arXiv subjects

Mohammad Shehab

Publications and source records attributed to Mohammad Shehab.

At least 19 recordsLinked to original sources

arXiv2026

Power from Space: Coordinated Satellite Charging for Off-Grid Wireless Systems

Satellite-enabled wireless power transfer (WPT) may be a transformative solution for charging Internet of Things (IoT) devices in off-grid scenarios where traditional technologies struggle to efficiently meet urgent energy demands. In this article, we review the advantages and limitations of microwave-based long-distance charging for satellite-enabled WPT. We then introduce our vision of coordinated space-based WPT, where multiple satellites jointly serve networks of ground devices. Potential use cases are presented highlighting application requirements. We evaluate the average received power at the target locations using two coordination schemes and perform a statistical characterization of the power spillover on undesired locations. We also shed light on the performance of inter-satellite laser WPT for different operating distances and transmit-receive apertures of the peer satellites. Moreover, we explore the integration of metasurfaces on satellite apertures and ground networks to boost energy conversion efficiency, scalability, and beam management. Finally, we outline relevant challenges and research directions towards implementing our vision.

eess.SP↗

arXiv2026

Multi-AUV Trajectory Learning for Sustainable Underwater IoT with Acoustic Energy Transfer

The Internet of Underwater Things (IoUT) supports ocean sensing and offshore monitoring but requires coordinated mobility and energy-aware communication to sustain long-term operation. This letter proposes a multi-AUV framework that jointly addresses trajectory control and acoustic communication for sustainable IoUT operation. The problem is formulated as a Markov decision process that integrates continuous AUV kinematics, propulsion-aware energy consumption, acoustic energy transfer feasibility, and Age of Information (AoI) regulation. A centralized deep reinforcement learning policy based on Proximal Policy Optimization (PPO) is developed to coordinate multiple AUVs under docking and safety constraints. The proposed approach is evaluated against structured heuristic baselines and demonstrates significant reductions in average AoI while improving fairness and data collection efficiency. Results show that cooperative multi-AUV control provides scalable performance gains as the network size increases.

eess.SY↗

arXiv2026

Wireless Energy Transfer from Space to Ground via Satellite Constellation Grids

This letter presents a framework for space-to-ground wireless energy transfer (WET) for wirelessly chargeable devices (WCD) located in remote areas or disaster situations. We consider a grid of multi-antenna satellites that charge a WCD within line-of-sight. Closed-form expressions for harvested energy are derived considering maximum ratio transmission (MRT) ensuring that the WCD meets its circuit charging threshold $P_{th}$. Simulations elucidate that milli-joule-level energy can be harvested during satellite grid visibility, with charging efficiency influenced by the number of satellites, their altitude, charging frequency, and grid inclination.

eess.SP↗

arXiv2026

AUV Trajectory Learning for Underwater Acoustic Energy Transfer and Age Minimization

Internet of underwater things (IoUT) is increasingly gathering attention with the aim of monitoring sea life and deep ocean environment, underwater surveillance as well as maintenance of underwater installments. However, conventional IoUT devices, reliant on battery power, face limitations in lifespan and pose environmental hazards upon disposal. This paper introduces a sustainable approach for simultaneous information uplink from the IoUT devices and acoustic energy transfer (AET) to the devices via an autonomous underwater vehicle (AUV), potentially enabling them to operate indefinitely. To tackle the time-sensitivity, we adopt age of information (AoI), and Jain's fairness index. We develop two deep-reinforcement learning (DRL) algorithms, offering a high-complexity, high-performance frequency division duplex (FDD) solution and a low-complexity, medium-performance time division duplex (TDD) approach. The results elucidate that the proposed FDD and TDD solutions significantly reduce the average AoI and boost the harvested energy as well as data collection fairness compared to baseline approaches.

cs.RO↗

arXiv2025

Age and Power Minimization via Meta-Deep Reinforcement Learning in UAV Networks

Age-of-information (AoI) and transmission power are crucial performance metrics in low energy wireless networks, where information freshness is of paramount importance. This study examines a power-limited internet of things (IoT) network supported by a flying unmanned aerial vehicle(UAV) that collects data. Our aim is to optimize the UAV flight trajectory and scheduling policy to minimize a varying AoI and transmission power combination. To tackle this variation, this paper proposes a meta-deep reinforcement learning (RL) approach that integrates deep Q-networks (DQNs) with model-agnostic meta-learning (MAML). DQNs determine optimal UAV decisions, while MAML enables scalability across varying objective functions. Numerical results indicate that the proposed algorithm converges faster and adapts to new objectives more effectively than traditional deep RL methods, achieving minimal AoI and transmission power overall.

cs.LG↗

arXiv2024

MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion

Accurate indoor localization remains challenging due to variations in wireless signal environments and limited data availability. This paper introduces MetaGraphLoc, a novel system leveraging sensor fusion, graph neural networks (GNNs), and meta-learning to overcome these limitations. MetaGraphLoc integrates received signal strength indicator measurements with inertial measurement unit data to enhance localization accuracy. Our proposed GNN architecture, featuring dynamic edge construction (DEC), captures the spatial relationships between access points and underlying data patterns. MetaGraphLoc employs a meta-learning framework to adapt the GNN model to new environments with minimal data collection, significantly reducing calibration efforts. Extensive evaluations demonstrate the effectiveness of MetaGraphLoc. Data fusion reduces localization error by 15.92%, underscoring its importance. The GNN with DEC outperforms traditional deep neural networks by up to 30.89%, considering accuracy. Furthermore, the meta-learning approach enables efficient adaptation to new environments, minimizing data collection requirements. These advancements position MetaGraphLoc as a promising solution for indoor localization, paving the way for improved navigation and location-based services in the ever-evolving Internet of Things networks.

eess.SP↗

arXiv2024

TinyML NLP Scheme for Semantic Wireless Sentiment Classification with Privacy Preservation

Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, often raise privacy concerns and demand significant on-device computational resources. Centralized learning (CL) on the edge provides an energy-efficient alternative but requires collecting raw data, compromising user privacy. While federated learning (FL) enhances privacy, it imposes high computational energy demands on resource-constrained devices. This study provides insights into deploying privacy-preserving, energy-efficient NLP models on edge devices. We introduce semantic split learning (SL) as an energy-efficient, privacy-preserving tiny machine learning (TinyML) framework and compare it to FL and CL in the presence of Rayleigh fading and additive noise. Our results show that SL significantly reduces computational power and CO2 emissions while enhancing privacy, as evidenced by a fourfold increase in reconstruction error compared to FL and nearly eighteen times that of CL. In contrast, FL offers a balanced trade-off between privacy and efficiency. Our code is available for replication at our GitHub repository: https://github.com/AhmedRadwan02/TinyEco2AI-NLP.

cs.LG↗

arXiv2024

Five Key Enablers for Communication during and after Disasters

Civilian communication during disasters such as earthquakes, floods, and military conflicts is crucial for saving lives. Nevertheless, several challenges exist during these circumstances such as the destruction of cellular communication and electricity infrastructure, lack of line of sight (LoS), and difficulty of localization under the rubble. In this article, we discuss key enablers that can boost communication during disasters, namely, satellite and aerial platforms, redundancy, silencing, and sustainable networks aided with wireless energy transfer (WET). The article also highlights how these solutions can be implemented in order to solve the failure of communication during disasters. Finally, it sheds light on unresolved challenges, as well as future research directions.

eess.SP↗

arXiv2024

Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification

Semantic and goal-oriented (SGO) communication is an emerging technology that only transmits significant information for a given task. Semantic communication encounters many challenges, such as computational complexity at end users, availability of data, and privacy-preserving. This work presents a TinyML-based semantic communication framework for few-shot wireless image classification that integrates split-learning and meta-learning. We exploit split-learning to limit the computations performed by the end-users while ensuring privacy-preserving. In addition, meta-learning overcomes data availability concerns and speeds up training by utilizing similarly trained tasks. The proposed algorithm is tested using a data set of images of hand-written letters. In addition, we present an uncertainty analysis of the predictions using conformal prediction (CP) techniques. Simulation results show that the proposed Semantic-MSL outperforms conventional schemes by achieving 20 % gain on classification accuracy using fewer data points, yet less training energy consumption.

cs.CV↗

arXiv2024

Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm

Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines.

cs.LG↗

arXiv2024

A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles

Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in low SNR scenarios. This work presents a multi-task-oriented semantic communication framework for connected and autonomous vehicles (CAVs). We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs. These encoded images are then transmitted from one CAV to another CAV through satellite in challenging weather conditions where visibility is impaired. In addition, we propose task-oriented semantic decoders for image reconstruction and classification tasks. Simulation results show that the proposed framework outperforms the conventional schemes, such as QAM-16, regarding the reconstructed image's similarity and the classification's accuracy. In addition, it can save up to 89 % of the bandwidth by sending fewer bits.

cs.NI↗

arXiv2024

Conservative and Risk-Aware Offline Multi-Agent Reinforcement Learning

Reinforcement learning (RL) has been widely adopted for controlling and optimizing complex engineering systems such as next-generation wireless networks. An important challenge in adopting RL is the need for direct access to the physical environment. This limitation is particularly severe in multi-agent systems, for which conventional multi-agent reinforcement learning (MARL) requires a large number of coordinated online interactions with the environment during training. When only offline data is available, a direct application of online MARL schemes would generally fail due to the epistemic uncertainty entailed by the lack of exploration during training. In this work, we propose an offline MARL scheme that integrates distributional RL and conservative Q-learning to address the environment's inherent aleatoric uncertainty and the epistemic uncertainty arising from the use of offline data. We explore both independent and joint learning strategies. The proposed MARL scheme, referred to as multi-agent conservative quantile regression, addresses general risk-sensitive design criteria and is applied to the trajectory planning problem in drone networks, showcasing its advantages.

cs.LG↗

arXiv2024

Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models

The age of information (AoI) is used to measure the freshness of the data. In IoT networks, the traditional resource management schemes rely on a message exchange between the devices and the base station (BS) before communication which causes high AoI, high energy consumption, and low reliability. Unmanned aerial vehicles (UAVs) as flying BSs have many advantages in minimizing the AoI, energy-saving, and throughput improvement. In this paper, we present a novel learning-based framework that estimates the traffic arrival of IoT devices based on Markovian events. The learning proceeds to optimize the trajectory of multiple UAVs and their scheduling policy. First, the BS predicts the future traffic of the devices. We compare two traffic predictors: the forward algorithm (FA) and the long short-term memory (LSTM). Afterward, we propose a deep reinforcement learning (DRL) approach to optimize the optimal policy of each UAV. Finally, we manipulate the optimum reward function for the proposed DRL approach. Simulation results show that the proposed algorithm outperforms the random-walk (RW) baseline model regarding the AoI, scheduling accuracy, and transmission power.

cs.LG↗

arXiv2023

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communications -- A Tutorial

Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need to identify and accurately characterize the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Specifically, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, and tail approximations, iv) rare events simulation, vi) queuing theory and information freshness, and v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-field games. Moreover, we often refer to prominent data-driven algorithms within the scope of the discussed tools/methodologies. Throughout the paper, we briefly review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

cs.NI↗

arXiv2023

Age Minimization in Massive IoT via UAV Swarm: A Multi-agent Reinforcement Learning Approach

In many massive IoT communication scenarios, the IoT devices require coverage from dynamic units that can move close to the IoT devices and reduce the uplink energy consumption. A robust solution is to deploy a large number of UAVs (UAV swarm) to provide coverage and a better line of sight (LoS) for the IoT network. However, the study of these massive IoT scenarios with a massive number of serving units leads to high dimensional problems with high complexity. In this paper, we apply multi-agent deep reinforcement learning to address the high-dimensional problem that results from deploying a swarm of UAVs to collect fresh information from IoT devices. The target is to minimize the overall age of information in the IoT network. The results reveal that both cooperative and partially cooperative multi-agent deep reinforcement learning approaches are able to outperform the high-complexity centralized deep reinforcement learning approach, which stands helpless in large-scale networks.

cs.LG↗

arXiv2023

Meta-Learning Based Few Pilots Demodulation and Interference Cancellation For NOMA Uplink

Non-Orthogonal Multiple Access (NOMA) is at the heart of a paradigm shift towards non-orthogonal communication due to its potential to scale well in massive deployments. Nevertheless, the overhead of channel estimation remains a key challenge in such scenarios. This paper introduces a data-driven, meta-learning-aided NOMA uplink model that minimizes the channel estimation overhead and does not require perfect channel knowledge. Unlike conventional deep learning successive interference cancellation (SICNet), Meta-Learning aided SIC (meta-SICNet) is able to share experience across different devices, facilitating learning for new incoming devices while reducing training overhead. Our results confirm that meta-SICNet outperforms classical SIC and conventional SICNet as it can achieve a lower symbol error rate with fewer pilots.

cs.IT↗

arXiv2023

Multi-UAV Path Learning for Age and Power Optimization in IoT with UAV Battery Recharge

In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages and the devices' energy consumption. The solution accounts for the UAVs' battery lifetime and flight time to recharging depots to ensure the UAVs' green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).

eess.SP↗

arXiv2022

A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme.

cs.AI↗