16 citations · 61 across the 30 of their papers we have counts for
31 papers
An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devices, lack of labeled data, and domain variability across environments.…
Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB
Alireza Mohammadhosseini, Fatemeh Afghah
Unmanned aerial vehicle (UAV)-mounted 5G New Radio base stations (gNBs) can augment terrestrial networks with an on-demand, repositionable Frequency Range 2 (FR2) capacity layer. T…
ORAN-DEFEND: Subspace Detection and Sanitization of Backdoor DRL xApps in Open RAN
Md Raihan Uddin, Fatemeh Lotfi, Tolunay Seyfi +1
Open Radio Access Networks (O-RAN) increasingly delegate near-real-time control to deep reinforcement learning (DRL) xApps obtained from third-party vendors, creating a new supply-…
Six Times to Spare: Characterizing GPU-Accelerated 5G LDPC Decoding for Edge-RSU Communications
Ryan Barker, Julia Boone, Tolunay Seyfi +3
Ultra-reliable low-latency vehicular communications (URLLC) require sufficient physical-layer (PHY) compute headroom at the network edge, where roadside units (RSUs) and compact ne…
Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN
Fatemeh Lotfi, Fatemeh Afghah
The increasing complexity of modern applications demands wireless networks capable of real time adaptability and efficient resource management. The Open Radio Access Network (O-RAN…
Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Next-generation networks utilize the Open Radio Access Network (O-RAN) architecture to enable dynamic resource management, facilitated by the RAN Intelligent Controller (RIC). Whil…
Adapt under Attack and Domain Shift: Unified Adversarial Meta-Learning and Domain Adaptation for Robust Automatic Modulation Classification
Ali Owfi, Amirmohammad Bamdad, Tolunay Seyfi +1
Deep learning has emerged as a leading approach for Automatic Modulation Classification (AMC), demonstrating superior performance over traditional methods. However, vulnerability t…
DORA: Dynamic O-RAN Resource Allocation for Multi-Slice 5G Networks
Alireza Ebrahimi Dorcheh, Tolunay Seyfi, Fatemeh Afghah
The fifth generation (5G) of wireless networks must simultaneously support heterogeneous service categories, including Ultra-Reliable Low-Latency Communications (URLLC), enhanced M…
ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Advanced wireless networks must support highly dynamic and heterogeneous service demands. Open Radio Access Network (O-RAN) architecture enables this flexibility by adopting modula…
Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing
Fatemeh Lotfi, Hossein Rajoli, Fatemeh Afghah
Modern wireless networks must adapt to dynamic conditions while efficiently managing diverse service demands. Traditional deep reinforcement learning (DRL) struggles in these envir…
REAL: Reinforcement Learning-Enabled xApps for Experimental Closed-Loop Optimization in O-RAN with OSC RIC and srsRAN
Ryan Barker, Alireza Ebrahimi Dorcheh, Tolunay Seyfi +1
Open Radio Access Network (O-RAN) offers an open, programmable architecture for next-generation wireless networks, enabling advanced control through AI-based applications on the ne…
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detec…
Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks
Alireza Ebrahimi, Fatemeh Afghah
5G technology enhances industries with high-speed, reliable, low-latency communication, revolutionizing mobile broadband and supporting massive IoT connectivity. With the increasin…
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification
Amirmohammad Bamdad, Ali Owfi, Fatemeh Afghah
DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications.…
Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
Fatemeh Lotfi, Fatemeh Afghah
As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes…
SkyGrid: Energy-Flow Optimization at Harmonized Aerial Intersections
Sahand Khoshdel, Fatemeh Afghah, Qi Luo
The rapid evolution of urban air mobility (UAM) is reshaping the future of transportation by integrating aerial vehicles into urban transit systems. The design of aerial intersecti…
PyroTrack: Belief-Based Deep Reinforcement Learning Path Planning for Aerial Wildfire Monitoring in Partially Observable Environments
Sahand Khoshdel, Qi Luo, Fatemeh Afghah
Motivated by agility, 3D mobility, and low-risk operation compared to human-operated management systems of autonomous unmanned aerial vehicles (UAVs), this work studies UAV-based a…
Deciphering Heartbeat Signatures: A Vision Transformer Approach to Explainable Atrial Fibrillation Detection from ECG Signals
Aruna Mohan, Danne Elbers, Or Zilbershot +2
Remote patient monitoring based on wearable single-lead electrocardiogram (ECG) devices has significant potential for enabling the early detection of heart disease, especially in c…
Thermal Image Calibration and Correction using Unpaired Cycle-Consistent Adversarial Networks
Hossein Rajoli, Pouya Afshin, Fatemeh Afghah
Unmanned aerial vehicles (UAVs) offer a flexible and cost-effective solution for wildfire monitoring. However, their widespread deployment during wildfires has been hindered by a l…
Hardware Acceleration for Real-Time Wildfire Detection Onboard Drone Networks
Austin Briley, Fatemeh Afghah
Early wildfire detection in remote and forest areas is crucial for minimizing devastation and preserving ecosystems. Autonomous drones offer agile access to remote, challenging ter…
Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning
Fatemeh Lotfi, Fatemeh Afghah
With emerging applications such as autonomous driving, smart cities, and smart factories, network slicing has become an essential component of 5G and beyond networks as a means of…
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Sahand Khoshdel +7
Wildfires have emerged as one of the most destructive natural disasters worldwide, causing catastrophic losses in both human lives and forest wildlife. Recently, the use of Artific…
Dynamic Online Modulation Recognition using Incremental Learning
Ali Owfi, Ali Abbasi, Fatemeh Afghah +2
Modulation recognition is a fundamental task in communication systems as the accurate identification of modulation schemes is essential for reliable signal processing, interference…
Attention-based Open RAN Slice Management using Deep Reinforcement Learning
Fatemeh Lotfi, Fatemeh Afghah, Jonathan Ashdown
As emerging networks such as Open Radio Access Networks (O-RAN) and 5G continue to grow, the demand for various services with different requirements is increasing. Network slicing…