摘要
A demodulator based on convolutional neural networks（ CNNs） is proposed to demodulate bipolar extended binary phase shifting keying（ EBPSK） signals transmitted at a faster-thanNyquist（ 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 showthat with the symbol rate of 1. 07 k Bd, the bandwidth of the band-pass filter（ BPF） in a transmitter of 1 k Hz 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 doublesymbol united-decision. In addition, the BER performance of single-symbol decision is approximately 0. 5 d B 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 k Bd, 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 uniteddecision method is approximately 0. 5 to 1. 5 d B 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.
针对超奈奎斯特速率传输信号在传输过程中产生的严重码间干扰问题,提出了一种基于卷积神经网络（CNN）的解调器,对双极性扩展的二进制相移键控（bipolar EBPSK）超奈奎斯特速率信号进行解调.利用卷积神经网络局部感受野、池化和权值共享的特点,提出了一种具有6层结构的卷积神经网络来解调扩展的二进制相移键控调制信号并消除码间干扰.实验结果表明：当码率为1.07 k Bd、发送端带宽限制为1 k Hz,且一个码元中跳变载波周期数K=5,10,15,28时,CNN单码元判决方法误码率性能总体优于CNN双码元联合判决方法;当K等于码元载波周期总数N,即K=N=28时,CNN单码元判决误码率方法优于相干解调约0.5 d B;当码率为1.07 k Bd、发送端带宽限制为500 Hz,且K=5,10,15,28时,CNN双码元联合判决方法优于CNN码元判决方法;当K=N=28时,CNN双码元判决方法优于相干解调约0.5~1.5 d B.基于CNN的解调器成功地解决了由超奈奎斯特速率双极性传输信号产生的严重码间干扰问题,有利于频谱利用率的提高.
基金
The National Natural Science Foundation of China(No.6504000089)
