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

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...

AI Technical Summary

Problems solved by technology

[0004] However, existing deep reinforcement learning-based beam-hopping resource allocation algorithms do not consider the co-channel interference between beams

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