Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Non-orthogonal multiple access (NOMA) aims to increase the spectral efficiency of fifth generation (5G) networks by relaxing the orthogonal use of radio-resources. In this work, a network with multiple half-duplex (HD) buffer-aided (BA) relays is considered, where the source transmits with fixed rate towards two users. The users might demand the same rate by the source (e.g., two cellular users requiring the same service), or they could have different rate requirements (e.g., a cellular user coexisting with an Internet of Things (IoT) device). By deploying multiple BA relays, increased reliability and additional degrees of freedom are provided. Leveraging the spectral efficiency of NOMA and the increased diversity gain of BA relaying, two relay selection algorithms with broadcasting are proposed for power-domain (PD) NOMA and hybrid NOMA/OMA, namely BA-NOMA and BA-NOMA/OMA, respectively. BA-NOMA can improve the performance in terms of outage probability when the power allocation factor α is selected such that robustness against channel uncertainties due to, e.g., outdated channel state information (CSI), is provided. Moreover, BA-NOMA/OMA further improves the sum-rate by switching to OMA when the relays cannot serve the users through NOMA. For both cases, a theoretical analysis for the outage probability is conducted and the asymptotic performance is studied. Finally, numerical results and comparisons with other state-of-the-art algorithms are provided for the outage probability, average throughput and average delay.