Quantum networks connect devices to facilitate the transmission of quantum bits (qubits), harnessing quantum mechanics for groundbreaking telecommunications applications. The quantum state exchange rely on quantum entangled particles which have to be sent from a Quantum Base Station (QBS) to Quantum Nodes (QNs) through an optical channel. Free Space Optical (FSO) links promise flexibility and cost advantages over the classical fiber-based infrastructure. However, the absence of Line of Sight (LoS) between the QBS and QNs may completely disrupt the communication. Therefore, this work focuses on an Intelligent Reflective Surface (IRS)-assisted Unmanned Aerial Vehicle (UAV)-aided FSO quantum network. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV hovering locations, constrained by fidelity and link-per-IRS limits. The intractability of the original problem is first tackled with a sigmoid-based approximations, subsequently split into three sub-problems, sequentially solved applying the Successive Convex Approximation (SCA) technique. Numerical results (i) validate the approximation accuracy, and (ii) demonstrates the effectiveness of the proposal against three baseline approaches, showing an order of magnitude increase in terms of fairness.

Unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRSs) represent a cutting-edge technology for improving the channel capacity of wireless communications, by capitalizing on UAVs’ 3-D mobility coupled with the IRSs’ smart radio capabilities. This work envisions a scenario in which a swarm of UAVs equipped with IRSs serves multiple Internet of Things (IoT) ground nodes (GNs) concurrently transmitting to a single base station (BS) via OFDMA. The huge number of passive elements composing the IRSs introduces a significant complexity in the mission design. Therefore, each IRS is divided into patches that can be simultaneously used to serve different nodes. Considering general Rician fading, a comprehensive channel model for IRS-assisted UAV-aided networks is derived. Then, a multiobjective mixed-integer nonlinear programming problem is conceived to maximize the sum-rate of the GNs and, at the same time, minimize the difference among the users’ data rates, by jointly optimizing the trajectories and the phase shift matrices. This nonconvex problem, reformulated in terms of scheduling (i.e., patch-GN assignment), is challenging to solve. Hence, it is rearranged as a Markov Decision Process and a quasi-optimal solution is obtained via Deep Reinforcement Learning. Extensive simulation analysis is performed to validate the results and the accuracy of the proposed model.

Quantum Internet (QI) is a system of interconnected quantum computers able to exchange information encoded in the so called quantum bits (qubits). Differently from the classical counterpart, qubits benefit from a manifold properties guaranteed by quantum mechanics, such as superposition and entanglement. Despite the fact that quantum networks bring significant advantages, several phenomena can negatively impact the overall system, potentially hindering communication. In order to evaluate the network performance, a comprehensive probability expression is derived in this work to ultimately determine how many qubits are expected to be successfully received by nodes. On this basis, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to fairly maximize the qubits exchanged between node pairs and jointly optimize (i) the position of the quantum source, and (ii) the entanglement distribution plan. To cope with the non-convexity of the problem, an iterative optimization algorithm, leveraging Block Coordinate Descendent (BCD) and Successive Convex Approximation (SCA) techniques, is proposed. A thorough simulation campaign is conducted to corroborate the theoretical findings. Numerical results demonstrates, under different parameter setups, that the proposed algorithm provides superior performance with respect to a baseline approach.

Non-Terrestrial Networks represent a valuable solution for providing connectivity to Internet of Things (IoT) devices in remote areas, where classical infrastructure is unavailable. Due to the low-power nature of IoT devices, an Unmanned Aerial Vehicle (UAV) can prevent the energy depletion of these Ground Nodes (GNs) by employing Wireless Power Transfer through an array antenna. Starting from the mathematical modeling of such a scenario, two Mixed-Integer Non-Linear Programming problems are formulated to fairly maximize (i) the energy distribution and (ii) the total amount of data transmitted to a Low Earth Orbit CubeSat. Therefore, it is necessary to optimize the drone kinematics, the transmission scheduling plan, and the beamforming vectors of the array antenna. To cope with their non-convexity, both problems are mathematically manipulated to reach a tractable form, for which two optimization algorithms are proposed and their complexity analyzed. To prove the effectiveness of the overall solution, a comprehensive simulation campaign is conducted under several parameter settings, such as number of GNs and UAV antenna elements with different transmission power levels. Finally, the proposal is compared with a baseline, which confirms the superiority of the proposal up to 7 times in terms of total transmitted data.

The Internet of Drones (IoD) is a networking architecture that stems from the interplay between Unmanned Aerial Vehicles (UAVs) and wireless communication technologies. Networked drones can unleash disruptive scenarios in many application domains. At the same time, to really capitalize their potential, accurate modeling techniques are required to catch the fine details that characterize the features and limitations of UAVs, wireless communications, and networking protocols. To this end, the present contribution proposes the Internet of Drones Simulator (IoD-Sim), a comprehensive and versatile open source tool that addresses the many facets of the IoD. IoD-Sim is a Network Simulator 3 (ns-3)-based simulator organized in a 3-layer stack, composed by (i) the Underlying Platform, which provides the telecommunication primitives for different standardized protocol stacks, (ii) the Core, that implements all the fundamental features of an IoD scenario, and (iii) the Simulation Development Platform, mainly composed by a set of tools that speeds up the graphical design for every possible use-case. In order to prove the huge potential of this proposal, three different scenarios are presented and analyzed from both a software perspective and a telecommunication standpoint. The peculiarities of this open-source tool are of interest for researchers in academia, as they will be able to extend to model upcoming specifications, including, but not limited to, mobile networks and satellite communications. Still, it will certainly be of relevance in industry to accelerate the design phase, thus improving the time-to-market of IoD-based services.

The Internet of Drones paradigm is considered as a key enabler for several cutting edge verticals, including surveillance, planetary exploration, protection, loads transportation, and aerology. The main limitations to its wide-scale adoption arise from the constraints on the resources available onboard of drones: this concerns energy, computational and storage capabilities. Unfortunately, current literature mainly focuses on energy limitations, leaving unexplored the interplay with other constraints. To bridge this gap, the present contribution also encompasses the limitations on the memory onboard, which can be critical when drones have to acquire high resolution multimedia signals for ambient awareness services. In particular, an iterative stochastic approach is conceived hereby to tune data flows from/to drones subject to energy and memory constraints in order to fulfill an Out-of-Service probability below a given threshold. Stemming from the proposed approach, two algorithms have been also designed that seek a different complexity-performance tradeoff. The first one is less complex and more conservative, since it plans the mission once at the beginning. The second, instead, is slightly more complex and aggressive but it allows the drone to gather and upload a higher volume of data and shorten the gap with respect to the ideal case.