  Faster-than-Nyquist (FTN) signaling can improve the spectral efficiency (SE);
however, at the expense of high computational complexity to remove the
introduced intersymbol interference (ISI). Motivated by the recent success of
ML in physical layer (PHY) problems, in this paper we investigate the use of ML
in reducing the detection complexity of FTN signaling. In particular, we view
the FTN signaling detection problem as a classification task, where the
received signal is considered as an unlabeled class sample that belongs to a
set of all possible classes samples. If we use an off-shelf classifier, then
the set of all possible classes samples belongs to an $N$-dimensional space,
where $N$ is the transmission block length, which has a huge computational
complexity. We propose a low-complexity classifier (LCC) that exploits the ISI
structure of FTN signaling to perform the classification task in $N_p \ll
N$-dimension space. The proposed LCC consists of two stages: 1) offline
pre-classification that constructs the labeled classes samples in the
$N_p$-dimensional space and 2) online classification where the detection of the
received samples occurs. The proposed LCC is extended to produce soft-outputs
as well. Simulation results show the effectiveness of the proposed LCC in
balancing performance and complexity.
