A resource allocation optimization method and system based on reinforcement learning

Abstract

The invention discloses a resource allocation optimization method based on reinforcement learning. The method comprises the following steps: acquiring a bandwidth value of a downlink, acquiring the number of physical resource blocks which can be called within a single transmission time interval according to the bandwidth value, and acquiring the number of user services to be transmitted, the characteristics of the user services to be transmitted on an nth physical resource block at the current t moment, and Characteristics of the whole downlink at the t-1 moment; judging whether the bandwidthutilization rate of the downlink needs to be improved; and if the bandwidth utilization rate of the downlink needs to be improved, whether the fairness of the downlink needs to be improved or not, orthe trade-off of the bandwidth utilization rate and the fairness of the downlink needs to be realized, and if the bandwidth utilization rate of the downlink needs to be improved, inputting the characteristics into the trained bandwidth utilization rate reinforcement learning model to obtain the metric value of the ith user service on the nth resource block. The technical problem that the scheduling performance is affected due to the fact that an existing algorithm only considers the local optimal solution condition caused by optimal allocation of a single resource block can be solved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and more specifically, relates to a resource allocation optimization method and system based on reinforcement learning. Background technique [0002] As a mainstream wireless communication network, a long term evolution (Long term evolution, LTE for short) network has been widely used at present. Downlink scheduling is a very important link in the existing LTE network. Currently, downlink scheduling methods commonly used mainly include Proportional fair (PF) algorithm, Max channel quality indicator (Max- CQI) algorithm, Modified Largest Weighted Delay First (M-LWDF for short) algorithm, and Exponential proportional fair (Exponentialproportional fair, EXP / PF for short) algorithm. [0003] However, the allocation strategy of the above-mentioned LTE downlink scheduling algorithm only considers the optimal allocation of a single resource block. Although it is possible to obtain the op...

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Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a resource allocation optimization method and system based on reinforcement learning. The local optimal solution situation caused by it will affect the technical problem of scheduling performance, and the present invention builds an allocation model based on historical allocation data, which has universal applicability and evolution

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