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

Abstract

The invention discloses a multi-base-station cooperative wireless network resource allocation method based on graph attention mechanism reinforcement learning. The method comprises the following steps: constructing and initializing an algorithm network structure G and a target network; executing resource allocation; repeating the resource allocation in the step 2, and training an algorithm networkstructure G; assigning a weight parameter of the algorithm network structure G to the target network every time the algorithm network structure G in the step 3 is trained for X times, thereby realizing updating of the target network; and after the step 3 is executed, completing the training process of the algorithm network structure G after executing for several times. The internal relation between the subjects is obtained through the graph attention mechanism, the fluctuation situation of each slice data packet in space-time is analyzed, and compared with a resource allocation strategy basedon an optimization algorithm and a genetic algorithm and a resource allocation strategy based on traditional reinforcement learning, higher system return can be obtained, namely higher spectral efficiency and better user experience are obtained, and the method can be adapted to a dynamically changing environment, and is more flexible and robust.

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...

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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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