Investigation Of Layered Grouping Random Access Scheme And Dynamic Preambles

Massive internet of things(m Io T)has a wide range of applications such as agriculture monitors,smart city and wearables.Since the m Io T terminals may reach a density of millions per square kilometer,the traditional LTE random access schemes having high signaling overhead and collision probability are unable to meet demand.In this thesis,based on the idea of divide and conquer,massive random access schemes and related key technologies are investigated to overcome the problems and challenges brought by m Io T.On the basis of analyzing the existing random access schemes,a grouped massive random access scheme is proposed,which improves the detection performance while reduce the preamble length.Furthermore,a layered grouping random access scheme for m Io T Scenarios is proposed jointly with project partners.A two-phase random access strategy is designed resulting significantly improved successful access probability and the increased number of supported terminals.Besides,the construction and maintenance of two-phase random access strategy and the random access procedure are also discussed in detail.Based on the state evolution theory of approximated message passing algorithm,a lower bound on the minimum preamble length for achieving a certain detection accuracy is derived.Simulation results show that the lower bound calculated by using derived formulas is in good agreement with the Monte Carlo emulation.To dynamically adjust the preamble length of user group according to the cell load,a dynamical preamble selection mechanism is proposed in this thesis,which considered both the signaling cost and detection performance.The MATLAB simulation platform of layered grouping random access scheme is built,and the relevant algorithms are realized,in cooperation with project partners.It is shown from the simulation results that that the proposed scheme has remarkable advantages in terms of large number of low energy consumption.