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An intelligent allocation method for multi-base station network resources based on spatiotemporal feature extraction

A network resource and intelligent allocation technology, applied in biological neural network models, electrical components, neural architectures, etc., can solve problems such as inability to cope well, lack of flexibility and scalability, and improve user experience and service satisfaction rate The effect of improving and expanding the receptive field

Active Publication Date: 2022-04-12
ZHEJIANG LAB +1
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Problems solved by technology

[0006] 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

Method used

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  • An intelligent allocation method for multi-base station network resources based on spatiotemporal feature extraction
  • An intelligent allocation method for multi-base station network resources based on spatiotemporal feature extraction
  • An intelligent allocation method for multi-base station network resources based on spatiotemporal feature extraction

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Embodiment Construction

[0047] In order to describe in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with the accompanying drawings.

[0048] refer to figure 1 , is a flow chart of the multi-base station cooperative network resource allocation method based on time feature extraction and reinforcement learning of the present invention, specifically including the following steps:

[0049] S1. Algorithm network structure G and target network Build and initialize, including the following sub-steps:

[0050] S11. The algorithm network structure G of this method is divided into a state vector encoding network Embed, a long short-term memory network LSTM, a graph attention mechanism network GAT and a deep Q network DQN.

[0051] S12. The state vector encoding network is composed of two layers of fully connected networks, denoted as

[0052] h m =Embed(s m ) = σ(W e the s m +b e ),

...

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Abstract

The invention discloses a multi-base station network resource intelligent allocation method based on spatio-temporal feature extraction, extracting location information and spatial features of each 5G base station through a graph attention mechanism, and using a long-term and short-term memory mechanism to learn network user behavior habits and extract time features , to analyze the fluctuation of each slice data packet in time and space, 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 get higher system return, that is, higher spectral efficiency And a better user experience, while adapting to a dynamically changing environment, it is more flexible and robust.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a multi-base station network resource intelligent allocation method based on spatio-temporal feature extraction. Background technique [0002] At present, 5G network has become an indispensable key link in the development of digital society. Compared with 4G network, the massive services it provides can meet our wider needs, and most of them cannot be realized by 4G. [0003] The ITU defines three main application scenarios for 5G: enhanced mobile broadband (eMBB), massive machine-type communication (mMTC), ultra-reliable low-latency communication (ultra-reliable low- latency communication, URLLC). Among them, eMBB is mainly applied to services such as AR / VR due to its high bandwidth, mMTC is applied to services such as the Internet of Things and smart home due to its high connection density, and URLLC with low latency and high reliability can be applied to autono...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04W72/04H04L41/14G06N3/04
CPCH04L41/145G06N3/044G06N3/045H04W72/53
Inventor 李荣鹏肖柏狄郭荣斌赵志峰张宏纲
Owner ZHEJIANG LAB
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