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, modeled considering drone kinematics and optical channel dynamics. An optimization problem is formulated to fairly maximize qubit reception at QNs by optimizing UAV trajectories, speeds, and accelerations, constrained by fidelity and link-per-IRS limits. The intractability of the original formulation is tackled with a dedicated strategy which combines sigmoid-based approximations, Block Coordinate Descent (BCD), and Successive Convex Approximation (SCA) techniques to achieve a quasi-optimal solution. Numerical results (i) validate the approximation accuracy, (ii) compare the effectiveness of the proposal against a baseline approach under varying conditions, and (iii) show the interplay among the degrees of freedom and the relation with the scenario parameters.

This work presents a robust approach to derive a quasi-optimal ground segment design in Q/V band for providing connectivity to non-geostationary satellites, considering a "N+P" diversity scheme to cope with rain fading. An optimization problem is formulated with the objective of guaranteeing to each satellite a minimum uplink sum-rate while minimizing the total number of GWs via a ℓ0-norm representation. A two-stage algorithm is proposed to tackle the non-convexity of the above and achieve GW diversity. Numerical results demonstrated that the proposal outperforms algorithms in the literature by reducing the required total number of GWs by 47%-67%.

This work investigates a Holographic Multiple-Input Multiple-Output (HMIMO) communication system consisting of a source and a receiver being two surfaces in the LoS configuration. The closed-form expressions of the electric field and the channel coupling coefficients are obtained leveraging an asymptotic expansion of the Green's function. The derived approximations show satisfactory accuracy in both near-and farfield conditions, across low and high frequencies. These are then employed to obtain insights regarding (i) the spatial distribution of the beams, and (ii) the mapping relation between source and receiver modes while ensuring sufficient orthogonality under prescribed conditions.

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’ 3D 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 multi-objective mixed-integer non-linear 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 non-convex 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.

In this work, the existence of a correspondence between fundamental programming language control structures and mathematical formulation is proven. The proposed interpretation is given through a well-defined logical circuit analytical expression. Relevant geometrical applications having wide implications in engineering branches are presented together with a new Penalty Method for constrained optimization problems handling.