arXiv subjects

Ahmed A. Al-habob

Publications and source records attributed to Ahmed A. Al-habob.

4 recordsLinked to original sources

arXiv2026

Resilient and Efficient Aerial Wireless Sensor Networks Using Pinching-Antenna Systems

This paper proposes a pinching antenna system to enhance the connectivity and efficiency in aerial wireless networks. The algebraic connectivity of the network's graph is maximized by optimizing the placement of the pinching antenna elements in the uplink and downlink waveguides. The number of deployed unmanned aerial vehicles (UAVs) is minimized while maintaining a minimum network connectivity indicator. A heuristic pinching antenna placement algorithm is designed to maximize the network connectivity. An iterative UAV deployment algorithm is also designed to find the minimum number of UAVs, their placement locations, and the pinching antenna placement. Simulation results show that the considered pinching antenna system improves network connectivity and efficiency.

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arXiv2026

Movable Antenna for Integrated Sensing and Communication in Air Sea Ground Networks

Integrated sensing and communication (ISAC) is a new paradigm for efficiently combining sensing and communication functionalities by leveraging shared hardware and radio resources. Despite its promise, ISAC yields conflicting beamforming goals and competition over the same resources. Movable antennas enable effective exploitation of spatial degrees of freedom through dynamic position/orientation control, thereby enhancing the performance of ISAC systems. This paper proposes a movable antenna framework for ISAC in air sea ground networks. A multi-objective optimization problem is formulated with the objectives of maximizing the communication rate of a set of aerial, sea, and ground devices and the sensing rate of a set of targets. The location and orientation of the antenna sub-arrays, as well as the transmit/receive beamforming, are optimized under practical constraints on the movable antennas' location and orientation. A solution is developed based on a $K$-means clustering approach to optimize the sub-arrays' orientation and a particle swarm optimization to place the sub-arrays in optimized locations. The transmit and receive beamforming are designed using a successive convex approximation and a generalized eigenvector method, respectively. Simulation results illustrate that the developed movable antenna framework improves the ISAC objective and provides a remarkable trade-off between the communication data rate and the targets' sensing rate when compared with the conventional stationary antenna array scenario.

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arXiv2025

Energy-Efficient Aerial Network Slicing for Computation Offloading, Data Gathering, and Content Delivery

This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants' requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, the user-UAV association, and the transmission power as well as the computing resources of each UAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

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arXiv2020

Mobile Edge Computing and Artificial Intelligence: A Mutually-Beneficial Relationship

This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. AI provides revolutionary solutions in nearly every important aspect of the MEC offloading process, such as resource management and scheduling. On the other hand, MEC servers are utilized to avail a distributed and parallelized learning framework, namely mobile edge learning.

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