In this paper, we study short-packet communications in wireless-powered cognitive Internet-of-Things (IoT) networks with multiple primary receivers (PRs). The considered system can be applied for small factory automations, where a source and multiple relays harvest energy from a multi-antenna dedicated power beacon (PB) to send short packets to a robot destination for controlling purposes under cognitive radio constraint imposed by PRs. We propose an opportunistic relay selection (ORS) scheme to maximize the end-to-end signal-to-noise ratio in cognitive IoT systems. Closed-form expressions for the average block error rate (BLER) of the proposed system are obtained, based on which the performance floor analysis, goodput, and energy efficiency (EE) are also carried out. Relying on analytical results, we develop a deep learning framework for the BLER prediction with high accuracy and short execution time. Simulation results show the BLER, goodput, and EE improvements of the ORS scheme over conventional relay selection schemes. Moreover, the developed deep learning-based evaluation model achieves the equivalent performance as the ORS scheme in terms of BLER, goodput, and EE, while remarkably reducing the execution time in cognitive IoT systems.

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.

This letter studies an Internet-of-Things (IoT)-based coordinated direct and relay transmission (CDRT) adopting non-orthogonal multiple access (NOMA), where an information source directly communicates with a strong user while a weak user needs the help of an IoT master node. Different from traditional NOMA-CDRT systems, in which NOMA protocol is only deployed at the information source to serve users, the proposed novel system, i.e., IoT-assisted NOMA-CDRT, have two stages: (1) information source transmits the information data to users and (2) IoT master node operating as a relay to decode and forward data of the weak user along with IoT-U. Under the considered system setup, we derive exact closed-form expressions for the outage probability of users as well as approximated expressions for the ergodic sum capacity. Our results show that the proposed system may not perform well in terms of outage performance at the strong user during the cooperative transmission phase, but achieves higher ergodic sum capacity in whole communication process, and enhances efficient spectrum usage, compared with conventional CDRT and orthogonal multiple access systems.

This paper investigates performance of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled multiple two-way full-duplex device-to-device (D2D) communication systems over Rayleigh fading channels under optimal and uncertain phase shift alignments. We derive closed-form expressions for outage probability (OP), sum throughput, ergodic capacity (EC) and energy efficiency. To gain insights, we quantify and reveal some useful guidelines for the performance behavior of the OP and the EC, such as diversity order and ergodic slope from high transmit power configuration. In addition, some critical points also deduced for the sum throughput and the system energy efficiency. Moreover, the impacts of the transmit power configurations, RIS deployments, allocating target data rate transmission, and the number of user deployments on the system performance are examined. Finally, we present some extensive simulations using Monte-Carlo method to corroborate the accuracy of the theoretical analysis.

In this paper, we propose short-packet communications (SPCs) with non-orthogonal multiple access (NOMA) for coordinated and direct relay transmission (CDRT) Internet-of-Things (IoT) networks to enhance spectrum utilization and system performance. In this system, a source transmits its packets to a near IoT user via direct transmission and a far IoT user via selective relaying transmission under practical conditions of imperfect successive interference cancellation and cochannel interference. We derive the closed-form and asymptotic expressions for the average block-error-rate (BLER), throughput, and the goodput of the considered system over Rayleigh fading channels. To further improve the considered system, we formulate the system throughput maximization problem. Numerical results show that our proposed system outperforms the orthogonal multiple access and conventional NOMA-CDRT ones in terms of both BLER, system throughput, and goodput. In addition, by jointly optimizing power allocation coefficients and channel coding ratios, the system throughput performance is maximized. Furthermore, for a given block-length, the proposed system with short messages achieves superior latency and reliability compared to the long-message one. Monte Carlo simulations are presented to confirm the theoretical analysis.

In this paper, we investigate the performance of non-orthogonal multiple access (NOMA)-based full-duplex Internet-of-Things (IoT) relay systems with simultaneous wireless information and power transfer (SWIPT) over Nakagami-m fading channels to improve the performance of a cell-edge user under perfect and imperfect successive interference cancellation (SIC). Two scenarios, i.e., direct and non-direct links, between the source node and cell-edge user are examined. The exact closed-form analytical and approximate expressions for the outage probability, system throughput, energy efficiency, and ergodic capacities are derived and validated via Monte Carlo simulations to characterize the proposed system performance. To further improve the system performance, we also provide a low-complexity algorithm to maximize the system throughput over-optimizing the time-switching factor. The results show that our proposed NOMA system can achieve superior performance compared to its orthogonal multiple access (OMA) counterpart under perfect SIC and with a low-to-medium signal-to-noise ratio under imperfect SIC, according to the level of residual self-interference and the quality of links.