Research On MIMO-OFDM Receiver Based On Convolutional Neural Network

In recent years,with the increase of communication business,5G came into being.Multiple-Input Multiple-Output(MIMO),Orthogonal Frequency Division Multiplexing(OFDM),and Non-Orthogonal Multiple Access(NOMA)are the keys of 5G.These three technologies can greatly improve the spectral efficiency of the system and the amount of user access.These techniques have disadvantages such as excessive Peak to Average Power Ratio(PAPR),difficulty in channel estimation,and excessive receiver complexity.Faced with these challenges in end-to-end systems,deep learning(DL)provides a new solution.In this paper,a neural network receiver is proposed to solve the above problems with MIMO-OFDM,NOMA and DL.(1)Aiming at the problem of bad data recovery performance caused by PAPR in MIMOOFDM systems,a neural network receiver model is proposed in this paper.The receiver to replace the entire information recovery link of the traditional receiver with neural network to simplify the system model.In this paper,a neural network receiver model is proposed.Further,a specific model of the 2D-Conv-DensNet neural network receiver was proposed,and on this basis,multiple binary classifiers are designed to recover multi-bit data.After sufficient training of the neural network,simulation experiments are carried out.The results show that the average bit error rate of the proposed neural network receiver in the range of the considered signal-tonoise ratio is 8.1%of the traditional hard-decision receiver,78.2%of the maximum likelihood receiver and 43.7%of other existing neural network receivers,respectively.In addition,the experiment also verifies that the proposed neural network receiver has better bit error(BER)rate performance than the traditional receiver regardless of the antenna combination,cyclic prefix length and modulation method,and even blind reception in a dynamic environment.Finally,the 2D-Conv-DensNet neural network receiver proposed in this paper can also compensate the signal distortion caused by the high PAPR to a certain extent.(2)There is serious inter-user interference in multi-user MIMO-NOMA system,so neural network receiver is introduced into MIMO-NOMA system.Two schemes are proposed,one of which directly uses a neural network as the receiver and the other is to add a neural network for precoding.The second scheme proposed in this paper is to add another neural network for precoding on the basis of the first scheme,which echoes the neural network receiver and realizes the joint optimization of minimizing the user information error,which makes the sy stem less complex.The simulation results show that in the range of the considered signal-tonoise ratio,the proposed scheme 1 and scheme 2 have only 78.4%and 63.2%of the best receiver BER performance,but only 21.6%and 5.2%complexity of the best receiver.This shows that the proposed scheme has better BER performance and lower complexity.In addition,it is also found in experiments that users with bad channel status in the proposed scheme are more sensitive to changes in cyclic prefix,learning rate and signal-to-noise ratio.