Data-Rate Driven Transmission Strategies for Deep Learning Based Communication Systems

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions

Signal detection with co-channel interference using deep learning

Signal detection using deep learning is a challenging and promising research topic. Several learning-based signal detectors have been proposed to produce significant results. However, most of them have ignored interference in their designs. In this paper, we evaluate the performance of learning-based signal detectors in the presence of co-channel interference under different channel conditions. Specifically, fully connected deep neural network (FCDNN) and convolutional neural network (CNN) are examined as the data-driven signal detector for blind signal detection without knowledge of the channel state information. Several important system parameters, including signal-to-interference ratio, number of interferences and type of interference, are considered. Numerical results show that FCDNN and CNN-based detectors have better performance and robustness to different SIRs conditions than traditional detectors in the presence of interference and FCDNN performs better than CNN when SIR is small and the order of interference modulation is high