Design And Implementation Of Optimized Radio Resource Control For Cellular Internet Of Things

Recently,with various applications of internet of things(IoT),the cellular internet of things(CIoT)has become an important branch of both wireless communication technology and IoT.The radio resource control(RRC)protocol is responsible for core functions such as connection control and bearer establishment in CIoT radio access network(RAN).The emergence of various IoT scenarios and diversified service requirements has made both RRC slicing and intelligent control an imperative requirement.This thesis focuses on designing and enhancing standardized capabilities of NB-IoT RRC protocol based on the current CIoT standards.The design is accomplished with RRC slicing architecture and divided into different modules on the basis of functions.The software is developed and tested on the open-source software platform Open Air Interface(OAI).According to the RRC slicing design,the RRC sublayer is realized with functions of creating,modifying,and deleting slices,meeting the requirements of independent operation of different slices.Furthermore,in order to satisfy various and dynamic service requirements of different IoT devices and optimize the power consumption,this thesis proposes a useful radio resource control optimization solution based on reinforcement learning algorithm.After analyzing the discontinuous reception(DRX)mechanism of the CIoT,the Q-learning algorithm is selected to improve the energy efficiency and downlink transmission delay.The proposed mechanism is implemented in the collaborative management module of RRC protocol layer and is capable of reconfiguring the DRX parameters of UE online dynamically.Finally,the simulation results show that the method can significantly improve the power consumption and latency combined metric of the IoT devices compared with the existing DRX implementation.