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A Study on the Adaptability of Deep Learning-Based Polar-Coded NOMA in Ultra-Reliable Low-Latency Communications

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Applied Information Processing Systems

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1354))

Abstract

According to ITU-R, 5G wireless communication’s primary goal is achieving too high data rates in the broadcast region. Polar coding has emerged as a pivotal channel coding technique for 5G to accomplish the previously mentioned goals. Subsequently, the Polar- Coded Non-Orthogonal Multiple Access (PC-NOMA) is observed as a favorable channel accessing technique for sporadic traffic of low data rate devices in a 5G Internet of Things (IoT) environment. Deep Learning algorithms are getting revolutionized in data analysis, Prediction, and decision-making by employing neural network hierarchy. When these Deep Learning algorithms get incorporated in channel estimation or resource allocation of Polar-coded NOMA, they appear to be a promising and robust solution for an uncertain channel. Meanwhile, ultra-reliable low-latency communication, one among the vital 5G use cases, has tremendous potential applications in the Internet of things generation. Consequently, the challenges of integrating deep learning techniques with PC-NOMA for URLLC use cases are reviewed, and the adaptability of Deep Learning algorithms for channel estimation and resource allocation of NOMA are surveyed here.

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Authors and Affiliations

  1. PSG College of Technology, Coimbatore, India

    N. Iswarya, R. Venkateswari & N. Madhusudanan

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  1. N. Iswarya
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  2. R. Venkateswari
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  3. N. Madhusudanan
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Editors and Affiliations

  1. Department of Electronics and Telecommunications Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, India

    Brijesh Iyer

  2. Department of Electronics and Computer Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Debashis Ghosh

  3. Department of Automatics and Applied Software, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

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Iswarya, N., Venkateswari, R., Madhusudanan, N. (2022). A Study on the Adaptability of Deep Learning-Based Polar-Coded NOMA in Ultra-Reliable Low-Latency Communications. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_4

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