5 citations · 7 across the 11 of their papers we have counts for
11 papers
Quantum-Aided Active Device Detection in Energy-Harvesting Symbiotic Radio Networks
Remon Polus, Deemah Tashman, Soumaya Cherkaoui
Massive connectivity in next-generation networks demands energy- and spectrum-efficient solutions for large-scale Internet of Things (IoT) deployments. Symbiotic radio (SR) enables…
Grover-Based PLS: AUD and Beamforming with Artificial Noise in CD-NOMA
Deemah H. Tashman, Soumaya Cherkaoui
Sixth-Generation (6G) networks will require massive connectivity, ultra-low latency, and robust security, making reliable Active User Detection (AUD) essential for interference con…
Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach
Taym Alshoghri, Deemah H. Tashman, Mohammad Reza Gerami +1
Internet of Medical Things (IoMT) devices operate under strict resource constraints while handling highly sensitive health data, making security and privacy critical concerns. Fede…
When Critics Disagree: Adaptive Reward Poisoning Attacks in RIS-Aided Wireless Control System
Deemah H. Tashman, Soumaya Cherkaoui
Reward-poisoning attacks present a significant risk to learning-based wireless control systems. Given this, we propose a Disagreement-Guided Reward Poisoning (DGRP) adaptive attack…
Adversarial Attacks in AI-Driven RAN Slicing: SLA Violations and Recovery
Deemah H. Tashman, Soumaya Cherkaoui
Next-generation (NextG) cellular networks are designed to support emerging applications with diverse data rate and latency requirements, such as immersive multimedia services and l…
ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities
Mira Chandra Kirana, Patatchona Keyela, Fatemeh Rostamian +2
As wireless communication systems become more advanced, Open Radio Access Networks (O-RAN) stand out as a notable framework that promotes interoperability and cost-effectiveness. A…
Trustworthy AI-Driven Dynamic Hybrid RIS: Joint Optimization and Reward Poisoning-Resilient Control in Cognitive MISO Networks
Deemah H. Tashman, Soumaya Cherkaoui
Cognitive radio networks (CRNs) are a key mechanism for alleviating spectrum scarcity by enabling secondary users (SUs) to opportunistically access licensed frequency bands without…
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…
Maximizing Reliability in Overlay Radio Networks with Time Switching and Power Splitting Energy Harvesting
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
Cognitive radio networks (CRNs) are acknowledged for their ability to tackle the issue of spectrum under-utilization. In the realm of CRNs, this paper investigates the energy effic…
Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
This paper presents a reinforcement learning (RL) based approach to improve the physical layer security (PLS) of an underlay cognitive radio network (CRN) over cascaded channels. T…
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…