A Network Selection Method Based on q-Learning Algorithm

Abstract

The invention discloses a network selection method based on a Q-learning algorithm. The network selection method includes the steps of (1) initializing a Q value table and setting a discount factor gamma and a learning rate alpha; (2) determining a business type k and load rates of two current networks when a set time is up so as to obtain a current state sn; (3) selecting a useable action from anaction set A and recording such action as well as next network state sn+1; (4) computing an immediate return function r according to the network state after the selected action is implemented; (5), updating a Q value function Qn (s, a), and gradually decreasing the learning rate alpha to 0 according to rules of an inverse proportional function; (6), repeating the steps (2)-(5) until Q values areconverged, in other words, a difference value of the Q values before and after updating is smaller than a threshold value; (7) returning to the step (3) to select the action and accessing to an optimal network. The network selection method based on the Q-learning algorithm is capable of decreasing a voice business block rate and a data service packet loss rate and increasing average network throughout.

Description

technical field [0001] The invention relates to a network selection method based on a Q learning algorithm, and belongs to the technical field of electric power wireless communication. Background technique [0002] At present, with the development of the power industry, the scale of the power grid is gradually expanding, and the network topology is becoming more and more complex. The current power communication backbone network based on optical fiber communication can no longer meet the needs of various services. For long-distance access nodes, directly laying optical fiber lines is expensive and lacks practical value; under sudden disasters, it is difficult to repair fiber optic line failures in time; Substation commissioning. In these fields where wired communication cannot play a key role, the application of wireless communication network technology and its networking system can provide high-quality communication guarantee. [0003] Therefore, wireless communication tec...

AI Technical Summary

Problems solved by technology

Although the multi-attribute decision-making method can consider network parameters more comprehensively, because it needs to determine the network weight in advance based on experience or expert judgment, this will lead to poor adaptability of this type of network selection algorithm to the network environment. The environment cannot dynamically select the appropriate access network

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