Method for selecting relay node in M2M communication based on reinforcement learning

Abstract

The invention discloses a method for selecting a relay node M2M communication based on reinforcement learning. The method comprises the following steps: S1, building a reinforcement learning model framework, and determining an action set, a state set and a reward value of reinforcement learning; S2, refining the reinforcement learning model framework; S3, performing iterative loop on the reinforcement learning model according to a specific problem; and S4, adjusting the reinforcement learning model according to a training result, and selecting the relay node by using the adjusted reinforcementlearning model. The energy consumption condition of each communication and the energy balance condition of the battery for supplying energy to the relay node are both brought into the reinforcement learning target, so that the system achieves balance in reducing the energy consumption and realizing the energy balance, the energy unbalance is avoided, and the service life of the whole system is prolonged.

Description

technical field [0001] The invention relates to a relay selection method, in particular to a method for selecting relay nodes in M2M communication based on reinforcement learning for battery-powered relay selection, and belongs to the technical field of wireless communication. Background technique [0002] In recent years, with the continuous development of computer technology, machine learning (Machine Learning) has gradually become the core technology of artificial intelligence, and has received extensive attention from many researchers in the industry. [0003] Specifically, according to whether there are labels in the sample data in its database, machine learning can be roughly divided into three types: supervised learning, unsupervised learning, and reinforcement learning. Among them, reinforcement learning is a learning method that takes environmental feedback as input and is guided by statistics and dynamic programming techniques. Reinforcement learning is inspired b...

AI Technical Summary

Problems solved by technology

[0006] When implementing these existing relay node selection methods, technicians found that relay nodes with better channel conditions in the entire wireless communication network will be frequently selected for use For information transmission, the energy of the battery powering it will be consumed faster than other relay nodes, resulting in an energy imbalance within the system and shortening the service life of the entire system

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