About Me: Rahul Gulia, Ph.D. Researcher in AI-Driven Wireless Networks

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I am a Ph.D. candidate in Electrical and Computer Engineering at the Rochester Institute of Technology (RIT), specializing in AI-driven optimization of wireless networks. My research bridges machine learning and communication systems, with a focus on 5G, mmWave (60 GHz WiGig), and UAV networks for industrial applications.

My work develops scalable, data-efficient solutions to accelerate wireless simulations, enabling real-time network planning. A key contribution is the WISVA framework, which reduces SINR heatmap prediction times from 37.9 hours to 21 minutes using variational autoencoders (VAEs), while maintaining >95% accuracy.

My research passion extends across the entire spectrum of wireless networking, from the fundamental physical layer to the intricate architecture of 5G core networks. I'm particularly fascinated by the potential of MIMO technologies to multiply capacity and reliability, the critical role of the Physical Layer in shaping transmission characteristics, and the intelligent coordination enabled by the MAC Layer, including protocols like CSMA/CA.

The evolution towards 5G Core architecture and its flexibility in supporting diverse services also fuels my curiosity. Understanding and contributing to IEEE Standards is paramount in ensuring interoperability and driving innovation within the wireless ecosystem.

At the heart of my work lies the transformative power of Artificial Intelligence and Machine Learning. I actively explore and develop advanced models such as Variational Autoencoders (VAEs) and LSTM models, investigating their unique capabilities in analyzing and optimizing wireless systems. Furthermore, I am deeply interested in creating cross-optimization models that leverage the strengths of different AI/ML techniques to achieve unprecedented levels of network performance.

Research Highlights

1. AI-Optimized Wireless Infrastructure

  • WISVA Framework: Combines VAEs and ns-3 simulations to model 5G/WiGig performance in smart warehouses.
  • Impact: Enables dynamic resource allocation for Industry 4.0 logistics.

2. 60 GHz mmWave Channel Modeling

  • Developed modified ns-3 modules to analyze material penetration and scattering losses in metal-rich warehouse environments.
  • Results published in Information (2023).

3. UAV Air-Ground Channel Modeling

  • Proposed ULAAG, a stochastic path loss model for indoor drone communications.

Publications

  • Rahul Gulia, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, and Michael E. Kuhl. Evaluation of 60 ghz wireless connectivity for an automated warehouse suitable for industry 4.0. Information, 14(9), 2023.
  • Rahul Gulia, Abhishek Vashist, Amlan Ganguly, Clark Hochgraf, and Michael E. Kuhl. Evaluation of 60 ghz wireless connectivity for an automated warehouse suitable for industry 4.0. Information, 14(9), 2023.
  • R. Gulia, A. Ganguly, A. Kwasinski, M. E. Kuhl, E. Rashedi and C. Hochgraf, "Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders," 2024 International Symposium on Networks, Computers and Communications (ISNCC), Washington DC, DC, USA, 2024, pp. 1-6, doi: 10.1109/ISNCC62547.2024.10759068.
  • Rahul Gulia, Amlan Ganguly, Clark Hochgraf, Andres Kwasinski, Michael E Kuhl and Ehsan Rashedi. [Forthcoming]. AI-based VAE model for Automated Warehouse 5G Infrastructure Modeling. In ACM Transactions on Internet of Things.


Academic Profiles

Contact

For collaborations or inquiries, please reach out via:
📧 Email: rg9828@rit.edu
🌐 Blog: learnwithpatience-wc.blogspot.com

 

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