In this letter, we investigate the outage performance of cooperative relaying transmissions in two-user non-orthogonal multiple access systems, wherein simultaneous wireless information and power transfer is employed at the near users to power their relaying operations. To this end, a best-near best-far (BNBF) user selection scheme is proposed. Considering three relaying protocols, i.e., decode-and-forward (DF), amplify-and-forward (AF), and hybrid DF/AF protocols, tight closed-form approximate expressions for the outage probability are derived to evaluate the system performance. Numerical results reveal that, for any relaying protocols used, the diversity order achieved by the BNBF scheme is M+1, where M is the number of far users, and does not depend on the number of near users.

In this paper, we study performance improvement methods for a cell-edge user of two-user non-orthogonal multiple access (NOMA) systems in downlink scenarios. To this end, we propose two cooperative relaying schemes, namely on/offfull- duplex relaying (on/off-FDR) and on/off-half-duplex relaying (on/off-HDR) schemes. More specifically, in order to improve the performance of the cell-edge user, we consider a cell-center user as a relay, where either FDR or HDR can be employed to assist the direct NOMA transmission from a base station (BS) to the cell-edge user. An on/off mechanism is proposed to decide whether the cooperative relaying transmission is necessary or not. The on/off relaying decision is made based on the quality of the direct and relaying links from the BS to the cell-edge user. The performance of the two proposed schemes is investigated in terms of outage probability (OP) and sum throughput. Numerical results reveal that the proposed schemes not only provide essential outage performance improvements for the cell-edge user, but also are able to improve the sum throughput of the two-user NOMA systems. The advantages and drawbacks of each proposed scheme are highlighted and insightful discussions are provided.

In this paper, we study the performance of a cell-edge user in a two-user multiple-input single-output non-orthogonal multiple access (MISO-NOMA) system. Since the outage performance and fairness data rate of cell-edge users are essential issues in NOMA systems, we focus on how to resolve such problems. To this end, we propose three cooperative downlink transmission schemes utilizing hybrid simultaneous wireless information and power transfer (SWIPT) and transmit antenna selection (TAS) protocols. Particularly, in each scheme, a cell-center user acts as a relay to assist the cell-edge user and its relaying operation is powered by a hybrid time-switching/power-splitting (TS/PS) SWIPT protocol. Additionally, each scheme employs a different TAS criterion to exploit the spatial diversity gain of a multiple antennas base station. In particular, we derive tight closed-form approximate expressions for the outage probabilities (OPs) and the corresponding asymptotic OPs to provide significant insights into the impact of our proposed schemes on the system performance. Our numerical results demonstrate the achievable performance improvements of the proposed schemes in comparison to that of the orthogonal multiple access (OMA) and non-cooperative NOMA systems. In addition, the proposed schemes achieve various level of diversity gains accompanying with different complexity requirements.

We propose an entropy-based modeling framework for supporting route stability in mobile ad hoc wireless networks. The basic motivations of the proposed modeling approach stem from the commonality observed in the location uncertainty in mobile ad hoc wireless networks and the concept of entropy. The corresponding results demonstrate that the proposed approach and parameters provide an accurate and efficient method of estimating and evaluating the route stability in dynamic mobile networks.

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 paper, we propose an efficient deep neural network-based relay selection (DNS) scheme to evaluate and improve the end-to-end throughput in wireless-powered cognitive Internet-of-Things (IoT) networks. In this system, multiple energy harvesting (EH) relays are deployed randomly to assist data transmission from a source node to multiple users under practical nonlinearity of the EH circuits. We first design an incremental relaying protocol, where a selected user will request the help from relays if the direct transmission is not favorable. In such a protocol, we develop a deep neural network framework for relay selection and throughput prediction with high accuracy, less channel feedback amount, and short execution time. Simulation results show that the proposed DNS scheme achieves higher throughput than the conventional relay selection methods, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the proposed DNS scheme achieves the root-mean-square-error (RMSE) of 6.6×10-3 on the considered dataset, which exhibits the lowest RMSE as compared to the state-of-the-art machine learning approaches.