This work considers a hybrid laser-battery powered uncrewed aerial vehicle (UAV) data collection system serving a passive Internet of Things deployment via monostatic backscatter communications. In this paper, we highlight the merits of the hybrid scheme over the laser only or battery only powered devices UAVs in terms of improved reach and durability. In addition, we study the laser energy consumption - destination battery level retention tradeoff optimization problem. Throughout this process, we optimize the single-rotor UAV’s three-dimensional trajectory, the UAV’s and the laser’s radiated power profiles, and the temporal battery usage profile while adopting path discretization. The resulting non-convex problem is solved via single-block successive convex approximation, for which novel bounds for the UAV propulsion energy, harvested energy, and collected data assuming a probabilistic line-of-sight channel model are derived. Finally, the simulation results show significant data collection gains, battery energy savings, and laser energy consumption reductions compared with a baseline scheme and highlight the complexity-optimality tradeoff.

Uncrewed aerial vehicles (UAVs) are expected to play a pivotal role in 6G networks due to their versatility and adaptability. One potential application for UAVs is wildfire coverage, as they can carry various sensors, including cameras and antennas. This study focuses on the multi-UAV trajectory optimization for wildfire coverage while satisfying multiple constraints, including the UAV dynamics, network connectivity, and limited energy batteries. The resulting complex optimization problem is time-varying and non-convex. To address this challenge, reinforcement learning, specifically the twin-delayed deep deterministic policy gradient algorithm, is adopted. A distributed learning procedure is devised to allow parallelization and significant reduction of the training time. The result is high coverage at standard flying altitudes with finite energy batteries.

Suitably equipped with cameras and sensors, uncrewed aerial vehicles (UAVs) can be instrumental for wildfire prediction, tracking, and monitoring, provided that uninterrupted connectivity can be guaranteed even if some of the ground access points (APs) are damaged by the fire itself. A cell-free network structure, with UAVs connecting to a multiplicity of APs, is therefore ideal in terms of resilience. This work proposes a trajectory optimization framework for a UAV swarm tracking a wildfire while maintaining cell-free connectivity with ground APs. Such optimization entails a constant repositioning of the multiplicity of UAVs as the fire evolves to ensure that the best possible view is acquired and transmitted reliably, while respecting altitude limits, avoiding collisions, and proceeding to recharge batteries as needed. Given the complexity and time-varying nature of this multi-UAV trajectory optimization, reinforcement learning is leveraged, specifically the twin-delayed deep deterministic policy gradient algorithm. The approach is shown to be highly effective for wildfire tracking and coverage and could be likewise applicable to survey other natural and man-made phenomena, including weather events, earthquakes, or chemical spills.

This paper investigates the maximization of har-vested data in a laser-powered uncrewed aerial vehicle (UAV) supporting Internet of Things (IoT) deployment. The system enables battery-free IoT devices to establish communication links with the UAV via bistatic backscattering with the aid of a power beacon source. Upon considering an unspecified flying time, we adopt path discretization and resort to the single-block successive convex approximation (SCA) to solve the data collection maximization problem. In addition to considering the UAV dynamics and power budget, two novel SCA-compatible bounds are introduced for the product of mixed convex/concave positive functions. Finally, the simulations conducted show that the proposed algorithm provides 90% increase in collected data under different operation conditions.

Recently, the use of uncrewed aerial vehicles (UAVs) in joint sensing and communication applications has received a lot of attention. However, integrating UAVs in current cellular systems presents major challenges related to trajectory optimization and interference management among others. This paper considers a multi-cell network including a UAV, which senses and forwards the sensory data from different events to the central base station. Particularly, the current manuscript covers how to design the UAV’s (i) 3D trajectory, (ii) power allocation, and (iii) sensing scheduling such that (a) a set of events are sensed, (b) interference to neighboring cells is kept at bay, and (c) the amount of energy required by the UAV is minimized. The resulting nonconvex optimization problem is tackled through a combination of (i) low-complexity binary optimization, (ii) successive convex approximation, and (iii) the Lagrangian method. Simulation results over a range of various key parameters have shown the merits of our approach, which consumes 33%-200% less energy compared to different benchmarks.

Uncrewed Aerial Vehicles (UAVs) provide a compelling solution for data collection in Internet of Things (IoT) networks due to their mobility and adaptability. However, the line-of-sight dominance in their channels may result in severe interference to ground users during UAV operations. To address this, we present an optimization framework that concurrently optimizes UAV trajectories and transmit powers. Our approach efficiently results in the collection of data from a variety of IoT sensors while (a) minimizing the UAVs flying time and (b) mitigating interference with terrestrial networks. Given the complex nature of such an optimization problem, this paper leverages reinforcement learning, specifically the twin delayed deep deterministic policy gradient algorithm, where a distributed learning algorithm is presented. Experimental results validate the efficacy of our proposed approach, demonstrating its capability to significantly enhance data collection in IoT networks while minimizing UAV flight time and interference with ground user links.

The use of uncrewed aerial vehicles (UAVs) in cell-free networks is poised to unleash a number of new opportunities to further improve wireless networks. However, cell-free UAV networks present major challenges related to the wireless nature of access and fronthaul links. This manuscript studies the uplink of cell-free systems where users connect to UAVs, the latter devices forwarding the information to a processing point through imperfect wireless fronthaul links. Three multiple access alternatives are considered for the fronthaul, namely frequency division multiples access, spatial division multiple access, and combinations thereof. Deterministic equivalent expressions for the spectral efficiency under these fronthaul schemes and minimum mean-square error reception are derived. Then, the optimization subproblems of (a) the 3D deployment of the UAVs, (b) the user transmit powers, and (c) the UAV transmit powers, are investigated. The joint optimization of these subproblems yields superior performance, with the 3D deployment being the main source of improvement.

We investigate Cell-Free massive MIMO networks, where each access point (AP) is equipped with a hybrid transceiver, reducing the complexity and cost compared to a fully digital transceiver. Asymptotic approximations for the spectral efficiency are derived for uplink and downlink. Capitalizing on these expressions, a max-min problem is formulated enabling us to optimize the (i) analog beamformer at the APs and (ii) pilot assignment. Simulations show that the optimization of these variables substantially increases the minimum user throughput.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks.

Cell-free (CF) structures are expected to be a game changer for beyond-5G wireless networks. With every user potentially communicating with every base station, cooperation at a central processing point is poised to provide much higher spectral efficiencies. At the same time, the growing interest in unmanned aerial vehicles (UAVs) makes CF-UAV networks an appealing scenario. This paper investigates the uplink of a CF network where UAVs serve as flying base stations. The optimization of the UAV locations is shown to markedly increase the minimum local-average signal-to-interference-plus-noise ratio, which in turn increases the spectral efficiency. The improvements are associated to pilot contamination and to geometry.

Unmanned Aerial Vehicle (UAV) is a promising technology to solve many new challenging problems. It provides high maneuverability and control, low manufacturing cost with respect to other flying technologies and many other features. In particular, there is an increasing interest in UAVs in the field of wireless communications, due to their capacity to carry on transceivers and establish communication among UAVs, or between UAVs and ground base stations/users. In this work, we investigate a UAV deployment in which each flying vehicle serves a set of users, carrying directional antennas. To do so, we maximize the minimum downlink rate among the users that a UAV serves. Due to the non-convexity of the problem, we will divide it into four sub-problems. Afterwards, an iterative algorithm is proposed to optimize the four sub-problems by using the block coordinate descent method, successive convex approximation and sequential quadratic programming. Simulation results show that the addition of directional antennas results in a better performance in terms of throughput compared with omni-directional benchmarks.

Decision-making procedures, when a set of individual binary labels is processed to produce a unique joint decision, can be approached modeling the individual labels as multivariate independent Bernoulli random variables. This probabilistic model allows an unsupervised solution using EM-based algorithms, which basically estimate the distribution model parameters and take a joint decision using a maximum a posteriori criterion. These methods usually assume that individual decision agents are conditionally independent, an assumption that might not hold in practical setups. Therefore, in this work we formulate and solve the decision-making problem using an EM-based approach, but assuming correlated decision agents. Improved performance is obtained on synthetic and real datasets, compared to classical and state-of-the-art algorithms.