Research on Channel Feedback Algorithm in UAV Inspection Communication Subsystem of Smart Grid

2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) Pub Date : 2021-08-01 DOI:10.1109/ISCEIC53685.2021.00056

Zekun Huang, Minzheng Li

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引用次数: 2

Abstract

With the rapid development of smart grids, inspection and maintenance of power equipment is essential. The large bandwidth, high reliability and low latency communication of 5G technology can meet the needs of drone inspections. However, the environment of the transmission line corridor is complex and changeable. During the UAV inspection, the channel environment and the channel transmission coefficient between the UAV and the base station change in real time. In order to ensure the reliability and effectiveness of communication, the UAV terminal needs to estimate and track the changed channel transmission coefficient in real time, and feed it back to the base station. Based on the Massive-MIMO millimeter wave drone communication scenario, this paper constructs the channel model of the drone terminal and the base station in this scenario and solves the estimation, trackingand and feedback of the channel state information in this scenario by fusing deep learning algorithms problem.

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智能电网无人机巡检通信子系统信道反馈算法研究

随着智能电网的快速发展，对电力设备的检测和维护必不可少。5G技术的大带宽、高可靠性、低时延通信可以满足无人机巡检的需求。然而，输电线路走廊的环境是复杂多变的。在无人机巡检过程中，无人机与基站之间的信道环境和信道传输系数是实时变化的。为了保证通信的可靠性和有效性，无人机终端需要实时估计和跟踪变化的信道传输系数，并将其反馈给基站。本文基于Massive-MIMO毫米波无人机通信场景，构建了该场景下无人机终端与基站的信道模型，并通过融合深度学习算法解决了该场景下信道状态信息的估计、跟踪与反馈问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 2nd International Symposium on Computer Engineering and Intelligent Communications (ISCEIC)

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