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
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[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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