Energy-efficient Predictive Deployment Strategy of UAVs Based on ConvLSTM with Attention Mechanism

TANG Lun; PU Hao; WANG Zhiping; WU Zhuang; CHEN Qianbin

doi:10.11999/JEIT211368

Volume 44 Issue 3

Mar. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(3): 960-968

TANG Lun, PU Hao, WANG Zhiping, WU Zhuang, CHEN Qianbin. Energy-efficient Predictive Deployment Strategy of UAVs Based on ConvLSTM with Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(3): 960-968. doi: 10.11999/JEIT211368

Citation:

TANG Lun, PU Hao, WANG Zhiping, WU Zhuang, CHEN Qianbin. Energy-efficient Predictive Deployment Strategy of UAVs Based on ConvLSTM with Attention Mechanism[J]. Journal of Electronics & Information Technology, 2022, 44(3): 960-968. doi: 10.11999/JEIT211368

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Energy-efficient Predictive Deployment Strategy of UAVs Based on ConvLSTM with Attention Mechanism

doi: 10.11999/JEIT211368

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), Sichuan and Chongqing Key R&D Projects (2021YFQ0053)

Received Date: 2021-11-30
Accepted Date: 2022-02-28
Rev Recd Date: 2022-02-28

Available Online: 2022-03-02

Publish Date: 2022-03-28

Abstract

Abstract

Unmanned Aerial Vehicle (UAV) can be used as the air base station to cover flexibly hotspots by its mobility. However, it is challenging for the network operators to forecast the distribution of network traffic and optimize the deployment of UAVs. To solve this problem, an energy-efficient predictive deployment strategy of UAVs based on ConvLSTM with Attention mechanism (A-ConvLSTM) is proposed: a convolutional long short term memory deep spatio-temporal network model A-ConvLSTM with attention mechanism is proposed to forecast the spatio-temporal distribution of users and cellular traffic. Then based on the forecast, the coverage and locations of UAVs are optimized. On the premise of meeting the requirements of user access rate, an optimization formulation is established with the goal of minimizing the transmission power of UAVs. The formulation is decoupled into two subproblems and an energy-efficient deployment algorithm is proposed for iterative solution. The experimental results show that the performance of A-ConvLSTM is better than that of each baseline model. Energy-efficient deployment algorithm can effectively reduce the transmission power consumption of UAVs, and achieve the overall area coverage with fewer UAVs.
- Unmanned Aerial Vehicle (UAV),
- Deep spatio-temporal network,
- Traffic forecast,
- Energy-efficient deployment of UAV

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References(18)

References

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