Context-aware learning-based access control method for power IoT - Zhenyu ZHOU; Zehan JIA; Haijun LIAO; Xiongwen ZHAO; Lei ZHANG (Tongxin xuebao)
In view of the problems of severe access conflicts, high queue backlog, and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era, a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was based on reinforcement learning and fast uplink grant technology, considering active state and dormant state of terminals, and the optimization objective was to maximize the total network energy efficiency under the long-term constraint of terminal access service quality requirements.Lyapunov optimization was used to decouple the long-term optimization objective and constraint, and the long-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems, which could be solved by the terminal state-aware upper confidence bound algorithm.The simulation results show that CLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.Compared with the traditional fast uplink grant, CLAC can improve the average energy efficiency by 48.11%, increase the proportion of terminals meeting access service quality requirements by 54.95%, and reduce the average queue backlog by 83.83%.