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Deep Learning-Based Non-orthogonal Multiple Access for 5G and Beyond Networks

  • Jan 1, 2022
  • Mariyam Ouaissa +3 more
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Abstract

Abstract Since the emergence of mobile communication systems as networks specifically for telephone service, they have experienced rapid progress. The evolution of mobile generations has seen a diversity of services, which includes data as the first service, then video, and other services. The fifth-generation network (5G) is the future of information networks. It is not really a further advancement of the fourth generation (4G) but another kind of mobile system model that supports high frequencies, extreme node densities, large-scale antennas, high bandwidth huge, etc. In this context, 5G networks can call on a wide range of new technologies. This allows for a leap in performance that eclipses its predecessors. These innovations will affect the transmission and design of the physical layer in addition to introducing disruption to the upper layers of the network. In fact, 5G New Radio (NR) will use many key technologies to achieve new levels of performance and efficiency. Some of the potential innovations in the 5G physical layer include Non-orthogonal Multiple Access (NOMA). The users are superimposed in the power domain, and the decoding mode is based on the Successive Interference Cancellation (SIC). To aim with the challenges in NOMA method, the deep learning (DL) models are a recommended solution. In this paper, we treat the technique NOMA for 5G and how the use of deep learning techniques can improve the performance of network.Keywords5GNRMultiple accessNOMASICCSIOFDMADL

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