Context learning-based massive terminal access control method for power internet of things

A technology for power Internet of Things and terminal access, applied in access restrictions, electrical components, advanced technologies, etc., can solve the problem that the base station cannot accurately obtain all information of massive terminals, and achieve the effect of ensuring access performance and avoiding waste of resources

Active Publication Date: 2021-05-14
NORTH CHINA ELECTRIC POWER UNIV (BAODING) +2
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Problems solved by technology

[0005] (2) Secondly, due to the limitation of network resources and signaling overhead, the base station cannot accurately obtain all the information of a large number of terminals, including terminal status, channel gain, queue backlog, etc., and it is necessary to formulate an uplink authorization strategy in the context of uncertain information

Method used

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  • Context learning-based massive terminal access control method for power internet of things
  • Context learning-based massive terminal access control method for power internet of things
  • Context learning-based massive terminal access control method for power internet of things

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Embodiment Construction

[0080] The present invention will be further described below according to specific embodiments.

[0081] As shown in Algorithm 1, the CLAC algorithm proposed by the present invention includes three stages: initialization stage (lines 2-3), decision stage (lines 5-15) and learning stage (lines 16-30).

[0082] Algorithm 1 CLAC Algorithm

[0083]

[0084]

[0085] The present invention has carried out simulation experiment to above-mentioned proposed CLAC algorithm, and has set three baseline algorithms and carried out the comparative verification of performance, and baseline algorithm is set as follows:

[0086] Baseline Algorithm 1: Energy Efficiency Priority Access Control Algorithm, which maximizes the total energy efficiency of the network based on the terminal state prediction algorithm, without considering the long-term constraints of access quality of service requirements.

[0087] Baseline Algorithm 2: An access control algorithm based on reinforcement learning, ...

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Abstract

The invention discloses an electric power Internet of Things mass terminal access control method based on context learning, which is applied to electric power Internet of Things mass terminal access control, and comprises three steps of constructing a system model, constructing a maximized network total energy efficiency model and designing an algorithm. According to the method, the uplink authorization strategy can be dynamically adjusted under the condition that information such as future channel state and terminal queue backlog does not need to be known in advance by combining rapid uplink authorization, reinforcement learning and a prediction algorithm, and the total energy efficiency of a network can be improved while the requirement of terminal access service quality is met.

Description

technical field [0001] The invention relates to the technical field of electric power Internet of Things, in particular to a method for controlling the access of massive terminals of electric power Internet of Things based on context learning. Background technique [0002] The power Internet of Things is an industrial-grade Internet of Things that realizes the interconnection of all things in the power system and human-computer interaction. Based on deep perception capabilities and advanced information and communication technologies, it can improve the level of precise control and intelligent dispatching of the power grid, and promote the traditional power system to the energy Internet. change. However, due to the limitation of wireless resources and computing resources, the concurrent access of massive terminals in the power Internet of Things has greatly increased the bearing pressure of the access network, leading to problems such as network congestion and overload, which...

Claims

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Application Information

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IPC IPC(8): H04W48/02G16Y10/35
CPCH04W48/02G16Y10/35Y02D30/70
Inventor 周振宇贾泽晗廖海君赵雄文张磊张素香
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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