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• W3186408972 abstract "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.KeywordsDeep learningDNNMachine learningNOMAPolar codesURLLC" @default.
• W3186408972 created "2021-08-02" @default.
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• W3186408972 date "2021-07-21" @default.
• W3186408972 modified "2023-09-27" @default.
• W3186408972 title "A Study on the Adaptability of Deep Learning-Based Polar-Coded NOMA in Ultra-Reliable Low-Latency Communications" @default.
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• W3186408972 doi "https://doi.org/10.1007/978-981-16-2008-9_4" @default.
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