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A joint beamforming and power control method for UAV assisted cellular network

A beamforming and power control technology, applied in the field of UAV-assisted communication, can solve the problems of slow convergence speed and high computational complexity, and achieve good practical application value

Pending Publication Date: 2021-04-23
BEIJING UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In terrestrial cellular communication systems, inter-cell joint beamforming and power control technologies can suppress interference better, but most of them are solved by traditional optimization methods, which have high computational complexity
For this reason, a reinforcement learning algorithm based on deep Q network (DQN) is proposed to calculate the UAV base station beamforming vector and power, but the calculation method has a slow convergence speed and affects its practical application

Method used

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  • A joint beamforming and power control method for UAV assisted cellular network
  • A joint beamforming and power control method for UAV assisted cellular network
  • A joint beamforming and power control method for UAV assisted cellular network

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Embodiment Construction

[0043] The implementation steps of the present invention are described below by experiments, and the parameters adopted in the experiments are

[0044] parameter value UAV base station maximum transmit power 40W UAV base station operating frequency 28GHz UAV base station spacing 1000m User terminal moving speed 2km / h Number of base station antennas {4,8,16,32,64} discount factor 0.995

[0045] Concrete realization steps of the present invention are:

[0046] Step (1): Initialize, set the state set s={s of the UAV base station 1 ,s 2 ,s 3 ,s 4}, where s 1 ,s 2 is the transmit power of the UAV base station in the cell and the interfering UAV base station, s 3 ,s 4 Be the beamforming vectors of the UAV base station and the interfering UAV base station in the cell, and the cell coverage radius is 1km;

[0047] The action set a is

[0048] a[0]=0: reduce the transmit power of the cell base station by 1dB

[0049] a[0]=...

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Abstract

The invention discloses a joint beam forming and power control method based on deep reinforcement learning, and provides a resource allocation method of joint beam forming and transmitting power based on UCB Duffing DQN deep reinforcement learning for an unmanned aerial vehicle auxiliary cellular communication system. In the algorithm learning process, the maximum confidence upper bound is used for selecting the optimal beam vector and transmitting power, the learning efficiency can be improved, convergence can be accelerated, and inter-cell interference can be inhibited to obtain the optimal system capacity.

Description

technical field [0001] The invention relates to the field of unmanned aerial vehicle assisted communication, and is an inter-cell interference management method combining beamforming and power control. Background technique [0002] With the rapid development of wireless communication technology, installing mobile communication equipment on unmanned aerial vehicle (UAV) makes the unmanned aerial vehicle (UAV) become an air mobile base station, which can form an air-ground integrated three-dimensional wireless communication network with the ground communication network, such as figure 1 , greatly expanding the application communication range of the mobile network. Aerial drones can also form a large-scale fleet to work collaboratively with the ground. This three-dimensional communication system can meet more business needs of future wireless networks. [0003] Compared with high-platform base stations and satellite communications, low-altitude UAVs support wireless mobile com...

Claims

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Application Information

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IPC IPC(8): H04B7/06H04B7/185H04W52/06H04W52/42H04J11/00G06N3/04G06N3/08
CPCH04B7/18504H04B7/0617H04W52/06H04W52/42H04J11/0056G06N3/08G06N3/084G06N3/045
Inventor 黎海涛张帅
Owner BEIJING UNIV OF TECH