Ammar Majeed
Papers
1
Total Citations
4
H-Index
1
About
Ammar Majeed is a rising researcher in wireless communications and indoor positioning, with a focus on ultra-wideband (UWB) technology and machine learning. His key contributions lie in improving the accuracy and reliability of UWB-based localization systems, particularly through the development of advanced signal processing and deep learning techniques. His most-cited work, "Accurate Multiclass NLOS Channels Identification in UWB Indoor Positioning System-Based Deep Neural Network" (2024), addresses a critical challenge in indoor navigation: distinguishing between line-of-sight (LOS) and non-line-of-sight (NLOS) propagation channels. By employing a deep neural network for multiclass classification, Majeed's approach significantly enhances distance measurement precision in complex, dynamic environments—a breakthrough for applications in autonomous robotics, smart buildings, and asset tracking. With early citations already accumulating, his research demonstrates immediate relevance and impact in the field. Majeed's work exemplifies the integration of AI with wireless systems to solve real-world localization problems, marking him as a promising contributor to next-generation indoor positioning technologies.
Research Focus
Key Achievements
Top Papers
- 1