Research On Multi-User Detection Algorithm Based On CS Theory In Uplink Grant-free NOMA

Massive Machine Type Communication(mMTC)is an important application scenario in the future mobile communication system,which has the characteristics of massive device access,low data rate and short data packet transmission.Non-Orthogonal Multiple Access(NOMA)technology can achieve system overload through non-orthogonal resource allocation to meet the needs of large-scale connections.In order to reduce transmission delay and signaling overhead,it is desirable to implement grant-free transmission in the uplink NOMA system.mMTC has the characteristics of sporadic communication,which causes sparsity of active user.Compressive Sensing(CS)reconstruction algorithm can recover sparse signals.However,the compressive sensing recovery algorithm often requires noise power or signal-to-noise ratio as a prior condition,which greatly reduces the adaptability of the multi-user detection algorithm.Therefore,this paper studies the multi-user detection in the implementation of dynamically changing channel state information(CSI)communication scenarios.The main work is as follows:(1)When the sparsity of the active user is unknown and the signal-to-noise ratio is known,The extension of threshold aided structured sparsity orthogonal matching pursuit(TA-SSOMP)algorithm applied to single-slot multi-user detection to multi-slot multi-user joint detection scenarios is studied.Firstly,structure sparse model is converted into a block sparse structure model equivalently,and then the converted block sparse structure model parameters are used as the input of the algorithm,and finally the algorithm is used to realize the recovery of the sparse signal.The algorithm uses the correlation between the energy of the signal residuals and the signal noise to set the iterative threshold,and enables joint detection of active user and sent data when the sparsity is unknown.The simulation results show that the TA-SSOMP algorithm can obtain better multi-user detection bit error rate performance and user bearing performance than other algorithms.(2)When the sparsity of the active user and signal-to-noise ratio are unknown,and combined with the theoretical advantages of the block sparse structure,a cross validation aided structured sparsity adaptive orthogonal matching pursuit algorithm(CVA-SSAOMP)is proposed.This algorithm transforms the structure sparse model into a block sparse structureequivalently,and uses the cross validation method in statistics and machine learning to adaptively estimate the sparsity of the active user through cross validation residual update without other prior conditions,and the iterative stopping condition is determined by the minimum cross validation residual value.When the sparsity of the active user and the signal-to-noise ratio are unknown,the joint detection of the active user and the transmitted data is realized.The simulation results show that the CVA-SSAOMP algorithm can effectively estimate the sparsity of the active user,thereby improving the system's bit error rate performance,and has the advantage of low complexity.