Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Receiver Design for Faster-than-Nyquist Signaling: Deep-learning-based
  Architectures   Faster-than-Nyquist (FTN) is a promising paradigm to improve bandwidth
utilization at the expense of additional intersymbol interference (ISI). In
this paper, we apply state-of-the-art deep learning (DL) technology into
receiver design for FTN signaling and propose two DL-based new architectures.
Firstly, we propose an FTN signal detection based on DL and connect it with the
successive interference cancellation (SIC) to replace traditional detection
algorithms. Simulation results show that this architecture can achieve
near-optimal performance in both uncoded and coded scenarios. Additionally, we
propose a DL-based joint signal detection and decoding for FTN signaling to
replace the complete baseband part in traditional FTN receivers. The
performance of this new architecture has also been illustrated by simulation
results. Finally, both the proposed DL-based receiver architecture has the
robustness to signal to noise ratio (SNR). In a nutshell, DL has been proved to
be a powerful tool for the FTN receiver design.

31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn