Aiming at the problem of high computational complexity of Vertical-BLAST(V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMO-OFDM) system signal detection, this paper f...Aiming at the problem of high computational complexity of Vertical-BLAST(V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMO-OFDM) system signal detection, this paper first uses Sorted QR Decomposition(SQRD) iterative operation instead of matrix inversion to reduce the computational complexity of the algorithm, and then considering that the algorithm is greatly affected by noise, Minimum Mean Square Error(MMSE) criterion is used to weaken the noise effect. At the same time, in order to reduce the noise and computational complexity, MMSE and SQRD are combined, which can not only reduce the noise and computational complexity, but also obtain the sub-optimal detection order, thus improving the detection performance of the MIMO-OFDM system. Finally, the numerical simulation of the MMSE-SQRD detection algorithm is carried out. The results show that the Eb/No of MMSE-SQRD algorithm is 2 dB greater than that of the MMSE algorithm and the computational complexity is O(NT3) under the conditions that NT =NR=2 and the BER is 10–2. The detection algorithm satisfies the demand of short wave and wideband wireless communication.展开更多
Multiuser detection technology is currently one of the effective ways to suppress multiple access interference and near-far effects. Firstly, through selecting a simple compensation matrix, fast improved approximation...Multiuser detection technology is currently one of the effective ways to suppress multiple access interference and near-far effects. Firstly, through selecting a simple compensation matrix, fast improved approximation power iteration(FIAPI) subspace tracking optimization algorithm is proposed. Secondly, for the disadvantage of high computational complexity of Kalman filtering algorithm, Kalman for blind adaptive multiuser detector based on FIAPI subspace tracking algorithm is designed. The simulation experiments show that the convergence and anti-interference ability of the blind adaptive multiuser detector based on FIAPI algorithm is greatly improved, and the average signal-to-interference ratio of the FAPI algorithm is improved by about 0.7 dB, which is higher than the average signal-to-interference ratio of the orthogonal projection approximation subspace tracking(OPAST) algorithm 2 dB or so.展开更多
Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and anal...Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and analyzes the influence of parameters in CNN structure on channel equalization,such as the number of convolution layers,the depth of convolution layer and the size of the convolution kernel layer.By simulating two typical short-wave time-varying channel,Rayleigh flat fading and frequency selective fading channels,we have the following results:1)Compared with the Recurrent Neural Network(RNN)structure equalizer,the CNN has higher accuracy during the training process,the convergence speed is faster,and the stability after convergence is higher.2)Under the condition of simulation,the CNN-based short-wave channel blind equalizer designed in this paper can effectively extract input signal when using 2×3×3 convolution kernel size and 2-layer convolutional layer.The characteristics of the classification layer improve the equalization performance while reducing the complexity of CNN structure.3)For the short-wave channel,the error rate of Convolution Neural Network Equalizer(CNNE)is lower than that of Recurrent Neural Network Equalizer(RNNE)under the same SNR.展开更多
基金Supported by the National Natural Science Foundation of China(61671333)
文摘Aiming at the problem of high computational complexity of Vertical-BLAST(V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing(MIMO-OFDM) system signal detection, this paper first uses Sorted QR Decomposition(SQRD) iterative operation instead of matrix inversion to reduce the computational complexity of the algorithm, and then considering that the algorithm is greatly affected by noise, Minimum Mean Square Error(MMSE) criterion is used to weaken the noise effect. At the same time, in order to reduce the noise and computational complexity, MMSE and SQRD are combined, which can not only reduce the noise and computational complexity, but also obtain the sub-optimal detection order, thus improving the detection performance of the MIMO-OFDM system. Finally, the numerical simulation of the MMSE-SQRD detection algorithm is carried out. The results show that the Eb/No of MMSE-SQRD algorithm is 2 dB greater than that of the MMSE algorithm and the computational complexity is O(NT3) under the conditions that NT =NR=2 and the BER is 10–2. The detection algorithm satisfies the demand of short wave and wideband wireless communication.
基金Supported by the National Natural Science Foundation of China(61671333).
文摘Multiuser detection technology is currently one of the effective ways to suppress multiple access interference and near-far effects. Firstly, through selecting a simple compensation matrix, fast improved approximation power iteration(FIAPI) subspace tracking optimization algorithm is proposed. Secondly, for the disadvantage of high computational complexity of Kalman filtering algorithm, Kalman for blind adaptive multiuser detector based on FIAPI subspace tracking algorithm is designed. The simulation experiments show that the convergence and anti-interference ability of the blind adaptive multiuser detector based on FIAPI algorithm is greatly improved, and the average signal-to-interference ratio of the FAPI algorithm is improved by about 0.7 dB, which is higher than the average signal-to-interference ratio of the orthogonal projection approximation subspace tracking(OPAST) algorithm 2 dB or so.
基金the National Natural Science Foundation of China(61671333)。
文摘Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and analyzes the influence of parameters in CNN structure on channel equalization,such as the number of convolution layers,the depth of convolution layer and the size of the convolution kernel layer.By simulating two typical short-wave time-varying channel,Rayleigh flat fading and frequency selective fading channels,we have the following results:1)Compared with the Recurrent Neural Network(RNN)structure equalizer,the CNN has higher accuracy during the training process,the convergence speed is faster,and the stability after convergence is higher.2)Under the condition of simulation,the CNN-based short-wave channel blind equalizer designed in this paper can effectively extract input signal when using 2×3×3 convolution kernel size and 2-layer convolutional layer.The characteristics of the classification layer improve the equalization performance while reducing the complexity of CNN structure.3)For the short-wave channel,the error rate of Convolution Neural Network Equalizer(CNNE)is lower than that of Recurrent Neural Network Equalizer(RNNE)under the same SNR.
