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Performance Analysis and Deep Learning Design of Wireless Powered Cognitive NOMA IoT Short-Packet Communications With Imperfect CSI and SIC

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Abstract
In this article, we study wireless-powered cognitive nonorthogonal multiple access (NOMA) Internet of Things (IoT) networks with short-packet communications to improve spectrum utilization and sustainability, as well as reduce the latency under imperfect channel state information (CSI) and successive interference cancelation (SIC). For performance evaluation, closed-form expressions for the block error rate (BLER) of the NOMA users, goodput, energy efficiency, latency, and reliability are derived. To gain some further insights into the system design, two scenarios can be taken into account for the positions of the primary receivers: 1) they are located near the secondary network and 2) they are located far away from the secondary network. Moreover, we propose an effective algorithm to minimize the BLERs of the NOMA users by optimizing power allocation coefficients. In addition, a novel multi-output deep-learning (DL) framework is designed to simultaneously predict the BLERs and goodputs of users towards real-time configurations for IoT systems. Numerical results show the outstanding performance of the proposed system over the orthogonal multiple access (OMA) one in terms of the BLER and goodput. Moreover, the proposed system achieves a lower latency and higher reliability compared to the long packet communications under the same channel settings. Furthermore, our designed multioutput DL also exhibits the lowest error performance and a short run-time prediction compared to the other multioutput regression models, while the predicted results using the DL model are almost matched with the simulation ones.
Author(s)
Thai-Hoc VuToan-Van NguyenTien-Tung NguyenSunghwan Kim
Issued Date
2022
Type
Article
Keyword
Cognitive radio (CR)deep learning (DL)infinite blocklengthnonorthogonal multiple access (NOMA)short-packet communication (SPC)wireless powered
DOI
10.1109/JIOT.2021.3121421
URI
https://oak.ulsan.ac.kr/handle/2021.oak/15618
Publisher
IEEE Internet of Things Journal
Language
영어
ISSN
2327-4662
Citation Volume
9
Citation Number
13
Citation Start Page
10464
Citation End Page
10479
Appears in Collections:
Engineering > IT Convergence
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