Dynamic resource allocation method for beam hopping satellite system based on deep reinforcement learning

A technology of satellite system and reinforcement learning, which is applied in the field of satellite communication, can solve problems such as co-channel interference without consideration, achieve the effect of reducing transmission delay and improving throughput

Pending Publication Date: 2022-05-13
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0004] However, existing deep reinforcement learning-based beam-hopping resource

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  • Dynamic resource allocation method for beam hopping satellite system based on deep reinforcement learning
  • Dynamic resource allocation method for beam hopping satellite system based on deep reinforcement learning
  • Dynamic resource allocation method for beam hopping satellite system based on deep reinforcement learning

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Embodiment Construction

[0074] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0075] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0076] Step 1. According to the characteristics of uneven temporal and spatial distribution of the beam-hopping GEO satellite system service, establish a service model of the forward link of the beam-hopping satellite system.

[0077] The forward link service model of the beam-hopping satellite system is as follows: figure 2 shown. In the beam-hopping satellite system, the ground wave position ψ is defined as ψ={c n |n=1,2,3,...,N}, where N represents the total number of ground waves, c n is the nth ground wave position, the maximum number of working beams is K, K≤N, and the beam-hopping period T is defined as T={t 1 ,t 2 ,...,t j ,...,t J}, where t j Indicates the jth beam-hopping slot, 1≤j≤J, J is the total number of beam-hopping slot...

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Abstract

The invention discloses a dynamic resource allocation method for a beam-hopping satellite system based on deep reinforcement learning. The method comprises the following steps: step 1, establishing a service model of a forward link of a beam-hopping GEO satellite system; step 2, storing a data packet of a service arriving at a ground wave position in each time slot in a data packet buffer queue; step 3, utilizing a degree reinforcement learning algorithm to model a resource allocation module of the satellite into an intelligent agent, and designing state input of the intelligent agent, an output decision action of the intelligent agent and an award of an evaluation action; 4, simulating the deep reinforcement learning algorithm in the step 3, and continuously training decision neural network weight parameters of the deep reinforcement learning algorithm; and step 5, completing dynamic allocation of the resources of the beam-hopping satellite system by using the decision neural network obtained by training in the step 4, and solving an optimal scheme of resource allocation of the beam-hopping satellite system. According to the invention, the transmission delay of the data packet is reduced, and the throughput of the beam-hopping satellite system is improved.

Description

technical field [0001] The invention relates to the field of satellite communication, in particular to a method for dynamically allocating resources of a beam-hopping satellite system based on deep reinforcement learning. Background technique [0002] In traditional multi-beam satellite systems, the power and frequency resources allocated to each beam are relatively fixed. However, because the service requests between beams are non-uniform and time-varying, traditional allocation algorithms cannot satisfy the service requests. Beam hopping (BH) technology is based on time slicing: only part of the beams are activated to work in the same time slot. The beam-hopping technology is driven by service requests and can greatly improve the utilization of system resources. At present, the resource allocation algorithms for the forward link of the beam-hopping satellite system mainly include heuristic algorithm, iterative algorithm and convex optimization algorithm. Both the heuris...

Claims

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Application Information

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IPC IPC(8): H04B7/185H04W16/10H04W16/28G06N3/08
CPCH04B7/18513H04B7/18519H04W16/10H04W16/28G06N3/08Y02D30/70
Inventor 张晨韩永锋张更新
Owner NANJING UNIV OF POSTS & TELECOMM
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