Autonomous unmanned aerial vehicles (UAVs) can be utilized as aerial relays to serve users far from terrestrial infrastructure. Unfortunately, existing algorithms for aerial relay path planning cannot accommodate general flight constraints or channel models. This is required in practice due to connectivity constraints, the presence of obstacles (e.g. buildings), and regulations. This paper proposes a framework that overcomes these limitations by spatially discretizing the flight region. To cope with the resulting exponential growth in complexity, the framework adopts a probabilistic roadmap approach, where a shortest path is found through a graph of randomly generated states. To attain high optimality with affordable complexity, the probability distribution used to generate these states is designed based on heuristic path planners with theoretical guarantees. The algorithms derived in this framework not only overcome the main limitations of existing schemes but also entail smaller computational complexity. Extensive theoretical and numerical results corroborate the merits of the proposed approach.

Radio map estimation (RME) involves spatial interpolation of radio measurements to predict metrics such as the received signal strength at locations where no measurements were collected. The most popular estimators nowadays project the measurement locations onto a regular grid and complete the resulting measurement tensor with a convolutional deep neural network. Unfortunately, these approaches suffer from poor spatial resolution and require a very large number of parameters. The first contribution addresses these limitations by means of an attention-based estimator based on transformers, which are AI models that achieved widespread popularity since they are the technology behind chatbots such as ChatGPT. The proposed scheme, named Spatial TransfOrmer for Radio Map estimation (STORM), not only outperforms the existing estimators, but also exhibits lower computational complexity, translation equivariance, rotation equivariance, and full spatial resolution. The second contribution is an extended transformer architecture that allows STORM to perform active sensing, by which the next measurement location is selected based on the previous measurements. This is particularly useful for minimization of drive tests (MDT) in cellular networks, where operators request user equipment to collect measurements. Finally, STORM is extensively validated by experiments with one ray-tracing and two real-measurement datasets.

The deployment of aerial base stations (ABSs) on unmanned aerial vehicles (UAVs) presents a promising solution for extending cellular connectivity to areas where terrestrial infrastructure is overloaded, damaged, or absent. A pivotal challenge in this domain is to decide the locations of a set of ABSs to effectively serve ground-based users. Most existing approaches oversimplify this problem by assuming that the channel gain between two points is a function of solely distance and, sometimes, also the elevation angle. In turn, this paper leverages propagation radio maps to account for arbitrary air-to-ground channel gains. This methodology enables the identification of an approximately minimal set of locations where ABSs need to be deployed to ensure that all ground terminals achieve a target service rate, while adhering to backhaul capacity limitations and avoiding designated no-fly zones. Relying on a convex relaxation technique and the alternating direction method of multipliers (ADMM), this paper puts forth a scalable solver whose computational complexity scales linearly with the number of ground terminals. Convergence is established analytically and an extensive set of simulations corroborate the merits of the proposed scheme relative to conventional methods.

Unmanned aerial vehicles (UAVs) with on-board relays can be used to establish multi-hop links that deliver high-speed connectivity beyond cell limits. This is of utmost importance e.g. in remote areas and in emergency scenarios. However, jointly designing the trajectories of multiple such flying relays is a complex task since the dimensionality of the underlying configuration space is too large to allow the direct application of traditional shortest-path methods. To bypass this difficulty, this work proposes a probabilistic roadmap algorithm based on a novel heuristic path design which is guaranteed to provide feasible paths for all UAVs under general conditions. This addresses the limitations of existing algorithms, which are typically based on non-linear optimization and, therefore, entail high complexity and cannot readily accommodate the presence of obstacles such as buildings. As corroborated via numerical experiments in an urban environment, the proposed scheme can establish a high-speed link with a user by means of just two aerial relays in a short time.

Mobile base stations on board unmanned aerial vehicles (UAVs) promise to deliver connectivity to those areas where the terrestrial infrastructure is overloaded, damaged, or absent. A fundamental problem in this context involves determining a minimal set of locations in 3D space where such aerial base stations (ABSs) must be deployed to provide coverage to a set of users. While nearly all existing approaches rely on average characterizations of the propagation medium, this work develops a scheme where the actual channel information is exploited by means of a radio tomographic map. A convex optimization approach is presented to minimize the number of required ABSs while ensuring that the UAVs do not enter no-fly regions. A simulation study reveals that the proposed algorithm markedly outperforms its competitors.