Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks
Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks
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DOI：
10.1109/twc.2022.3178445
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发表时间：
2022-05
影响因子：
10.4
通讯作者：
Bofu Yang;Xuelin Cao;Chongwen Huang;C. Yuen;M. D. Renzo;Yong Liang Guan;D. Niyato;Lijun Qian;M. Debbah
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文献类型：
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作者：
Bofu Yang;Xuelin Cao;Chongwen Huang;C. Yuen;M. D. Renzo;Yong Liang Guan;D. Niyato;Lijun Qian;M. Debbah
越来越多的人关注智能频谱感知，这就需要高效的训练和推理技术。本文提出了一种新的联邦学习(FL)框架，称为联邦频谱学习(FSL)，它利用了可重构智能表面(RISS)的优点，克服了深度衰落信道的不利影响。突出的是，我们通过在每个RIS控制器处利用完全训练的卷积神经网络(CNN)模型来赋予传统RIS频谱学习能力，从而帮助基站在每次训练迭代开始时协作地推断请求参与FL的用户。为了充分开发FL和RISS的潜力，我们解决了三个技术挑战：RISS相移配置、用户与RIS关联和无线带宽分配。由此产生的联合学习、无线资源分配和用户-RIS关联设计被描述为一个优化问题，其目标是最大化系统效用，同时考虑FL预测精度的影响。在这种背景下，FL预测的准确性与资源优化的性能相互作用。特别是，如果训练的CNN模型的准确性恶化，资源分配的性能就会恶化。利用真实的射频轨迹对提出的FSL框架进行了测试，数值结果表明该框架在频谱预测精度和系统实用性方面具有优势：在使用更多的RISS和反射元件时，可以获得更好的CNN预测精度和FL系统实用性。
Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.