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20172026
most citedElectrical Load Forecasting Using Edge Computing and Federated Learning

251 citations · 729 across the 54 of their papers we have counts for

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5 papers · 1 filter

eess.SP2026

RIS-Assisted Joint Resource Allocation for 6G FR3 IoT Networks

Muddasir Rahim, Irfan Azam, Soumaya Cherkaoui

In sixth-generation (6G) networks, the deployment of large numbers of Internet of Things (IoT) users (IU) necessitates efficient resource utilization and reliable connectivity, mak…

eess.SP2026

Reliable IoT Communications in 6G Non-Terrestrial Networks with Dual RIS

Muddasir Rahim, Soumaya Cherkaoui

The increasing demand for Internet of Things (IoT) applications has accelerated the need for robust resource allocation in sixth-generation (6G) networks. In this paper, we propose…

eess.SP2026

Dual-Tier IRS-Assisted Mid-Band 6G Mobile Networks: Robust Beamforming and User Association

Muddasir Rahim, Soumaya Cherkaoui

The rapid growth of Internet of Things (IoT) applications necessitates robust resource allocation in future sixth-generation (6G) networks, particularly at the upper mid-band (7-15…

eess.SP2025

Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks

Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular ne…

eess.SP2025

Performance Optimization of Energy-Harvesting Underlay Cognitive Radio Networks Using Reinforcement Learning

Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed…