arXiv subjects

Mohammed Almekhlafi

Publications and source records attributed to Mohammed Almekhlafi.

4 recordsLinked to original sources

arXiv2026

Joint Satellite Power Consumption and Handover Optimization for LEO Constellations

In satellite constellation-based communication systems, continuous user coverage requires frequent handoffs due to the dynamic topology induced by the Low Earth Orbit (LEO) satellites. Each handoff between a satellite and ground users introduces additional signaling and power consumption, which can become a significant burden as the size of the constellation continues to increase. This work focuses on the optimization of the total transmission rate in a LEO-to-user system, by jointly considering the total transmitted power, user-satellite associations, and power consumption, the latter being handled through a penalty on handoff events. We consider a system where LEO satellites serve users located in remote areas with no terrestrial connectivity, and formulate the power allocation problem as a mixed-integer concave linear program (MICP) subject to power and association constraints. Our approach can be solved with off-the-shelf solvers and is benchmarked against a naive baseline where users associate to their closest visible satellite. Extensive Monte Carlo simulations demonstrate the effectiveness of the proposed method in controlling the handoff frequency while maintaining high user throughput. These performance gains highlight the effectiveness of our handover-aware optimization strategy, which ensures that user rates improve significantly, by about 40%, without incurring a disproportionate rise in the handoff frequency.

cs.IT↗

arXiv2026

Multi-Agent Reinforcement Learning for Joint Handover Management and Power Allocation in Multi-Orbit Satellite Networks

Future sixth-generation non-terrestrial networks are expected to combine low Earth orbit (LEO), medium Earth orbit (MEO), and geostationary Earth orbit (GEO) satellites, whose complementary layers must be coordinated through joint user association, power allocation, and handover management under fast LEO dynamics. This paper studies this problem by formulating it as a mixed-integer nonlinear program and decomposing it into a multi-agent reinforcement learning (MARL) policy that selects the associations and a convex power-allocation subproblem solved exactly at each time slot that defines the reward of the MARL part. The association policy is trained with multi-agent proximal policy optimization (MAPPO) and the targeted multi-agent communication (TarMAC) mechanism, and is made aware of the orbital layer through a state that encodes layer-dependent handover penalties. Evaluated on a realistic multi-constellation scenario built from real two-line element data over Nairobi, Kenya, the proposed policy reaches 92% of the throughput of a greedy signal-to-noise ratio (SNR) maximizing scheme while triggering more than four times fewer handovers, and improves throughput by roughly 14% over a conservative stay heuristic. Compared to an LEO-only learned policy of identical architecture, it attains slightly higher throughput with fewer handovers by offloading a fraction of the users to the MEO and GEO layers, an emergent multi-orbit behavior that drives its favorable throughput and handover trade-off.

eess.SP↗

arXiv2025

Connectivity-Aware Task Offloading for Remote Northern Regions: a Hybrid LEO-MEO Architecture

Arctic regions, such as northern Canada, face significant challenges in achieving consistent connectivity and low-latency computing services due to the sparse coverage of Low Earth Orbit (LEO) satellites. To enhance service reliability in remote areas, this paper proposes a hybrid satellite architecture for task offloading that combines Medium Earth Orbit (MEO) and LEO satellites. We develop an optimization framework to maximize task offloading admission rate while balancing the energy consumption and delay requirements. Accounting for satellite visibility and limited computing resources, our approach integrates dynamic path selection with frequency and computational resource allocation. Because the formulated problem is NP-hard, we reformulate it into a mixed-integer convex form using disjunctive constraints and convex relaxation techniques, enabling efficient use of off-the-shelf optimization solvers. Simulation results show that, compared to a standalone LEO network, the proposed hybrid LEO-MEO architecture improves the task admission rate by 15\% and reduces the average delay by 12\%. These findings highlight the architecture's potential to enhance connectivity and user experience in remote Arctic areas.

eess.SP↗

arXiv2023

After-Stroke Arm Paresis Detection using Kinematic Data

This paper presents an approach for detecting unilateral arm paralysis/weakness using kinematic data. Our method employs temporal convolution networks and recurrent neural networks, guided by knowledge distillation, where we use inertial measurement units attached to the body to capture kinematic information such as acceleration, rotation, and flexion of body joints during an action. This information is then analyzed to recognize body actions and patterns. Our proposed network achieves a high paretic detection accuracy of 97.99\%, with an action classification accuracy of 77.69\%, through knowledge sharing. Furthermore, by incorporating causal reasoning, we can gain additional insights into the patient's condition, such as their Fugl-Meyer assessment score or impairment level based on the machine learning result. Overall, our approach demonstrates the potential of using kinematic data and machine learning for detecting arm paralysis/weakness. The results suggest that our method could be a useful tool for clinicians and healthcare professionals working with patients with this condition.

cs.CV↗