Cellular connectivity for UAVs: Network modeling, performance analysis, and design guidelines

The growing use of aerial user equipments (UEs) in various applications requires ubiquitous and reliable connectivity for safe control and data exchange between these devices and ground stations. Key questions that need to be addressed when planning the deployment of aerial UEs are whether the cellular network is a suitable candidate for enabling such connectivity and how the inclusion of aerial UEs might impact the overall network efficiency. This paper provides an in-depth analysis of user and network-level performance of a cellular network that serves both unmanned aerial vehicles (UAVs) and ground users in the downlink. Our results show that the favorable propagation conditions that UAVs enjoy due to their height often backfire on them, as the increased load-dependent co-channel interference received from neighboring ground base stations (BSs) is not compensated by the improved signal strength. When compared with a ground user in an urban area, our analysis shows that a UAV flying at 100 m can experience a throughput decrease of a factor 10 and a coverage drop from 76% to 30%. Motivated by these findings, we develop UAV and network-based solutions to enable an adequate integration of UAVs into cellular networks. In particular, we show that an optimal tilting of the UAV antenna can increase the coverage from 23% to 89% and throughput from 3.5 to 5.8 b/s/Hz, outperforming ground UEs. Furthermore, our findings reveal that depending on the UAV altitude and its antenna configuration, the aerial user performance can scale with respect to the network density better than that of a ground user. Finally, our results show that network densification and the use of microcells limit the UAV performance. Although UAV usage has the potential to increase the area spectral efficiency (ASE) of cellular networks with a moderate number of cells, they might hamper the development of future ultradense networks

An Adaptive Algorithm to Optimize the Dynamics of IEEE 802.15.4 Networks

Presentado en ICST 2013IEEE 802.15.4 standard is becoming one of the most popular technologies for the deployment of low rate Wireless Personal Area Networks with strong power constraints. In order to reduce the energy consumption, beacon-enabled networks with long network inactive periods can be employed. However, the duration of these inactivity periods, as some other configuration parameters, are conventionally set to default values and remain fixed during the whole network operation. This implies that if they are misconfigured the network will not adapt to changes in the conditions of the environment, particularly to the most determining one, i.e. the traffic load. This paper proposes a simple procedure for the dynamic adaptation of several key parameters of IEEE 802.15.4 networks. Under this procedure, the 802.15.4 parameters are modified as a function of the existing traffic conditions.Spanish National Project No.TEC2009-13763-C02-01

2D Angularly Dependent Array Error Calibration for 1D Array via Neural Network with Local Manifold Interpolation

The calibration of the angularly dependent array error is a challenging task for signal processing. In this paper, we propose a neural network (NN)-based two-dimensional (2D) calibration method for a linear array. Firstly, the array steering vectors are measured on an azimuth grid at different elevations in an anechoic chamber, and the off-grid steering vectors are derived by the proposed local manifold interpolation (LMI) technique to reduce the risk of model overfitting. Then, the phase differences are extracted to form the features of the training data. At last, noise is added to the training data to enable the NN model to generalize well to the noisy data. The proposed method is evaluated by the indoor and outdoor measured data from a 77 GHz automotive radar and is compared with the conventional signal processing-based methods. The evaluation results show that a single NN model trained at the lowest signal-to-noise ratio (SNR) outperforms conventional methods by at least 55% on average over the entire SNR range and gives close performance to the perfect array without array error at low to medium SNR

Coverage maximization for a poisson field of drone cells

The use of drone base stations to provide wireless connectivity for ground terminals is becoming a promising part of future technologies. The design of such aerial networks is however different compared to cellular 2D networks, as antennas from the drones are looking down, and the channel model becomes height-dependent. In this paper, we study the effect of antenna patterns and height-dependent shadowing. We consider a random network topology to capture the effect of dynamic changes of the flying base stations. First we characterize the aggregate interference imposed by the co-channel neighboring drones. Then we derive the link coverage probability between a ground user and its associated drone base station. The result is used to obtain the optimum system parameters in terms of drones antenna beamwidth, density and altitude. We also derive the average LoS probability of the associated drone and show that it is a good approximation and simplification of the coverage probability in low altitudes up to 500 m according to the required signal-to-interference-plus-noise ratio (SINR)

Remote ID for separation provision and multi-agent navigation

In this paper, we investigate the integration of drone identification data (Remote ID) with collision avoidance mechanisms to improve the safety and efficiency of multi-drone operations. We introduce an improved Near Mid-Air Collision (NMAC) definition, termed as UAV NMAC (uNMAC), which accounts for uncertainties in the drone's location due to self-localization errors and possible displacements between two location reports. Our proposed uNMAC-based Reciprocal Velocity Obstacle (RVO) model integrates Remote ID messages with RVO to enable enhanced collision-free navigation. We propose modifications to the Remote ID format to include data on localization accuracy and drone airframe size, facilitating more efficient collision avoidance decisions. Through extensive simulations, we demonstrate that our approach halves mission execution times compared to a conservative standard Remote ID-based RVO. Importantly, it ensures collision-free operations even under localization uncertainties. By integrating the improved Remote ID messages and uNMAC-based RVO, we offer a solution to significantly increase airspace capacity while adhering to strict safety standards. Our study emphasizes the potential to augment the safety and efficiency of future drone operations, thereby benefiting industries reliant on drone technologies.Comment: 10 pages, 8 figures, 2023 IEEE/AIAA 42nd Digital Avionics Systems Conference (DASC

Financial Transaction Tax: Small is Beautiful

The case for taxing financial transactions merely to raise more revenues from the financial sector is not particularly strong. Better alternatives to tax the financial sector are likely to be available. However, a tax on financial transactions could be justified in order to limit socially undesirable transactions when more direct means of doing so are unavailable for political or practical reasons. Some financial transactions are indeed likely to do more harm than good, especially when they contribute to the systemic risk of the financial system. However, such a financial transaction tax should be very small, much smaller than the negative externalities in question, because it is a blunt instrument that also drives out socially useful transactions. There is a case for taxing over-the-counter derivative transactions at a somewhat higher rate than exchange-based derivative transactions. More targeted remedies to drive out socially undesirable transactions should be sought in parallel, which would allow, after their implementation, to reduce or even phase out financialtransaction taxes