With the rapid development of various services in wireless communications,
spectrum resource has become increasingly valuable. Faster-than-Nyquist (FTN)
signaling, which was proposed in the 1970s, has been a promising paradigm to
improve the spectrum utilization. In this paper, we try to apply FTN into
secure communications and propose a secure and high-spectrum-efficiency
transmission system based on FTN and deep learning (DL). In the proposed
system, the hopping symbol packing ratio with random values makes it difficult
for the eavesdropper to obtain the accurate symbol rate and inter-symbol
interference (ISI). While the receiver can use the blind estimation to choose
the true parameters with the aid of DL. The results show that without the
accurate symbol packing ratio, the eavesdropper will suffer from severe
performance degradation. As a result, the system can achieve a secure
transmission with a higher spectrum efficiency. Also, we propose a simplified
symbol packing ratio estimation which has bee employed in our proposed system.
Results show that the proposed simplified estimation achieves nearly the same
performance as the original structure while its complexity has been greatly
reduced