Optimization Design Of DRX-based System In NR

As the Fifth Generation Mobile Communication Technology(5G)continues to use for commercial purposes,the application scenarios of massive MachineType of Communication(mMTC)have become increasingly clear.The business scenarios are being more extensive and business models is also getting increasingly complicated.However,due to the need of supporting ultra-large-scale device accessing to the network,energy consumption has become a point that cannot be ignored.In order to support the concept of sustainable development in green technology,the need for energy efficiency improvement of terminal equipment is becoming more and more urgent under the 5G NR system.In the Long term Evolution(LTE)system,the Discontinuous Reception System(DRX)is widely used as a reliable and effective means of reducing energy consumption.Therefore,the DRX mechanism will continue to be used as an energy efficiency improvement method in the NR system.The core idea of the Discontinuous Reception System is to allow users'devices to enter a periodic monitoring state because of the characteristics of the intermittent arrival of data packets during transmission.And the rest of the time when there is no data packet receiving requirement,devices can turn off its transceiver and enter the dormant state to save energy consumption.However,since the user equipment in the sleep phase cannot process the data packets arriving in this phase in time,a delay problem of data packet reception will occur,which will affect the quality of service.Therefore,it is necessary to solve the problems of energy efficiency improvement and time delay guarantee in Discontinuous Reception systems.And the topic will be developed from the balance between those two points.This thesis establishes a semi-Markov state transition model for the DRX system,and establishes a power consumption model to calibrate energy consumption and delay time output firstly.After that,a fuzzy logic controller in the fuzzy mechanism is introduced.The delay of data packet reception and the rate of data packet arrival are used as input parameters to fuzzy process of the system.And then adjust the cycle length of the DRX adaptively.By using the simulation results,we can analyze and study the energy efficiency and delay time performance between the fuzzy logic algorithm-based DRX system and the traditional DRX system under different parameters.It can prove that the energy consumption and delay performance of this scheme are better than the fixed-parameter DRX.Secondly,the consideration of multi-user and channel mechanism is introduced.The On-duration timer parameter in DRX is adjusted by the system load changes adaptive algorithm;the Inactivity timer parameter in DRX is adjusted by the channel changes adaptive algorithm.Through the simulation results of different user access conditions and different channel conditions,the energy efficiency and throughput performance of the improved adaptive version of the DRX system and the fuzzy logic algorithm DRX system are analyzed and researched.It shows that the solution can indeed be used in the case of complex multi-users and multi-channels.The efficient energy utilization of the system is optimized under the controllable reduction of throughput loss.Finally,combined with the introduction of massive MachineType of Communication under 5G NR,the business traffic model is expanded.In the traditional eDRX system,a reinforcement learning algorithm is introduced to optimize this stochastic optimization problem through the Markov decision process.And the Actor-critic algorithm based on symmetric disturbance sampling with faster convergence is applied.The system will predict the traffic service model by learning historical transmission data information.And then implement the optimal DRX system parameter configuration under the current traffic model.And maximize the improvement of energy efficiency within the tolerance of the maximum delay.Through the analyzing and studying of simulation results,it indeed improves the energy efficiency and time delay performance.