Beam hopping resource allocation method and system based on deep reinforcement learning, storage medium and equipment

A resource allocation and reinforcement learning technology, applied in the field of beam hopping resource allocation, can solve the problems of lack of continuity, poor performance of different traffic delays, etc., achieve moderate computational complexity, solve dynamic resource management problems, and good algorithm performance Effect

Active Publication Date: 2021-10-29
HARBIN INST OF TECH +1
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  • Application Information

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Problems solved by technology

[0005] In order to solve the problem that the existing beam-hopping satellite communication system has poor time-delay performance due to the lack of continuity when the service scene is constantly changing during resource allocation, the present invention implements a beam-hopping resource allocation method based on reinforcement learning. Research

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  • Beam hopping resource allocation method and system based on deep reinforcement learning, storage medium and equipment
  • Beam hopping resource allocation method and system based on deep reinforcement learning, storage medium and equipment
  • Beam hopping resource allocation method and system based on deep reinforcement learning, storage medium and equipment

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specific Embodiment approach 1

[0039] This embodiment is a beam-hopping resource allocation method based on deep reinforcement learning, including the following steps:

[0040] 1. Build a beam-hopping satellite communication system model as the basis for analyzing the beam-hopping resource allocation algorithm:

[0041] The schematic diagram of the beam-hopping satellite communication scenario is as follows: figure 1 As shown, the satellite provides K beams to cover N cells in total C={c n |n=1,2,…,N}, c n which is figure 1 In the cell, the satellite has the function of beam hopping. Assuming that the traffic requests of each cell are expressed in the form of data packets, the size of each data packet is Mbit, and the subject arrival rate is The Poisson distribution of , where is t j time zone c n The arrival rate of ; there is a buffer on the star, and the data packets in the buffer are in means t j time zone c n The number of buffered data packets; the maximum effective delay of data packet...

specific Embodiment approach 2

[0086] This embodiment is a beam-hopping resource allocation system based on deep reinforcement learning, and the system is used for the above-mentioned beam-hopping resource allocation method based on deep reinforcement learning.

specific Embodiment approach 3

[0087] This embodiment is a storage medium, where at least one instruction is stored, and the at least one instruction is loaded and executed by a processor to implement the method for allocating beam-hopping resources based on deep reinforcement learning.

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Abstract

The invention discloses a hopping beam resource allocation method and system based on deep reinforcement learning, a storage medium and equipment, and belongs to the technical field of communication. In order to solve the problem that the time delay performance of different service volumes is poor due to the fact that an existing beam-hopping satellite communication system lacks continuity when a service scene changes continuously during resource allocation, ground service requests are divided into a real-time data service and a non-real-time data service, and optimization functions are established respectively; then the maximum effective time length Tth of data in the satellite buffer is divided into M equal-length segments, and the M equal-length segments correspond to M hopping beam time slots; a ground cell service volume request formed by data packet time delay, the number of real-time data packets and non-real-time data packets is taken as an environment state S, a satellite beam is taken as an intelligent agent, cell illumination is taken as an action, and an optimization problem of resource allocation in a satellite beam hopping technology is taken as a Markov decision process; and hopping beam resource allocation is conducted based on the deep Q network. The method and system is mainly used for allocating hopping beam resources.

Description

technical field [0001] The invention relates to a method for allocating beam hopping resources, which belongs to the technical field of communications. Background technique [0002] Satellite communication has the characteristics of wide coverage area, large communication capacity, good transmission quality, rapid networking, and is not affected by geographical and climate environments. Despite the rapid development of land mobile communication systems and network scale, in vast and sparsely populated areas and areas with harsh natural environments, it is still necessary to rely on the unique technical characteristics of satellite communications to provide communication services and cooperate with land mobile communication networks to form a world The Internet achieves seamless global coverage. The satellite communication system is a typical resource-constrained system. The limited payload and spectrum resources on the satellite are the key factors that limit the developmen...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04B7/185G06N3/04G06N3/08
CPCH04B7/18513H04B7/18519G06N3/08G06N3/045
Inventor 杨明川窦映喆焦利彬薛冠昌谢冰玉
Owner HARBIN INST OF TECH
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