arXiv subjects

Hajar El Hammouti

Publications and source records attributed to Hajar El Hammouti.

At least 19 recordsLinked to original sources

arXiv2026

Orientation-Aware Control and Trajectory Design for Aerial RIS-Assisted Wireless Communications

Aerial Reconfigurable Intelligent Surfaces (ARISs) can enhance wireless connectivity in obstructed environments by combining reconfigurable metasurfaces with aerial mobility. However, in practical deployments, the Reconfigurable Intelligent Surface (RIS) is rigidly mounted on a multi-rotor Unmanned Aerial Vehicle (UAV), which creates coupled position-orientation dynamics that directly affect communication quality. Since RIS performance is highly sensitive to orientation, point-mass or orientation-invariant models may lead to optimistic performance estimates. This paper proposes an orientation-aware ARIS framework that explicitly accounts for UAV dynamics, actuation limits, and orientation-dependent channels. The proposed approach combines a communications-guided reference trajectory planner with a Nonlinear Model Predictive Control for dynamically feasible trajectory tracking and favorable RIS orientation, while employing lightweight beamforming and RIS phase-shift updates. Simulations show that accounting for orientation improves user throughput by 13.77%, while the proposed joint communication updates improve network throughput by 28% over phase-shift-only optimization and by at least a factor of 2.5 over beamforming-only optimization. These results highlight the importance of orientation-aware modeling and joint communication-control design in ARIS-assisted wireless systems.

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arXiv2026

3D Euler-Angle Orientation Control for Two-Ray Fading Mitigation in Maritime Air-to-Sea Communications

Maritime Air-to-Sea links are dominated by a line-of-sight ray and a sea-surface reflected ray whose destructive combination produces deep fades. Existing mitigation strategies optimize Unmanned Aerial Vehicle position or trajectory but leave attitude unexploited. This paper treats the full three dimensional attitude as a physical-layer control variable that shapes the two-ray interference through antenna phase-center displacement. Under small-angle and far-field assumptions, the constructive-interference condition reduces to an affine constraint in the Euler angles and admits a closed-form family of minimum-norm attitude candidates. A differentiable soft-minimum rule yields a smooth reference tracked by a constrained Nonlinear Model Predictive Control controller on a fully-actuated tilting multirotor. The proposed scheme increases cumulative throughput by 11.4% over a pitch-only benchmark and 22.2% over a zero-orientation baseline, while preserving trajectory tracking and respecting actuator limits.

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arXiv2026

Protein Representation Learning with Secondary-Structure and Energy-Filtered Hydrogen-Bond Graphs

Graph-based representations are widely used in protein modeling, yet many existing approaches rely primarily on sequence adjacency or geometric proximity, which only partially reflect the principles governing protein folding. Proteins instead adopt complex three-dimensional conformations organized around secondary structure elements, such as $α$-helices and $β$-sheets, which encode recurring local motifs and stabilizing hydrogen-bond interactions. In this work, we introduce a secondary-structure-aware graph neural network for protein representation learning. Residue-level node representations are augmented with secondary structure assignments, and graph edges are constructed from hydrogen-bond interactions filtered by their energetic strength. This design enables the model to capture both local structural context and long-range couplings that are central to protein stability and function. We evaluate the proposed approach on commonly used protein benchmarks and observe consistent improvements over existing graph-based methods. In addition, the resulting graph representations offer enhanced biological interpretability, as the learned connectivity aligns with established structural motifs. These findings suggest that incorporating secondary structure and energy-filtered hydrogen-bond topology provides an effective inductive bias for protein representation learning. The code is released at https://github.com/mohamedmohamed2021/SSProNet

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arXiv2026

Semantic-Aware UAV Command and Control for Efficient IoT Data Collection

Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler technology for data collection from Internet of Things (IoT) devices. However, effective data collection is challenged by resource constraints and the need for real-time decision-making. In this work, we propose a novel framework that integrates semantic communication with UAV command-and-control (C&C) to enable efficient image data collection from IoT devices. Each device uses Deep Joint Source-Channel Coding (DeepJSCC) to generate a compact semantic latent representation of its image to enable image reconstruction even under partial transmission. A base station (BS) controls the UAV's trajectory by transmitting acceleration commands. The objective is to maximize the average quality of reconstructed images by maintaining proximity to each device for a sufficient duration within a fixed time horizon. To address the challenging trade-off and account for delayed C&C signals, we model the problem as a Markov Decision Process and propose a Double Deep Q-Learning (DDQN)-based adaptive flight policy. Simulation results show that our approach outperforms baseline methods such as greedy and traveling salesman algorithms, in both device coverage and semantic reconstruction quality.

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arXiv2025

Just Few States are Enough: Randomized Sparse Feedback for Stability of Dynamical Systems

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 $0.3\%$ of the state coordinates.

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arXiv2025

Free-Space Optical Communication-Driven NMPC Framework for Multi-Rotor Aerial Vehicles in Structured Inspection Scenarios

This paper introduces a Nonlinear Model Predictive Control (NMPC) framework for communication-aware motion planning of Multi-Rotor Aerial Vehicles (MRAVs) using Free-Space Optical (FSO) links. The scenario involves MRAVs equipped with body-fixed optical transmitters and Unmanned Ground Vehicles (UGVs) acting as mobile relays, each outfitted with fixed conical Field-of-View (FoV) receivers. The controller integrates optical connectivity constraints into the NMPC formulation to ensure beam alignment and minimum link quality, while also enabling UGV tracking and obstacle avoidance. The method supports both coplanar and tilted MRAV configurations. MATLAB simulations demonstrate its feasibility and effectiveness.

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arXiv2025

Robust Planning and Control of Omnidirectional MRAVs for Aerial Communications in Wireless Networks

A new class of Multi-Rotor Aerial Vehicles (MRAVs), known as omnidirectional MRAVs (o-MRAVs), has gained attention for their ability to independently control 3D position and orientation. This capability enhances robust planning and control in aerial communication networks, enabling more adaptive trajectory planning and precise antenna alignment without additional mechanical components. These features are particularly valuable in uncertain environments, where disturbances such as wind and interference affect communication stability. This paper examines o-MRAVs in the context of robust aerial network planning, comparing them with the more common under-actuated MRAVs (u-MRAVs). Key applications, including physical layer security, optical communications, and network densification, are highlighted, demonstrating the potential of o-MRAVs to improve reliability and efficiency in dynamic communication scenarios.

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arXiv2025

Harnessing the Potential of Omnidirectional UAVs in RIS-Enabled Wireless Networks

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.

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arXiv2024

Reshaping UAV-Enabled Communications with Omnidirectional Multi-Rotor Aerial Vehicles

A new class of Multi-Rotor Aerial Vehicles (MRAVs), known as omnidirectional MRAVs (o-MRAVs), has attracted significant interest in the robotics community. These MRAVs have the unique capability of independently controlling their 3D position and 3D orientation. In the context of aerial communication networks, this translates into the ability to control the position and orientation of the antenna mounted on the MRAV without any additional devices tasked for antenna orientation. This additional Degrees of Freedom (DoF) adds a new dimension to aerial communication systems, creating various research opportunities in communications-aware trajectory planning and positioning. This paper presents this new class of MRAVs and discusses use cases in areas such as physical layer security and optical communications. Furthermore, the benefits of these MRAVs are illustrated with realistic simulation scenarios. Finally, new research problems and opportunities introduced by this advanced robotics technology are discussed.

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arXiv2024

Harnessing the Potential of Omnidirectional Multi-Rotor Aerial Vehicles in Cooperative Jamming Against Eavesdropping

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.

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arXiv2024

Energy Efficient Aerial RIS: Phase Shift Optimization and Trajectory Design

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.

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arXiv2024

Multi-sided Matching for the Association of Space-Air-Ground Integrated Systems

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, multi-layered, 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.

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arXiv2023

Age-of-Information in UAV-assisted Networks: a Decentralized Multi-Agent Optimization

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.

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arXiv2023

Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks

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 $29.5\%$.

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arXiv2023

Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks

Unmanned aerial vehicles (UAVs) are seen as a promising technology to perform a wide range of tasks in wireless communication networks. In this work, we consider the deployment of a group of UAVs to collect the data generated by IoT devices. Specifically, we focus on the case where the collected data is time-sensitive, and it is critical to maintain its timeliness. Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized. To this end, we formulate the studied problem as a mixed-integer nonlinear programming (MINLP) under time and quality of service constraints. To efficiently solve the resulting optimization problem, we investigate the cooperative Multi-Agent Reinforcement Learning (MARL) framework and propose an RL approach based on the popular on-policy Reinforcement Learning (RL) algorithm: Policy Proximal Optimization (PPO). Our approach leverages the centralized training decentralized execution (CTDE) framework where the UAVs learn their optimal policies while training a centralized value function. Our simulation results show that the proposed MAPPO approach reduces the global AoU by at least a factor of 1/2 compared to conventional off-policy reinforcement learning approaches.

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arXiv2022

Age-of-Updates Optimization for UAV-assisted Networks

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.

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arXiv2021

Client Selection in Federated Learning based on Gradients Importance

Federated learning (FL) enables multiple devices to collaboratively learn a global model without sharing their personal data. In real-world applications, the different parties are likely to have heterogeneous data distribution and limited communication bandwidth. In this paper, we are interested in improving the communication efficiency of FL systems. We investigate and design a device selection strategy based on the importance of the gradient norms. In particular, our approach consists of selecting devices with the highest norms of gradient values at each communication round. We study the convergence and the performance of such a selection technique and compare it to existing ones. We perform several experiments with non-iid set-up. The results show the convergence of our method with a considerable increase of test accuracy comparing to the random selection.

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arXiv2020

The Optimal and the Greedy: Drone Association and Positioning Schemes for Internet of UAVs

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 games 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 user-UAV association and 3D-location problem. Next, we exploit the sub-modular 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 guaranteed performance 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 number of iterations.

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