A multi-base station cooperative wireless network resource allocation method based on graph attention mechanism reinforcement learning

Abstract

The invention discloses a multi-base station collaborative wireless network resource allocation method based on graph attention mechanism reinforcement learning. The method includes: building and initializing algorithmic network structure G and target network; performing resource allocation; repeating step 2 for resource allocation times, Train the algorithmic network structure G; each time the algorithmic network structure G is trained X times in step 3, assign the weight parameters of the algorithmic network structure G to the target network to realize the update of the target network; after step 3 is executed once, complete the training of the algorithmic network structure G process. Obtain the internal relationship between subjects through the graph attention mechanism, and analyze the fluctuation of each slice data package in space and time. Compared with the resource allocation strategy based on optimization algorithm and genetic algorithm and the resource allocation strategy based on traditional reinforcement learning, it can be obtained Higher system returns, that is, higher spectral efficiency and better user experience, and can adapt to dynamically changing environments, with more flexibility and robustness.

Description

technical field [0001] The present invention relates to a multi-base station cooperative network resource allocation method and the field of reinforcement learning, more specifically, to a multi-base station cooperative wireless network resource allocation method based on graph attention mechanism reinforcement learning, and belongs to the technical field of wireless communication. Background technique [0002] Faced with the rapid growth of mobile data traffic, the fifth generation (5G) mobile communication network needs to provide network services with different performances for diverse business scenarios from different subscribers. The three core application scenarios are: (a) enhanced Enhanced mobile broadband (eMBB) is used to provide users with stable and high-peak data transmission rates to meet typical services such as 4k / 8k HD, AR / VR, and holographic images; (b) massive machine communication (massivemachine -type communications (mMTC), used to provide services for l...

AI Technical Summary

Problems solved by technology

[0004] Traditional dedicated resource allocation schemes and resource allocation strategies based on optimization algorithms and heuristic algorithms often have strict restrictions and complex derivations to form specific optimization problems. Such methods lack flexibility and scalability. When user characteristics and The proportion of users with various performances changes, and these algorithms cannot cope well

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