In this paper, the outage behavior of dual-hop multiuser multirelay cognitive radio networks under spectrum-sharing constraints is investigated. In the proposed cognitive radio network, the secondary network is composed of one secondary-user (SU) source that communicates with one out of L destinations through a direct link and also via the help of one out of N relays by using an efficient relay-destination selection scheme. Additionally, a selection combining (SC) scheme to select the best link (direct or dual-hop link) from the SU source is employed at the selected SU destination. Adopting an underlay approach, the SU communication is performed accounting for an interference constraint, where the overall transmit power is governed by the interference at the primary-user (PU) receiver, as well as by the maximum transmission power available at the respective nodes. Closed-form expressions for the outage probability are derived, from which an asymptotic analysis reveals that the diversity order of the considered system is not affected by the interference and is equal to N + L for both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols. The analytical results are corroborated by Monte Carlo simulations, and insightful discussions are provided.

In this paper, we propose a unified framework for hybrid satellite/unmanned aerial vehicle (HSUAV) terrestrial non-orthogonal multiple access (NOMA) networks, where satellite aims to communicate with ground users with the aid of a decode-forward (DF) UAV relay by using NOMA protocol. All users are randomly deployed to follow a homogeneous Poisson point process (PPP), which is modeled by the stochastic geometry approach. To reap the benefits of satellite and UAV, the links of both satellite-to-UAV and UAV-to-ground user are assumed to experience Rician fading. More practically, we assume that perfect channel state information (CSI) is infeasible at the receiver, as well as the distance-determined path-loss. To characterize the performance of the proposed framework, we derive analytical approximate closed-form expressions of the outage probability (OP) for the far user and the near user under the condition of imperfect CSI. Also, the system throughput under delay-limited transmission mode is evaluated and discussed. In order to obtain more insights, the asymptotic behavior is explored in the high signal-to-noise ratio (SNR) region and the diversity orders are obtained and discussed. To further improve the system performance, based on the derived approximations, we optimize the location of the UAV to maximize the sum rate by minimizing the average distance between the UAV and users. The simulated numerical results show that: i) there are error floors for the far and the near users due to the channel estimation error; ii) the outage probability decreases as the Rician factor K increasing, and iii) the outage performance and system throughput performance can be further improved considerably by carefully selecting the location of the UAV.

An important use case in fifth generation systems are vehicular applications, where, reliability and low latency are the main requirements. In order to determine if a vehicular application can be used one can apply machine learning (ML) tools to determine if these constraints are met, which open questions such as “which data is available”, “which features to use”, “the quality of this prediction”, etc. In this paper we address some aspects of predicting quality-of-service (QoS) in a cellular vehicular-to-everything scenario, where we employ supervised learning as well as the autoregressive integrated moving average filter to predict if a packet can be delivered within a desired latency window. Particularly, we are interested in the reliability of this prediction, including predicting if a packet generated some time ahead will be delivered in time. Such information is essential when asserting that a vehicular application can indeed be employed safely. We show via simulation results that ML can be used as a prediction tool in vehicular applications. For instance, QoS levels can be predicted two seconds ahead with 85 % reliability.

This work proposes a reliable leakage detection methodology for water distribution networks (WDNs) using machine-learning strategies. Our solution aims at detecting leakage in WDNs using efficient machine-learning strategies. We analyze pressure measurements from pumps in district metered areas (DMAs) in Stockholm, Sweden, where we consider a residential DMA of the water distribution network. Our proposed methodology uses learning strategies from unsupervised learning (K-means and cluster validation techniques), and supervised learning (learning vector quantization algorithms). The learning strategies we propose have low complexity, and the numerical experiments show the potential of using machine-learning strategies in leakage detection for monitored WDNs. Specifically, our experiments show that the proposed learning strategies are able to obtain correct classification rates up to 93.98%.