基于 CNN 解调器的超奈奎斯特速率通信

A demodulator based on convolutional neural networks CNNs is proposed to demodulate bipolar extended binary phase shifting keying EBPSK signals transmitted at a faster-than-Nyquist FTN rate solving the problem of severe inter symbol interference ISI caused by FTN rate signals. With the characteristics of local connectivity pooling and weight sharing a six-layer CNNs structure is used to demodulate and eliminate ISI.The results show that with the symbol rate of 1.07 kBd the bandwidth of the band-pass filter BPF in a transmitter of 1 kHz and the changing number of carrier cycles in a symbol K =5 10 15 28 the overall bit error ratio BER performance of CNNs with single-symbol decision is superior to that with a double-symbol united-decision.In addition the BER performance of single-symbol decision is approximately 0.5 dB better than that of the coherent demodulator while K equals the total number of carrier circles in a symbol i.e. K=N=28.With the symbol rate of 1.07 kBd the bandwidth of BPF in a transmitter of 500 Hz and K=5 10 15 28 the overall BER performance of CNNs with double-symbol united-decision is superior to those with single-symbol decision. Moreover the double-symbol united-decision method is approximately 0.5 to 1.5 dB better than that of the coherent demodulator while K =N=28.The demodulators based on CNNs successfully solve the serious ISI problems generated during the transmission of FTN rate bipolar EBPSK signals which is beneficial for the improvement of spectrum efficiency.