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

In this paper, we study wireless-powered cognitive non-orthogonal 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 cancellation (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 optimized power allocation coefficients. In addition, a novel multi-output deep-learning (DL) framework is designed to simultaneously predict the BLERs and goodputs of the users towards real-time configurations for IoT systems. Our results show 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 multi-output DL also exhibits the lowest error performance and a short run-time prediction compared to the other multi-output regression models, while the predicted results using the DL model are almost matched with the simulation ones.