Research On The Recovery Algorithm Of Nonlinear Distorted SCMA-OFDM Signal

With the rapid development of the mobile Internet and the era coming of the Internet of Things(IoT),massive connection has become one of the basic requirements for supporting the Internet of Things.Therefore,as one of the key links in wireless communication,multi-access technology has become a hot research topic in wireless communication firstly.Limited spectrum resources limit the application of the traditional Orthogonal Multiple Access(OMA)technology in the Fifth Generation(5G),so in order to access more users in the limited spectrum resources,some non-orthogonal Multiple Access(NOMA)technologies has been proposed.Due to the advantages in the number of connections supported and spectrum utilization,Sparse code multiple access has become one of the most competitive non-orthogonal multiple access technologies.In this paper,OFDM technology is adopted to modulate SCMA signal into multiple subcarriers to form scma-ofdm signal for transmission.While inherits the advantages of OFDM modulation technology,such as robustness against multipath fading,high spectral efficiency and easy implementation,it also inherits the defects of its high Peak-to-Average Power Ratio(PAPR).However,the high PAPR of SCMA-OFDM signal will make the signal at the sending end enter the saturation region of the high power amplifier and cause the non-linear distortion of the signal(manifested as in-band distortion and out-of-band radiation),thus causing the performance degradation of the in-band system and interference with the out-of-band system.Therefore,in order to reduce the nonlinear distortion when signals passing through the system and maximize the advantages of OFDM technology,how to suppress the PAPR of SCMA-OFDM signals at the sending end of the system and how to reduce or even eliminate the influence caused by the nonlinear distortion at the receiving end has become the focus of research.The research in this paper is mainly based on signal distortion technology which is one of the three popular PAPR suppression technologies.The main work of this paper is as follows:(1)In the sending end,we try to lower the PAPR of SCMA-OFDM by using the simplest signal distortion technique,clipping.However,the clipping process brings extra noise to the SCMA-OFDM system but the conventional message passing algorithm(C-MPA)did not take it into account,which will cause the C-MPA decoding to be inaccurate and make the bit error rate(BER)performance of the C-MPA scheme decline sharply with the increasing of SNR.Now that the clipping process can be seen as an operation which brings extra noise to the SCMA-OFDM signal,we employ an analytic method to analyze the random characteristics of the clipping noise and take both the AWGN and the clipping noise into consideration for decoding accurately.Compared with the traditional MPA detection Algorithm(C-MPA),the Clipping Noise Aided Message Passing Algorithm(CNA-MPA)proposed in this paper adds consideration to the Clipping Noise,and corrects the inaccuracy of C-MPA decoding,so as to improve the BER performance of the system.(2)Since the clipping process is available in advance at both sides,we propose an iterative clipping noise elimination scheme,which the clipping noise is reconstructed by using the same clipping operation on the detected data at the receiving end as on the sending end,and then remove the clipping noise from the original receive signals to restore the BER performance.The proposed iterative clipping noise elimination scheme can restore the BER performance of the clipped SCMA-OFDM system within small gap compared with the nonclipped SCMA-OFDM system case.(3)Considering that the number of iterations will result in extremely high complexity for iterative schemes,based on the iterative clipping noise elimination scheme,we propose a new scheme which combines the elimination of clipping niose and the MPA detection in SCMA-OFDM system,the elimination of clipping noise is adopted immediately after a user's data detection to eliminate the user's noise in time,which makes the data detection of subsequent users more accurate,thus improving the convergence speed of the algorithm and reducing the complexity of the algorithm.