Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA

• URL: http://arxiv.org/abs/2004.12416v1
• Date: Sun, 26 Apr 2020 15:22:44 GMT
• Title: Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA
• Authors: Ahmet Emir, Ferdi Kara, Hakan Kaya
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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