摘要
在传统AirComp系统中,汇聚节点接收到来自不同发送端的信号相位是否严格对齐将直接影响Air-Comp的计算精度,将AirComp引入分布式联邦学习和分布式推理系统中,由于相位对齐问题造成的计算误差则会导致模型训练精度和推理精度下降。目前,现有的AirComp分布式联邦学习和分布式推理系统,无论在训练还是推理过程中,基本上都未考虑信道对模型性能的影响,导致其推理精度远低于本地训练和推理的结果,这一点在低信噪比时表现得尤为突出。该文提出了一种MOSI-AirComp系统,其中同一轮参与计算的发射信号来自同一节点,因此可以忽略信号的相位对齐问题。此外,该文设计了一种双支路训练模型,上支路基于原始模型的基础上添加Loss层模拟信道干扰,而下支路保持原始的网络模型结构用于推理任务,以实现更好的抗衰落和抗噪声能力。该文还提出了一种基于权重的功率控制方案和路径选择算法,根据节点间距离和模型权重选择最优的传输回路,并将模型权重作为功率控制因子的一部分来调节传输功率,以此实现卷积过程中的乘法操作,同时利用Air-Comp的叠加特性完成加法操作,从而实现空中卷积。仿真结果证明了MOSI-AirComp系统的有效性。与传统模型相比,双支路训练模型在小尺度衰落场景下,MNIST数据集和CIFAR10数据集在不同信噪比下的推理精度分别提高了2%~18%和0.4%~11.2%。
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.
作者
刘乔寿
邓义锋
胡昊南
杨振巍
LIU Qiaoshou;DENG Yifeng;HU Haonan;YANG Zhenwei(School of Communications and Information Engineering,Chongqing University of Posts and Telecommunications Chongqing 400065,China;Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commis-sion of China Chongqing 400065,China;Chongqing Key Laboratory of Ubiquitous Sensing and Networking Chongqing 400065,China)
出处
《电子与信息学报》
北大核心
2025年第7期2263-2272,共10页
Journal of Electronics & Information Technology
基金
重庆市教委科学技术研究计划重大项目(KJZD-M202400602,KJZD-K202200604)
重庆市自然科学基金面上项目(CSTB2024NSCQ-MSX0731)。
