Research On Sparse Multi-user Detection Strategy In Non-orthogonal Multiple Access System

With the progress of science and technology,wireless communication service is developing rapidly,which puts forward higher requirements for the number of user connections and spectrum resources in 5G communication system.Non-orthogonal multiple access(NOMA)technology is considered to be one of the potential technologies for future 5G networks to support massive connectivity and low transmission latency.It can realize system overload,and allow more users to share the same time-frequency resources,which can effectively save spectrum resources,so as to deal with the problem of insufficient spectrum resources in the field of broadband wireless communication.In the uplink grant-free non-orthogonal multiple access system,in order to obtain the user activity,the base station receivers need to detect the active user data information.However,the traditional detection algorithm has difficulty in signal recovery,and a new detection method must be explored.As the user activity is sparse during wireless communication process,making the application of compressive sensing technique to sparse multi-user detection in NOMA system become a hot topic in signal processing.Therefore,how to construct a reasonable system model and how to select the appropriate detection strategy to improve the spectrum efficiency and system detection performance are the urgent problems to be solved at present.In view of the above problems,this paper proposes the corresponding sparse multi-user detection strategy based on the NOMA system,using compressive sensing technology to improve the bite error rate(BER)performance of the system,the specific contents are as follows:1.We study the sparse multi-user detection strategy based on a single antenna NOMA system.In order to take full advantage of the sparse characteristics of the signal in sporadic communication system,compressive sensing related theoretical knowledge has been extended to NOMA system.In this paper,two kinds of compressive sensing reconstruction algorithms,orthogonal matching pursuit algorithm and compressive sampling matching pursuit algorithm,are introduced in detail,which can be used to recover sparse signals.Also,we make a simulation comparison with algorithms under ideal conditions.In addition,we analyze the relationship between the number of active users and BER performance,and the impact of different measurement matrices as spread spectrum sequences on the performance of the system.2.We study the sparse multi-user detection strategy based on multiple antennas NOMA system.Using the relationship between signal sparsity and detection performance,a substep multi-user detection strategy is proposed.Meanwhile,the user activity information is detected by combining the compressive sensing-based hard fusion algorithm.We select a part of the active user in the estimated support set,after subtracting this part,the system signal sparsity will be reduced.And then,the spare signals are secondary detected so as to obtain the remaining user data.At the same time,the feasibility of the algorithm is verified by theoretical analysis.Simulation results show that the proposed scheme can significantly improve the accuracy of user detection with the change of signal to noise ratio,the number of active users,or overload rate.