Energy Efficiency Optimization On 5G Massive Machine Type Communication

Wireless communication is about to enter the 5G era,massive machine type communication will be widely used in all areas of production and life,whose diversified communication needs and device energy constraints restrict the application and development of machine type communication(MTC)with other factors.This thesis takes the energy efficiency of machine device in the scene of periodic sending fixed size packet as a typical situation of energy efficiency optimization of massive MTC,focuses on the Energy efficiency Optimization scheme with multi-hop relay and wireless resource management,improves the energy efficiency of MTC device,and increases the efficiency of network resource utilization.The main research contents of this thesis are as follows:Firstly,this thesis systematically investigates the problem of energy efficiency optimization in massive MTC scenarios,focuses on the problem of energy efficiency in delay tolerance and different delay requirements,analyzes the relevant typical schemes to obtain the gaps that can be improved and innovated and lay a foundation for the follow-up work.In order to further address the issue of MTC energy efficiency,this thesis introduces the data aggregation strategy based on collaborative communication.In addition,for the energy consumption model under the scenario with different delay requirements,this thesis proposes a power selection scheme based on the target of maximum one-hop energy efficiency,where the research on energy efficiency optimization is carried out.Secondly,aiming at the massive MTC scene of delay tolerance,this thesis proposes a uniform clustering algorithm based on the average residual energy,which uses the data aggregation function of MTC device to adjust the clustering situation in the network in time according to the change of residual energy information to balance the energy consumption load of cluster head.In this thesis,the optimal number of cluster head expressions under the target of minimum total energy consumption of single communication round is solved by convex optimization method.On this basis,this thesis uses heuristic thought to further improve the current algorithm,where the algorithm can still achieve the performance of the above algorithm in the absence of a given number of cluster heads.Experimental results show that the algorithm proposed in this thesis makes the energy consumption of MTC device more balanced,improves the overall energy utilization efficiency and extends the network life.Finally,for massive MTC scenarios with different latency requirements,a heuristic energy efficiency optimization scheme based on genetic algorithm is proposed in this thesis.Under the premise of orthogonal resource allocation,the problem of wireless resource allocation is transformed into the transmission order problem of MTC device.With the change of residual energy information and the requirement of delay,the resource utilization strategy of MTC device is adjusted based on the genetic algorithm in time by using the residual energy information of MTC device and the Euclidean distance from the convergent node as the basis of relay selection to use the network wireless transmission resources and MTC device battery energy efficiently.Simulation experiments are carried out under different signal-to-noise ratio threshold conditions.The results show that the network energy efficiency increases with the increase of the signal-to-noise ratio threshold.But the number of communication hops also increases in the scene.