Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning

Ravisankar Malladi, Manoj Kumar Beuria, Ravi Shankar and Sudhansu Sekhar Singh

The Journal of Defense Modeling and Simulation, 2022, vol. 19, issue 4, 829-838

Abstract: In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.

Keywords: Non-orthogonal multiple access; deep learning; successive interference cancellation; multiple-input multiple-output; deep neural network (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:joudef:v:19:y:2022:i:4:p:829-838

DOI: 10.1177/15485129211022857

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