Said Muhammad | Searcharxiv

arXiv2025

THEAS: Efficient Power Management in Multi-Core CPUs via Cache-Aware Resource Scheduling

The dynamic adaptation of resource levels enables the system to enhance energy efficiency while maintaining the necessary computational resources, particularly in scenarios where workloads fluctuate significantly over time. The proposed approach can play a crucial role in heterogeneous systems where workload characteristics are not uniformly distributed, such as non-pinning tasks. The deployed THEAS algorithm in this research work ensures a balance between performance and power consumption, making it suitable for a wide range of real-time applications. A comparative analysis of the proposed THEAS algorithm with well-known scheduling techniques such as Completely Fair Scheduler (CFS), Energy-Aware Scheduling (EAS), Heterogeneous Scheduling (HeteroSched), and Utility-Based Scheduling is presented in Table III. Each scheme is compared based on adaptability, core selection criteria, performance scaling, cache awareness, overhead, and real-time suitability.

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arXiv2025

Distributed Deep Learning with RIS Grouping for Accurate Cascaded Channel Estimation

Reconfigurable Intelligent Surface (RIS) panels are envisioned as a key technology for sixth-generation (6G) wireless networks, providing a cost-effective means to enhance coverage and spectral efficiency. A critical challenge is the estimation of the cascaded base station (BS)-RIS-user channel, since the passive nature of RIS elements prevents direct channel acquisition, incurring prohibitive pilot overhead, computational complexity, and energy consumption. To address this, we propose a deep learning (DL)-based channel estimation framework that reduces pilot overhead by grouping RIS elements and reconstructing the cascaded channel from partial pilot observations. Furthermore, conventional DL models trained under single-user settings suffer from poor generalization across new user locations and propagation scenarios. We develop a distributed machine learning (DML) strategy in which the BS and users collaboratively train a shared neural network using diverse channel datasets collected across the network, thereby achieving robust generalization. Building on this foundation, we design a hierarchical DML neural architecture that first classifies propagation conditions and then employs scenario-specific feature extraction to further improve estimation accuracy. Simulation results confirm that the proposed framework substantially reduces pilot overhead and complexity while outperforming conventional methods and single-user models in channel estimation accuracy. These results demonstrate the practicality and effectiveness of the proposed approach for 6G RIS-assisted systems.

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