Capacity optimization method and device for RIS auxiliary communication system carried by unmanned aerial vehicle

An auxiliary communication and system capacity technology, applied in the field of mobile communication, can solve problems such as no perfect solution, achieve the effect of optimizing economic benefits and realizing system capacity

Active Publication Date: 2022-04-29
UNIV OF SCI & TECH BEIJING
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The present invention provides a method and device for optimizing the capacity of a UAV-equipped RIS auxiliary communication system to solve the problem of optimizing the system capacity of a UAV-equipped RIS auxiliary communication system. There is currently no perfect solution to the technical problem

Method used

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  • Capacity optimization method and device for RIS auxiliary communication system carried by unmanned aerial vehicle
  • Capacity optimization method and device for RIS auxiliary communication system carried by unmanned aerial vehicle
  • Capacity optimization method and device for RIS auxiliary communication system carried by unmanned aerial vehicle

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no. 1 example

[0043] This embodiment provides a method for optimizing the capacity of a UAV-equipped RIS-assisted communication system, which is mainly used for the non-orthogonal multiple access (NOMA) downlink assisted by an unmanned aerial vehicle (UAV) equipped with a smart reflective surface (RIS). In road scenarios, the purpose is to change the trajectory optimization and energy constraints of traditional UAV assisted communication, introduce cutting-edge RIS technology, and provide a UAV trajectory and RIS beamforming matrix optimization based on deep reinforcement learning for UAV equipped with RIS assisted communication. method. Mainly in the scenario where UAV is equipped with RIS for auxiliary communication, by simultaneously optimizing the UAV trajectory and RIS beamforming matrix, and using the dual deep Q network (DDQN) algorithm in deep reinforcement learning (DRL) to learn, so as to meet the needs of the community The minimum target data rate of internal users is used to max...

no. 2 example

[0086] This embodiment provides a device for optimizing the capacity of an auxiliary communication system equipped with a RIS on a UAV. The device for optimizing the capacity of an auxiliary communication system equipped with an RIS on a UAV includes the following modules:

[0087] The communication system modeling module is used to use UAV UAV equipped with RIS as a relay to construct a UAV equipped with RIS auxiliary communication system;

[0088] The problem description module is used to transform the system capacity optimization problem of UAV equipped with RIS auxiliary communication system into the UAV trajectory and RIS beamforming matrix optimization problem with user rate guarantee and UAV energy consumption constraints;

[0089] The deep reinforcement learning module is used to obtain the optimal UAV trajectory and the optimal RIS beamforming matrix based on the deep reinforcement learning algorithm, so as to maximize the system capacity while satisfying the user's mi...

no. 3 example

[0092] This embodiment provides an electronic device, which includes a processor and a memory; at least one instruction is stored in the memory, and the instruction is loaded and executed by the processor, so as to implement the method of the first embodiment.

[0093] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) and one or more memories, wherein at least one instruction is stored in the memory, so The above instruction is loaded by the processor and executes the above method.

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Abstract

The invention discloses an unmanned aerial vehicle (UAV)-carried RIS auxiliary communication system capacity optimization method and device, and the method comprises the steps: taking a UAV-carried RIS as a relay, and constructing a UAV-carried RIS auxiliary communication system; converting a system capacity optimization problem of the UAV carrying the RIS auxiliary communication system into a UAV trajectory and RIS beam forming matrix optimization problem with user rate guarantee and UAV energy consumption constraint; based on a deep reinforcement learning algorithm, an optimal UAV trajectory and an optimal RIS beamforming matrix are obtained, and system capacity maximization is achieved under the condition that the minimum target data rate of a user and UAV energy consumption constraints are met. According to the method, the track and phase shift problems are solved, and meanwhile, the system capacity can be maximized under the energy consumption constraint.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to a method and device for optimizing the capacity of an auxiliary communication system equipped with an intelligent reflective surface (RIS) on an unmanned aerial vehicle. Background technique [0002] RIS is an innovative transmission technology applied to wireless communication. RIS is conceived as an inexpensive, adaptive thin sheet of composite material that can be programmed to modify radio waves. It can be deployed on scenes such as walls or buildings. Due to its programmability, RIS can be reconfigured after deployment in a wireless environment. Typically, a RIS is a planar array whose surface consists of many metamaterial components. Each element is reconfigurable and the phase shift can be changed independently. By adjusting the phase of each element, the convergence of the signal received by the RIS to the target direction is optimized, thereby increasing...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L41/0823H04L41/14H04W16/28G06N3/08
CPCH04L41/0823H04L41/145H04W16/28G06N3/08B64U2101/20Y02D30/70
Inventor 张海君黄庙林王先梅隆克平王健全李卫云翔
Owner UNIV OF SCI & TECH BEIJING
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