIT241022 cstr: 32379.14.JEIT241022

LIU Qiaoshou^{1, 2, 3},
DENG Yifeng^{1, 2, 3
,
,},
HU Haonan¹,
YANG Zhenwei^{1, 2, 3}

1.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications Chongqing 400065, China
2.
Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China Chongqing 400065, China
3.
Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing 400065, China

Funds: The Major Project of Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M202400602, KJZD-K202200604), The General Project of Chongqing Natural Science Foundation (CSTB2024NSCQ-MSX0731)

Received Date: 2024-11-14
Rev Recd Date: 2025-03-16

Available Online: 2025-03-25

Publish Date: 2025-07-22

Abstract

Abstract

Objective In traditional AirComp systems, the computational accuracy is directly affected by the alignment of received signal phases from different transmitters. When applied to distributed federated learning and distributed inference systems, phase misalignment can introduce computational errors, reducing model training and inference accuracy. This study proposes the MOSI-AirComp system, in which transmitted signals in each computation round originate from the same node, thereby eliminating signal phase alignment issues. Methods (1) A dual-branch training model is proposed, increasing network complexity only during training. The traditional model is extended to a dual-branch structure, where the lower branch retains the original model, and the upper branch incorporates additional loss layers for training. (2) An MOSI-AirComp-based weight-power control scheme is introduced. Each node is equipped with multiple transmitting antennas and a single receiving antenna. Pre-trained model weights are offloaded to task nodes as part of the power control factor, which adjusts transmission power during inference. This optimization enhances signal amplitude for convolution operations while reducing computation time. Since data transmission originates from the same node, phase alignment issues are avoided. AirComp integrates signals from multiple antennas for convolution summation, enabling airborne convolution. (3) A TSP-based node selection algorithm is proposed, using weight mean and path as evaluation parameters to determine the optimal transmission path, ensuring efficient data transmission. Results and Discussions Compared to the traditional network model, the dual-branch training model significantly improves inference accuracy under small-scale fading. For the MNIST and CIFAR-10 datasets, accuracy increases by 2%～18% and 0.4%～11.2% under different SNR values (Fig. 5 and Fig. 6). The MSE decreases by 0.056～0.154 and 0.047～0.23 under different maximum node power budgets (Fig. 7). In noise-only scenarios, inference accuracy improves by 0.7%～5.5% and 0.3%～7.1% under different SNR values (Fig. 5 and Fig. 6), while MSE decreases by 0.035～0.152 and 0.056～0.253 under different maximum node power budgets (Fig. 8). Conclusions An MOSI-AirComp system is proposed to address the phase alignment issue inherent in traditional AirComp scenarios. The system enables airborne convolution through a power control scheme and enhances the traditional network model with a dual-branch structure. The upper branch simulates multiplicative Rayleigh fading using loss layers and incorporates model data into the convolution layer output of the lower branch to simulate additive noise effects. To account for node limitations in IoT networks, a model-weight-improved Traveling Salesman Problem (TSP) node selection algorithm is proposed. Future advancements in AirComp deployment for distributed computing and communication frameworks hold promise, particularly with the rapid development of 6G and IoT.
- AirComp,
- Distributed inference,
- Convolutional Neural Network (CNN),
- Power control

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References(16)

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