智能反射面赋能的联邦边缘学习及其在车联网中的应用

doi:10.11959/j.issn.1000-436x.2023192

通信学报 ›› 2023, Vol. 44 ›› Issue (10): 46-57.doi: 10.11959/j.issn.1000-436x.2023192

• 学术论文 • 上一篇

智能反射面赋能的联邦边缘学习及其在车联网中的应用

王平¹^,², 杨志伟¹, 李贺举¹

¹ 同济大学电子与信息工程学院，上海 201804
² 上海自主智能无人系统科学中心，上海 201210

修回日期:2023-09-10 出版日期:2023-10-01 发布日期:2023-10-01
作者简介:王平（1978− ），男，湖北黄冈人，博士，同济大学副教授、博士生导师，主要研究方向为车联网
杨志伟（1982− ），男，河南许昌人，同济大学博士生，主要研究方向为车路云协同智能驾驶
李贺举（1995−），男，安徽阜阳人，同济大学博士生，主要研究方向为无线通信和联邦学习
基金资助:
上海市科学技术委员会基金资助项目(21511102400)

Federated edge learning with reconfigurable intelligent surface and its application in Internet of vehicles

Ping WANG¹^,², Zhiwei YANG¹, Heju LI¹

¹ College of Electrical and Information Engineering, Tongji University, Shanghai 201804, China
² Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai 201210, China

Revised:2023-09-10 Online:2023-10-01 Published:2023-10-01
Supported by:
The Project of Shanghai Municipal Science and Technology Commission(21511102400)

摘要/Abstract

摘要：

针对无线链路和数据分布的异构性导致在FEEL训练中很难实现无线通信和模型精度的最佳权衡的问题，提出了一种智能反射面（RIS）赋能的空中联邦边缘学习系统，其利用智能反射面的信道可重构性自适应地配置信号传播环境，并利用空中计算实现联邦边缘学习模型的快速聚合。具体来说，首先刻画无线信道和数据异构影响下的联邦优化算法收敛行为，并以此构造统一的无线资源优化问题，通过联合设计收发端波束成形方案和RIS相移来优化学习性能。仿真结果验证了所提方案的有效性，并证明RIS可以在数据异构前提下提高空中联邦边缘学习系统准确性。最后，探讨了其在车联网中应用的可能性。

关键词: 联邦边缘学习, 数据异构, 智能反射面, 空中计算, 收敛分析, 车联网

Abstract:

Aiming at the problem that it is difficult to achieve an optimal trade-off between wireless communication and model accuracy in FEEL training caused by the heterogeneity of wireless links and data distribution, a reconfigurable intelligent surface (RIS) enabled federated edge learning system was proposed, which exploited the channel reconfigurability of RIS to adaptively manipulate the signal propagation environment, and utilized the over-the-air computation (Aircomp) to achieve fast model aggregation.Specifically, the convergence behavior of the FEEL algorithm under the influence of wireless channels and data heterogeneity was rigorously derived, and accordingly, an unified wireless resources optimization problem was constructed with the goal of minimizing the learning loss by jointly designing the transceiver design and the RIS phase shift.Simulation results demonstrate that the proposed scheme achieves substantial performance improvement compared against several baselines, and prove that RIS can play an important role in improving the accuracy of Aircomp enabled FEEL systems under data heterogeneity.Finally, the probability of applying it into Internet of vehicles is discussed.

Key words: federated edge learning, data heterogeneity, reconfigurable intelligent surface, over-the-air computation, convergence analysis, Internet of vehicles

中图分类号:

TN92

王平, 杨志伟, 李贺举. 智能反射面赋能的联邦边缘学习及其在车联网中的应用[J]. 通信学报, 2023, 44(10): 46-57.

Ping WANG, Zhiwei YANG, Heju LI. Federated edge learning with reconfigurable intelligent surface and its application in Internet of vehicles[J]. Journal on Communications, 2023, 44(10): 46-57.

图/表 9

图1

图2

表1

仿真参数"

参数	值
K	40
J	5
L	50
P₀/dB	-10
$σ_{n}^{2}$ /dB	-100
μ	1
E	1
τ	1
PL	3.76
f_c/MHz	915
G_BS/ dBi	5
G_D/dBi	0
G_DR/ dBi	5
d_x	$\frac{3 \times 10^{7}}{f_{c}}$
d_y	$\frac{3 \times 10^{7}}{f_{c}}$

表1

图3

图4

图5

图6

图7

图8

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智能反射面赋能的联邦边缘学习及其在车联网中的应用

Federated edge learning with reconfigurable intelligent surface and its application in Internet of vehicles

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