Search Results for author:
Found 16 papers, 1 papers with code
Positioning Error Impact Compensation through Data-Driven Optimization in User-Centric Networks
The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network.
An AI-enabled Bias-Free Respiratory Disease Diagnosis Model using Cough Audio: A Case Study for COVID-19
The merit of RBFNet is demonstrated by comparing classification performance with State of The Art (SoTA) Deep Learning (DL) model (CNN LSTM) after training on different unbalanced COVID-19 data sets, created by using a large scale proprietary cough data set.
Towards using Cough for Respiratory Disease Diagnosis by leveraging Artificial Intelligence: A Survey
Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices.
An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks
Thus, the defense mechanism can provide the resilience and robustness for zero touch automation SON engines against the adversarial MDT attacks
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework
The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation.
See as a Bee: UV Sensor for Aerial Strawberry Crop Monitoring
Precision agriculture aims to use technological tools for the agro-food sector to increase productivity, cut labor costs, and reduce the use of resources.
Towards a Hybrid RF/Optical Lunar Communication System (LunarComm)
The prospect of mankind returning to the Moon has garnered a great amount of attention in recent years.
Machine Learning Aided Holistic Handover Optimization for Emerging Networks
An XGBoost based model has the best performance for edge RSRP and HOSR while random forest outperforms others for load prediction.
Interpretable AI-based Large-scale 3D Pathloss Prediction Model for enabling Emerging Self-Driving Networks
In modern wireless communication systems, radio propagation modeling to estimate pathloss has always been a fundamental task in system design and optimization.
Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions
The exponential rise in mobile traffic originating from mobile devices highlights the need for making mobility management in future networks even more efficient and seamless than ever before.
Towards Designing Systems with Large Number of Antennas for Range Extension in Ground-to-Air Communications
Providing broadband connectivity to airborne systems using ground based cellular networks is a promising solution as it offers several advantages over satellite-based solutions.
On the Trade-offs between Coverage Radius, Altitude and Beamwidth for Practical UAV Deployments
However, they analyze UAV coverage radius and altitude interplay while omitting or over-simplifying an important aspect of UAV deployment, i. e., effect of a realistic antenna pattern.
A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters
Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users.
Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks
In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input.
AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App
Building on the prior work on cough-based diagnosis of respiratory diseases, we propose, develop and test an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app.
Can Machine Learning Be Used to Recognize and Diagnose Coughs?
One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections.
