In this letter, we investigate the use of machine learning (ML) to reduce the detection complexity of faster-than-Nyquist (FTN) signaling. In particular, we view the FTN signaling detection problem as… Click to show full abstract
In this letter, we investigate the use of machine learning (ML) to reduce the detection complexity of faster-than-Nyquist (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 the set of all possible classes samples. We observe that by jointly considering $N_{p}$ samples, where $N_{p} \ll N$ and $N$ is the transmission block length, for the FTN signaling detection, the distance between the classes samples of any distance-based classifier increases, and hence, the detection performance improves. That said, we propose a low-complexity classifier (LCC) that exploits the ISI structure of FTN signaling to perform the classification task in $N_{p}$ -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.
