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The growing use of Unmanned Aerial Vehicles (UAVs) for various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground terminals. Depending on the application, UAV-mounted wireless equipment can either be an aerial user equipment (AUE) that co-exists with the terrestrial users, or it can be a part of wireless infrastructure providing a range of services to the ground users. For instance, AUE can be used for real-time search and rescue and/or video streaming (surveillance, broadcasting) and Aerial Base Station (ABS) can enhance coverage, capacity and energy efficiency of wireless networks. In both cases, UAV-based solutions are scalable, mobile, easy and fast to deploy. However, several technical challenges have to be addressed before such solutions will become widely used. In this work, we present a tutorial on wireless communication with UAVs, taking into account a wide range of potential applications. The main goal of this work is to provide a complete overview of the main scenarios (AUE and ABS), channel and performance models, compare them, and discuss open research points. This work is intended to serve as a tutorial for wireless communication with UAVs, which gives a comprehensive overview of the research done until now and depicts a comprehensive picture to foster new ideas and solutions while avoiding duplication of past work. We start by discussing the open challenges of

Up until now, path planning for unmanned aerial vehicles (UAVs) has mainly been focused on the optimisation towards energy efficiency. However, to operate UAVs safely, wireless coverage is of utmost importance. Currently, deployed cellular networks often exhibit an inadequate performance for aerial users due to high amounts of intercell interference. Furthermore, taking the never-ending trend of densification into account, the level of interference experienced by UAVs will only increase in the future. For the purpose of UAV trajectory planning, wireless coverage should be taken into account to mitigate interference and to lower the risk of dangerous connectivity outages. In this paper, several path planning strategies are proposed and evaluated to optimise wireless coverage for UAVs. A simulator using a real-life 3D map is used to evaluate the proposed algorithms for both 4G and 5G scenarios. We show that the proposed Coverage-Aware A* algorithm, which alters the UAV's flying altitude, is able to improve the mean SINR by 3-4dB and lower the cellular outage probability by a factor of 10. Furthermore, the outages that still occur have a 60% shorter length, hence posing a lower risk to induce harmful accidents.

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Network slicing, a key enabler for future wireless networks, divides a physical network into multiple logical networks that can be dynamically created and configured. In current IEEE 802.11 (Wi-Fi) networks, the only form of network configuration is a rule-based optimization of few parameters. Future access points (APs) are expected to have self-organizational capabilities, able to deal with large configuration spaces in order to dynamically configure each slice. Deep Reinforcement Learning (DRL) can achieve promising results in highly dynamic and complex environments without the need for an operating model, by learning the optimal strategy after interacting with the environment. However, since the number of possible slice configurations is huge, achieving the optimal strategy requires an exhaustive learning period that might yield an outdated slice configuration. In this paper, we propose a fast-learning DRL model that can dynamically optimize the slice configuration of unplanned Wi-Fi networks without expert knowledge. Enhanced with an off-line learning step, the proposed approach is able to achieve the optimal slice configuration with a fast convergence, which is attractive for dynamic scenarios.

Fine-grained indoor localization has attracted attention recently because of the rapidly growing demand for indoor location-based services (ILBS). Specifically, massive (largescale) multiple-input and multiple-output (MIMO) systems have received increasing attention due to high angular resolution. This paper presents an indoor localization testbed based on a massive MIMO orthogonal frequency-division multiplexing (OFDM) system, which supports physical-layer channel measurements. Instead of exploiting channel state information (CSI) directly for localization, we focus on positioning from the perspective of multipath components (MPCs), which are extracted from the CSI through the space-alternating generalized expectationmaximization (SAGE) algorithm. On top of the available MPCs, we propose a generalized fingerprinting system based on different single-metric and hybrid-metric schemes. We evaluate the impact of varying antenna topologies, feeding metrics, sizes of the training set, and fingerprinting methods. The experimental results show that the proposed fingerprinting method can achieve centimeter-level positioning accuracy with a relatively small training set. Specifically, the distributed uniform linear array obtains the highest accuracy with about 1.63-2.5-cm mean absolute errors resulting from the high spatial resolution.

This paper considers the problem of radar sensing by using a large number of antennas. We use the orthogonal frequency division multiplexing (OFDM) waveform, and show that the large arrays used in massive multiple-input multipleoutput (MIMO) communications enable accurate localization in the array near-field, even at the narrow bandwidths typically encountered at low carrier frequencies. We validate our findings experimentally with a massive MIMO testbed operating at 3.5 GHz carrier frequency and 18 MHz OFDM bandwidth in an indoor environment. We consider a single moving cylinder, and demonstrate a median accuracy of (3.4, 5.6) cm in (x, y) in the near-field. We show that the accuracy is maintained with only a single subcarrier, and that the resolution increases with an order of magnitude when combining all antennas, effectively surpassing the 16.67 m bistatic range resolution set by the OFDM waveform. We use a radar symbol duration of 71.88 µs at an effective transmission period of 2.5 ms, which indicates that the radar and communication systems can be implemented in timedivision with a capacity loss of only 2.9%. Our results suggest that near-field radar sensing can be integrated into future massive MIMO systems operating at low carrier frequencies and narrow bandwidths.

Massive MIMO (MaMIMO) Channel State Information (CSI) based user positioning systems using Convolutional Neural Networks (CNNs) show great potential, reaching a very high accuracy without introducing any overhead in the MaMIMO communication system. In this study, we show that both these systems can position indoor users in both Line-of-Sight and in non-Line-of-Sight conditions with an accuracy of around 20 mm. However, to further develop these positioning systems, more insight in how the CNN infers the position is needed. The used CNNs are a black box and we can only guess how they position the users. Therefore, the second focus of this paper is on opening the black box using several experiments. We explore the current limitations and promises using the open dataset gathered on a real-life 64-antenna MaMIMO testbed. In this way, extra insight in the system is gathered, guiding research on MaMIMO CSIbased positioning systems using CNNs in the right direction.