AI4Networks Research Center

I-Net: AI-Based Self Organizing Network Framework for 5G and Beyond

Title: I-Net: AI-Based Self Organizing Network Framework for 5G and Beyond.
Funding: This project is funded by the Qatar National Research Fund (QNRF) under Grant No. NPRP12-S 0311-190302.
Duration: Two years (January 5, 2020 - January 5, 2022)
Funding Amount: $ 395,360.00

The problem addressed by the project

To address the key challenges of 5G networks by investigating, designing, developing and evaluating a deep automation solution namely I-NET.

I-NET project sits at the confluence of two transformational technologies i.e., 5G and machine learning or artificial intelligence.

Project Goals

The overarching goal of I-NET is to build an Artificial Intelligence (AI) based Self Organizing Network (SON) framework for autonomous operation and optimization of 5G and beyond cellular networks.

This project will help automate the post-deployment network operation and optimization for reducing costs, handling complexity and maximizing resources efficiency to enhance the technical and commercial viability of future Mobile Cellular Network’s (MCN).

Active Members

Lead PI: Dr. Adnan Abu-Dayya (Qatar University)

PI: Prof. Fethi Filali (Qatar University)

PI: Dr. Ali Imran (University of Oklahoma)

Research Scientist: Dr. Ali Rizwan (QMIC Qatar)

GRA: Marvin Manalastas (Ph.D. student)

GRA: Haneya Naeem Quereshi (Ph.D. student)

GRA: Muhammad Sajid Riaz (Ph.D. student)

Industry Collaborators

3rd party SON vendor: AISON, LLC, USA

Integration of project outcomes into teaching

The project outcomes are being adapted into following course being taught by PI.

"Emerging Topics in 5G and Beyond" Offered in Sring 2018, 2019 and 2020.

Tutorials

Ali Imran, Moving Towards Zero-Touch Automation, A Key Enabler for 6G: Addressing the Training Data Sparsity/Scarcity Challenge a half day tutorial at IEEE BlackSeaCom 2020, May 26-29, 2020.
Ali Imran, Muhammad Ali Imran, Moving Towards Zero-Touch Automation, A Key Enabler for 6G: The Challenges & Opportunities a half day tutorial at IEEE Wireless Communications and Networking Conference, May 25-28, 2020.

Keynotes/Invited Talks

Ali Imran "Leveraging AI for Zero-Touch Automation in 6G: How to Address the Training Data Sparsity/Scarcity Challenge?" keynote at IEEE 2nd International workshop on Data Driven Intelligence for Networks and Systems workshop at Infocom 2020, July 6-9, Toronto, Canada.

International Collaboration Opportunities

Project student has interned in the following company:

New T-Mobile (Formerly known as Sprint), CT, USA in Spring, 2020.

Journal Articles
1. H. N. Qureshi, U. Masood, M. Manalastas, A. Zaidi, H. Farooq, J. Forgeat, M. Bouton, S. Bothe, P. Karlsson, A. Rizwan, and A. Imran, "Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework," IEEE Communications Surveys and Tutorials [Accepted], March 2023.
2. A. Zaidi, H. Farooq, A. Rizwan, A. Abu-Dayya, and A. Imran, "A Framework to Address Mobility Management Challenges in Emerging Networks," IEEE Wireless Communications, November 2022, doi: 10.1109/MWC.015.2100666.
3. S. K. Kasi, U. S. Hashmi, S. Ekin, A. Abu-Dayya and A. Imran, "D-RAN: A DRL-based Demand-Driven Elastic User-Centric RAN Optimization for 6G & Beyond," IEEE Transactions on Cognitive Communications and Networking, October 2022, doi: 10.1109/TCCN.2022.3217785.
4. S. M. A. Zaidi, M. Manalastas, M. U. B. Farooq, H. Qureshi, A. Abu-Dayya, and A. Imran, "A Data Driven Framework for QoE-Aware Intelligent EN-DC Activation," IEEE Transactions on Vehicular Technology, pp. 1-14, October 2022, doi: 10.1109/TVT.2022.3211741.
5. M. S. Riaz, H. N. Qureshi, U. Masood, A. Rizwan, A. Abu-Dayya and A. Imran, "A Hybrid Deep Learning-Based (HYDRA) Framework for Multifault Diagnosis Using Sparse MDT Reports," IEEE Access, vol. 10, pp. 67140-67151, June 2022, doi: 10.1109/ACCESS.2022.3185639.
6. M. Manalastas, M. U. B. Farooq, S. M. A. Zaidi, A. Abu-Dayya, and A. Imran, "A Data Driven Framework for Inter-Frequency Handover Failure Prediction and Mitigation," IEEE Transactions on Vehicular Technology, pp. 1-1, March 2022, doi: 10.1109/TVT.2022.3157802.
7. A. Rizwan, M. Jaber, F. Filali, A. Imran and A. Abu-Dayya, "A Zero-Touch Network Service Management Approach Using AI-Enabled CDR Analysis," IEEE Access, vol. 9, pp. 157699 - 157714, November 2021 , doi: 10.1109/ACCESS.2021.3129281.
8. H. N. Qureshi, A. Imran and A. Abu-Dayya, "Enhanced MDT-based Performance Estimation for AI Driven Optimization in Future Cellular Networks," IEEE Access, September 2020, doi: 10.1109/ACCESS.2020.3021030.
9. S. M. A. Zaidi, M. Manalastas, H. Farooq, and A. Imran, "Mobility Management in Emerging Ultra-Dense Cellular Networks: A Survey, Outlook, and Future Research Directions," IEEE Access, vol. 8, pp. 183505-183533, August 2020, doi: 10.1109/ACCESS.2020.3027258.
10. U. S. Hashmi, S. A. R. Zaidi, A. Imran, and A. Abu-Dayya, "Enhancing Downlink QoS and Energy Efficiency through a User-Centric Stienen Cell Architecture for mmWave Networks," IEEE Transactions on Green Communications and Networking, vol. 4, no. 2, pp. 387-403, June 2020, doi: 10.1109/TGCN.2020.2967888.
11. H. Farooq, A. Asghar, and A. Imran, "Mobility Prediction Based Proactive Dynamic Network Orchestration for Load Balancing With QoS Constraint (OPERA)," IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, vol. 69, no. 3, pp. 3370-3383, March 2020, doi: 10.1109/TVT.2020.2966725.
12. O. Onireti, L. Zhang, A. Imran, and M. A. Imran , "Outage Probability in the Uplink of Multitier Millimeter Wave Cellular Networks," IEEE SYSTEMS JOURNAL, January 2020, doi: 10.1109/JSYST.2020.2965001.
Peer Reviewed Conference Papers
1. M.U.B. Farooq, M. Manalastas, S.M.A. Zaidi, A. Abu-Dayya, and A. Imran, "Machine Learning Aided Holistic Handover Optimization for Emerging Networks," IEEE International Conference on Communications (ICC), pp. 710-715, IEEE, May 2022, doi: 10.1109/ICC45855.2022.9839024.
2. M. S. Riaz, H. N. Qureshi, U. Masood, A. Rizwan, A. Abu-Dayya, and A. Imran, "Deep Learning-based Framework for Multi-Fault Diagnosis in Self-Healing Cellular Networks," IEEE Wireless Communications and Networking Conference (WCNC 2022), pp. 746-751, April 2022, doi: 10.1109/WCNC51071.2022.9771947.
3. A. Rizwan, A. Abu-Dayya, F. Filali and A. Imran, "Addressing Data Sparsity with GANs for Multi-fault Diagnosing in Emerging Cellular Networks," International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 318-323, IEEE, March 2022, doi: 10.1109/ICAIIC54071.2022.9722696.
4. M. Nabeel, M. Manalastas, A. Ijaz, H. Refai, and A. Imran, "Investigating Handover Behavior with 5G and Beyond TurboRAN Testbed," Seventh International Conference On Mobile And Secure Services (MobiSecServ), pp. 1-6, February 2022, doi: 10.1109/MobiSecServ50855.2022.9727204.
5. M. U. B. Farooq, M. Manalastas, W. Raza, A. Ijaz, S.M.A. Zaidi, A. Abu-Dayya, and A. Imran, " Data Driven Optimization of Inter-Frequency Mobility Parameters for Emerging Multi-band Networks," IEEE Global Communications Conference (GLOBECOM), pp. 1-6, IEEE, December 2020, doi: 10.1109/GLOBECOM42002.2020.9348101.
6. S. M. A. Zaidi, M. Manalastas, A. Abu-Dayya, and A. Imran, "AI-Assisted RLF Avoidance for Smart EN-DC Activation," IEEE Global Communications Conference (GLOBECOM), pp. 1-6, IEEE, December 2020, doi: 10.1109/GLOBECOM42002.2020.9322339.
Theses
1. Realistic Modeling and Evaluation of Handover Events in a Multi-Carrier Cellular Network as a Preliminary Step Towards COP-KPI Relationship Realization (Marvin Manalastas Masters Thesis)