While classical control theory assumes that the controller has access to measurements of the entire state (or output) at every time instant, this paper investigates a setting where the feedback controller can only access a randomly selected subset of the state vector at each time step. Due to the random sparsification that selects only a subset of the state components at each step, we analyze the stability of the closed-loop system in terms of Asymptotic Mean-Square Stability (AMSS), which ensures that the system state converges to zero in the mean-square sense. We consider the problem of designing both a feedback gain matrix and a measurement sparsification strategy that minimizes the number of state components required for feedback, while ensuring AMSS of the closed-loop system. Interestingly, (1) we provide conditions on the dynamics of the system under which it is possible to find a sparsification strategy, and (2) we propose a Linear Matrix Inequality (LMI) based algorithm that jointly computes a stabilizing gain matrix, and a randomized sparsification strategy that minimizes the expected number of measured state coordinates while preserving the AMSS. Our approach is then extended to the case where the sparsification probabilities vary across the state components. Based on these theoretical findings, we propose an algorithmic procedure to compute the vector of sparsification parameters, along with the corresponding feedback gain matrix. To the best of our knowledge, this is the first study to investigate the stability properties of control systems that rely solely on randomly selected state measurements. Numerical simulations demonstrate that, in some settings, the system achieves comparable performance to full-state feedback while requiring measurements from only 0.3 percent of the state coordinates.

Multirotor Aerial Vehicles (MRAVs) when integrated into wireless communication systems and equipped with a Reflective Intelligent Surface (RIS) enhance coverage and enable connectivity in obstructed areas. However, due to limited degrees of freedom (DoF), traditional under-actuated MRAVs with RIS are unable to control independently both the RIS orientation and their location, which significantly limits network performance. A new design, omnidirectional MRAV (o-MRAV), is introduced to address this issue. In this paper, an o-MRAV is deployed to assist a terrestrial base station in providing connectivity to obstructed users. Our objective is to maximize the minimum data rate among users by optimizing the o-MRAV’s orientation, location, and RIS phase shift. To solve this challenging problem, we first smooth the objective function and then apply the Parallel Successive Convex Approximation (PSCA) technique to find efficient solutions. Our simulation results show significant improvements of 28% and 14% in terms of minimum and average data rates, respectively, for the o-MRAVs compared to traditional u-MRAVs.

Recent research in communications-aware robotics has been propelled by advancements in 5G and emerging 6G technologies. This field now includes the integration of Multi-Rotor Aerial Vehicles (MRAVs) into cellular networks, with a specific focus on under-actuated MRAVs. These vehicles face challenges in independently controlling position and orientation due to their limited control inputs, which adversely affects communication metrics such as Signal-to-Noise Ratio. In response, a newer class of omnidirectional MRAVs has been developed, which can control both position and orientation simultaneously by tilting their propellers. However, exploiting this capability fully requires sophisticated motion planning techniques. This paper presents a novel application of omnidirectional MRAVs designed to enhance communication security and thwart eavesdropping. It proposes a strategy where one MRAV functions as an aerial Base Station, while another acts as a friendly jammer to secure communications. This study is the first to apply such a strategy to MRAVs in scenarios involving eavesdroppers.

Reconfigurable Intelligent Surface (RIS) technology has gained significant attention due to its ability to enhance the performance of wireless communication systems. The main advantage of RIS is that it can be strategically placed in the environment to control wireless signals, enabling improvements in coverage, capacity, and energy efficiency. In this paper, we investigate a scenario in which a drone, equipped with a RIS, travels from an initial point to a target destination. In this scenario, the aerial RIS (ARIS) is deployed to establish a direct link between the base station and obstructed users. Our objective is to maximize the energy efficiency of the ARIS while taking into account its dynamic model including its velocity and acceleration along with the phase shift of the RIS. To this end, we formulate the energy efficiency problem under the constraints of the dynamic model of the drone. The studied problem is challenging to solve. To address this, we proceed as follows. First, we introduce an efficient solution that involves decoupling the phase shift optimization and the trajectory design. Specifically, the closed-form expression of the phase-shift is obtained using a convex approximation, which is subsequently integrated into the trajectory design problem. We then employ tools inspired by economic model predictive control (EMPC) to solve the resulting trajectory optimization. Our simulation results show a significant improvement in energy efficiency against the scenario where the dynamic model of the UAV is ignored.

Federated Learning (FL) has emerged as a promising decentralized machine learning (ML) paradigm where distributed clients collaboratively train models without sharing their private data. However, the heterogeneous properties of the clients, combined with the high dimensions of ML models considerably slow down the wall-clock convergence time. To address these challenges, we propose FedHC, a framework that jointly optimizes resource allocations and uplink compression levels of the clients to minimize the overall latency while respecting the energy budget and convergence guarantees. To solve the formulated optimization problem, we first derive the required number of global training rounds, to achieve the target accuracy. Then we propose an iterative algorithm, where at each step optimal CPU levels and bandwidth along with compression levels are derived. Our numerical results show the performance -with time reduction up to 4×- and robustness to non-IID data of our approach, compared to the benchmarks.

Space-air-ground integrated networks (SAGINs) will play a pivotal role in 6G communication systems. They are considered a promising technology for enhancing network capacity in densely populated urban areas and extending connectivity to rural regions. However, the complex, multilayered, and heterogeneous nature of SAGINs demands an innovative approach to designing their multi-tier associations. In this context, we propose a modeling of the SAGINs association problem using multi-sided matching theory. Our objective is to devise a reliable, asynchronous, and fully distributed approach that associates nodes across the layers to maximize the total end-to-end rate of the assigned agents. To achieve this, our problem is formulated as a multi-sided many-to-one matching game. We introduce a randomized matching algorithm with minimal information exchange. The algorithm is shown to reach an efficient and stable association between nodes in adjacent layers. Simulation results show that our proposed approach yields significant gains compared to both greedy and distance-based algorithms,

Semantic communication has gained attention as a key enabler for intelligent and context-aware communication. However, one of the key challenges of semantic communications is the need to tailor the resource allocation to meet the specific requirements of semantic transmission. In this paper, we focus on networks with limited resources where devices are constrained to transmit with limited bandwidth and power over large distance. Specifically, we devise an efficient strategy to select the most pertinent semantic features and participating users, taking into account the channel quality, the transmission time, and the recovery accuracy. To this end, we formulate an optimization problem with the goal of selecting the most relevant and accurate semantic features over devices while satisfying constraints on transmission time and quality of the channel. This involves optimizing communication resources, identifying participating users, and choosing specific semantic information for transmission. The underlying problem is inherently complex due to its non-convex nature and combinatorial constraints. To overcome this challenge, we efficiently approximate the optimal solution by solving a series of integer linear programming problems. Our numerical findings illustrate the effectiveness and efficiency of our approach in managing semantic communications in networks with limited resources.

Unmanned aerial vehicles (UAVs) are a highly promising technology with diverse applications in wireless networks. One of their primary uses is the collection of time-sensitive data from Internet of Things (IoT) devices. In UAV-assisted networks, the Age-of-Information (AoI) serves as a fundamental metric for quantifying data timeliness and freshness. In this work, we are interested in a generalized AoI formulation, where each packet's age is weighted based on its generation time. Our objective is to find the optimal UAVs' trajectories and the subsets of selected devices such that the weighted AoI is minimized. To address this challenge, we formulate the problem as a Mixed-Integer Nonlinear Programming (MINLP), incorporating time and quality of service constraints. To efficiently tackle this complex problem and minimize communication overhead among UAVs, we propose a distributed approach. This approach enables drones to make independent decisions based on locally acquired data. Specifically, we reformulate our problem such that our objective function is easily decomposed into individual rewards. The reformulated problem is solved using a distributed implementation of Multi-Agent Reinforcement Learning (MARL). Our empirical results show that the proposed decentralized approach achieves results that are nearly equivalent to a centralized implementation with a notable reduction in communication overhead.

This paper addresses the problem of Age-of-Information (AoI) in UAV-assisted networks. Our objective is to minimize the expected AoI across devices by optimizing UAVs’ stopping locations and device selection probabilities. To tackle this problem, we first derive a closed-form expression of the expected AoI that involves the probabilities of selection of devices. Then, we formulate the problem as a non-convex minimization subject to quality of service constraints. Since the problem is challenging to solve, we propose an Ensemble Deep Neural Network (EDNN) based approach which takes advantage of the dual formulation of the studied problem. Specifically, the Deep Neural Networks (DNNs) in the ensemble are trained in an unsupervised manner using the Lagrangian function of the studied problem. Our experiments show that the proposed EDNN method outperforms traditional DNNs in reducing the expected AoI, achieving a remarkable reduction of 29.5%.

Unmanned aerial vehicles (UAVs) have been proposed as a promising technology to collect data from IoT devices and relay it to the network. In this work, we are interested in scenarios where the data is updated periodically, and the collected updates are time-sensitive. In particular, the data updates may lose their value if they are not collected and analyzed timely. To maximize the data freshness, we optimize a new performance metric, namely the Age-of-Updates (AoU). Our objective is to carefully schedule the UAVs hovering positions and the users' association so that the AoU is minimized. Unlike existing works where the association parameters are considered as binary variables, we assume that devices send their updates according to a probability distribution. As a consequence, instead of optimizing a deterministic objective function, the objective function is replaced by an expectation over the probability distribution. The expected AoU is therefore optimized under quality of service and energy constraints. The original problem being non-convex, we propose an equivalent convex optimization that we solve using an interior-point method. Our simulation results show the performance of the proposed approach against a binary association.

This work considers the deployment of unmanned aerial vehicles (UAVs) over a predefined area to serve a number of ground users. Due to the heterogeneous nature of the network, the UAVs may cause severe interference to the transmissions of each other. Hence, a judicious design of the user-UAV association and UAV locations is desired. A potential game is defined where the players are the UAVs. The potential function is the total sum rate of the users. The agents' utility in the potential game is their marginal contribution to the global welfare or their so-called wonderful life utility. A game-theoretic learning algorithm, binary log-linear learning (BLLL), is then applied to the problem. Given the potential game structure, a consequence of our utility design, the stochastically stable states using BLLL are guaranteed to be the potential maximizers. Hence, we optimally solve the joint user-UAV association and 3-D-location problem. Next, we exploit the submodular features of the sum rate function for a given configuration of UAVs to design an efficient greedy algorithm. Despite the simplicity of the greedy algorithm, it comes with a performance guarantee of 1-1/e of the optimal solution. To further reduce the number of iterations, we propose another heuristic greedy algorithm that provides very good results. Our simulations show that, in practice, the proposed greedy approaches achieve significant performance in a few iterations.

In this paper, we study the joint 3D placement of unmanned aerial vehicles (UAVs) and users association under bandwidth limitation and quality of service constraints. In order to allow to UAVs to dynamically change their 3D locations in a distributed fashion while maximizing the network's sum-rate, we break the underlying optimization into 3 subproblems where we separately solve the 2D UAVs positioning, the altitude optimization, and the UAVs-users association. First, given fixed 3D positions of UAVs, we propose a fully distributed matching based association that alleviates the bottlenecks of the bandwidth and guarantees the required quality of service. Next, to address the 2D positions of UAVs, we adopt a modified version of K-means algorithm, with a distributed implementation, where UAVs dynamically change their 2D positions in order to reach the barycenter of the served users cluster. In order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited-interferences, the UAVs altitudes game is a non-cooperative potential game where the players (UAVs) can maximize the limited-interference sum-rate by only optimizing a local utility function. Our simulation results show that, using the proposed approach, the network's sum rate of the studied scenario is improved by 200% as compared with the trivial case where the classical version of K-means is adopted and users are assigned, at each iteration, to the closest UAV.

In this paper, we propose a distributed algorithm that allows unmanned aerial vehicles (UAVs) to dynamically learn their optimal 3D locations and associate with ground users while maximizing the network's sum-rate. Our approach is referred to as 'Learn-As-You-Fly' (LAYF) algorithm. LAYF is based on a decomposition process that iteratively breaks the underlying optimization into three subproblems. First, given fixed 3D positions of UAVs, LAYF proposes a distributed matching-based association that alleviates the bottlenecks of bandwidth allocation and guarantees the required quality of service. Next, to address the 2D positions of UAVs, a modified version of K-means algorithm, with a distributed implementation, is adopted. Finally, in order to optimize the UAVs altitudes, we study a naturally defined game-theoretic version of the problem and show that under fixed UAVs 2D coordinates, a predefined association scheme, and limited interference, the UAVs altitudes game is a potential game where UAVs can maximize the limited interference sum-rate by only optimizing a local utility function. Our simulation results show that the network's sum-rate is improved as compared to both a centralized suboptimal solution and a distributed approach that is based on closest UAVs association.

In this paper, we are interested in building a joint availability and access cost policy for Unmanned Aerial Vehicles (UAVs)-empowered flying access networks. Indeed, we build a duopoly model to capture the adversarial behavior of UAVs operators in terms of their pricing and availability strategies. That is UAVs operators need to decide about the optimal beaconing period (period needed to send short messages advertising the existence of a UAV) while saving energy. Furthermore, they need to decide about the best price strategy in order to maximize their respective market share. Therefore, a tractable analysis for the game's Nash Equilibrium, both in terms of pricing and availability is derived. We show that this special game exhibits some very interesting properties as it is sub-modular with respect to the availability policy, whereas it is super-modular with respect to the service fee. Furthermore, we implement a learning scheme using best-response dynamics that allows operators to learn their joint pricing-availability strategies in a fast, accurate yet completely distributed fashion. Extensive simulations show the convergence of the proposed schemes to the joint pricing-availability Nash equilibrium and provide attractive insights on how the game parameters could be set to control the duopoly.

In this paper, we address the problem of energy-aware user satisfaction in self-organizing networks. Our main objective is to meet with the users requirements while reducing energy consumption. Accordingly, we aim at seeking satisfaction equilibria, mainly the efficient satisfaction equilibrium (ESE). We first define conditions of existence and uniqueness of ESE. Considering information-theoretic transmission rate based measure, we fully characterize the ESE and prove that, whenever it exists, it is a solution of a linear system. We show that, at the ESE, no player can increase its Quality of Service without degrading the energy performance. Finally, in order to reach the ESE, we propose a fully distributed scheme based on the Banach-Picard algorithm and show, through simulation results, its qualitative properties.

Cooperative sensing enables secondary users to combine individual sensing results in order to attain sensing accuracies beyond those achieved by consumer RF devices. However, due to sensing costs, secondary users may prefer not to cooperate to the sensing task, leading to higher false alarm probability. In this paper, we study how information about the presence of cooperators affects the dynamics of cooperative sensing schemes. We consider two scenarios, namely the case when SUs cannot detect the presence of other potential cooperators, and the case when SUs have prior information on the presence of other SUs in radio range. Using an evolutionary game framework, we demonstrate that protocols delivering such type of information to SUs reduce cooperation and ultimately lead to degraded network performance. Finally, a learning process based on the replicator dynamics is proposed which is capable to drive the system to the evolutionary stable solution. The results of the paper are illustrated through numerical simulations.

Cooperative sensing is a promising technique that enables secondary users (SUs) to combine their channel observations in order to improve the spectrum sensing accuracy. However, in an adversarial environment where the secondary nodes are particularly selfish and the spectrum sensing is energy costly, selfish SUs can easily exploit the spectrum sensing result without participating in the sensing process. In this paper, we model the cooperative sensing as a volunteer dilemma where SUs have the choice to volunteer in order to sense and share the spectrum sensing results, or to free ride the spectrum sensing and achieve possibly a higher profitability. We mainly give a full characterization of the Nash equilibria and prove a counterintuitive property that claims: the probability of volunteering decreases when the number of SUs increases. Additionally, we propose a practical negotiation algorithm in order to select efficiently one SU to access to the channel. Finally, we propose a distributed algorithm that converges to Nash equilibria and show its performance through simulation results.

Heterogeneous deployment of base stations with different transmit power levels helps increase the network efficiency. Yet, the allocation of powers to base stations should be optimal in order to minimize interferences and thus improve users through-put. In this paper, we present a game theoretical approach to maximize the overall throughput in heterogeneous networks with optimal power allocation. We formulate the allocation problem as a "potential game". This type of games is characterized by a potential function, and has a specific property: a pure Nash equilibrium solution always exists and it is, moreover, either a local or a global optimizer of the potential function. We use a fully distributed algorithm that requires minimal coordination between base stations to reach a Nash equilibrium, and we prove that it is necessarily a global maximum. The performance of the algorithm is evaluated through simulations.