Research On Large Scale Terminal Multiple Access Scheme Based On Compression Sensing

The evolution of fifth generation mobile Communication systems and Internet of Things has accelerated the development of Massive Machine Type Communication(m MTC).How to carry authorization-free random access of m MTC terminals with limited spectrum resources is a difficulty and hotspot in the field of wireless communication in the last few years.For sparse burst m MTC scenarios,Compressive Sensing(CS)based passive multiple access technology becomes one of the solutions,its essence is based on Compressive Sensing Multi-User Detection(CS-MUD)and signal reconstruction.However,due to the limited capacity of spread codebook and access number of active terminals,the performance of existing CS based random access scheme is not satisfactory.Therefore,this paper studies the problem of large scale non-authorization random access transmission based on CS,explores methods to improve random access performance from two aspects of designing spread codebook set and optimizing multi-user detection and reconstruction algorithm,and proposes a large scale multiple access scheme based on CS.The main research contents and achievements are as follows:(1)In view of the large number of orthogonal spread codes required by large-scale terminals,the spread codebook design and reuse method is studied,and a large-scale multiple access scheme based on block sequence codebook is proposed.In this scheme,a large capacity spreading sequence codebook generation scheme is proposed by grouping large scale terminals and designing basic spread sequences and specific shift modes for each group.The sparse structure of the signal is combined with multi-carrier technology,that is,the molecular carriers of different data groups are reused to improve the spectral efficiency.In view of the above codebook design scheme,a group orthogonal matching pursuit algorithm based on compressed sensing and codebook sequence blocks is designed to jointly detect active users and uplink data,so as to achieve authorization-free largescale random access.The simulation results show that compared with the existing CS-MUD scheme,the proposed scheme has better access probability and bit error rate performance than other schemes,and the bit error rate(BER)is reduced by an order of magnitude when the receiving signal to noise ratio is 20 d B.(2)Aiming at the problem that passive multiple access is sensitive to user sparsity,this paper studies multiple access under the dynamic change of active terminal sparsity,and proposes a largescale multiple access scheme based on adaptive signal reconstruction.In this scheme,the uplink multi-user signals received by the base station were modeled as a stack,and a block sparse signal vector model was constructed.Then,an adaptive matching pursuit algorithm based on block sparse model and compressive sensing,called BSMAMP algorithm,was designed to jointly detect active users and their uplink data under unknown sparsity.Three strategies are introduced in this algorithm:dynamic step size strategy,dynamic pruning strategy and dynamic iteration strategy.The dynamic step size strategy adaptively adjusts the initial set of active users,the dynamic pruning strategy adaptively optimizes the support set,and the dynamic iteration strategy optimizes the iteration times of signal detection and reconstruction.The simulation results indicate that the proposed scheme’s BER is lower than the current CS-MUD scheme,and the calculation cost is lower when the user sparsity is larger.In this paper,the works provide the technology solution and the algorithm support for largescale terminal authority-free multiple access scenarios and also help to solve the constraints of scalability and reliability of m MTC system.