Unmanned aerial vehicle (UAV) relaying is an efficient solution to provide wireless access for emergency communications due to the high flexibility. The system reliability is usually constrained under limited bandwidth and power resources. Downlink non-orthogonal multiple access (NOMA) can improve the system reliability through a higher resource utilization. To this end, we introduce downlink NOMA to a UAV-enabled mobile relaying system and investigate a scenario where a fixed-wing UAV flies in a circular trajectory to serve as a mobile decode-and-forward (DF) relay in an emergency situation. Since guaranteeing a reliable link is necessary in an emergency situation, our formulated problem is to minimize the maximum outage probability among all links, taking into account the bandwidth and power allocations based on downlink NOMA. Specially, the condition for successful successive interference cancellation (SIC) is also considered. By making change of variables and introducing slack variables, we reformulate our problem into a more tractable form, then we propose an iteration algorithm to solve our problem based on the successive convex optimization (SCO) technique. The optimized bandwidth and power allocation schemes as well as the min-max outage probability along the UAV trajectory are obtained, respectively. Two benchmarks are designed to reveal the performance of our proposed algorithm, and the reliability gain can be obtained by comparing the min-max outage probability and the overall average outage probability. INDEX TERMS UAV, mobile relaying, downlink NOMA, outage probability, resource allocation. I. INTRODUCTION A. BACKGROUND AND MOTIVATION

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Unmanned aerial vehicles (UAVs) as aerial relays have found many applications in the current communication network. Since the air‐to‐ground (AtG) link differs from the terrestrial link, ensuring a reliable AtG transmission remains to be a meaningful issue. This article investigates that a UAV works as an aerial relay to forward information from multiple access points to multiple remote base stations in an emergency situation. Using the full‐duplex decode‐and‐forward relaying mode, we formulate a problem to minimize the maximum outage probability among all links, jointly optimizing the UAV altitude, power control, and bandwidth allocation. The outage probability function takes into account the line‐of‐sight probability‐based AtG fading and the self‐interference introduced by full‐duplex relaying, both of which make our formulated problem nonconvex. We reformulate the outage probability function into a more tractable function. Then, we decouple the original problem into two subproblems, that is, altitude optimization as well as power control and bandwidth allocation. Each subproblem is solved by the successive convex optimization technique, and an overall iteration algorithm is proposed to solve the original problem based on the block coordinate descent theory. Simulation results reveal the reliability gain of our proposed algorithm compared with our designed benchmark. Besides, the optimal UAV three‐dimensional location that minimizes the global min‐max outage probability is found by numerical simulations. Finally, the average throughput gain can also be obtained by our proposed algorithm.

Biological oxygen demand (BOD5) is an indicator used to monitor water quality. However, the standard process of measuring BOD5 is time consuming and could delay crucial mitigation works in the event of pollution. To solve this problem, this study employed multiple machine learning (ML) methods such as random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP) to train a best model that can accurately predict the BOD5 values in water samples based on other physical and chemical properties of the water. The training parameters were optimized using genetic algorithm (GA) and feature selection was done using sequential feature selection (SFS) method. The proposed machine learning framework was firstly tested on the public dataset (Waterbase). MLP method produced the best model, with R2 score of 0.7672791942775417, relative MSE and relative MAE of approximately 15%. Feature importance calculations indicated that CODCr, Ammonium and Nitrate are features that highly correlates to BOD5. In the field study with a small private dataset consisting of water samples collected from two different lakes in Jiangsu Province of China, the trained model was found to have similar range of prediction error (around 15%), similar relative MAE (around 14%) and achieved about 6% better relative MSE.