Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA
This addresses a domain-specific issue for wireless communication systems by proposing an incremental improvement to pilot interval reduction in NOMA.
The study tackled the problem of reduced spectral efficiency in uplink NOMA systems due to pilot signals by researching deep learning-based detectors that respond to a single pilot from users, aiming to maintain spectral efficiency by reducing the time interval.
Non-Orthogonal Multiple Access (NOMA) has higher spectral efficiency than orthogonal multiple access (OMA) techniques. In uplink communication systems that the channel is not known at the receiver, pilot signals sent from each user in different time intervals have reduced the spectral efficiency of NOMA. In this study, in the uplink communication system, DL-deep learning based detectors which are known to respond to the pilot signals sent from the users at the base station have been researched. It is aimed to maintain the spectral efficiency of NOMA by sending a single pilot from users, thus reducing the time interval in the DL detectors.