AI4Networks Research Center

TurboRAN: State of the art testbed for enabling unprecedented experimental research on multi-band, multi-tier artificial intelligence enabled wireless networks of future

TurboRAN: Testbed for Ultra-Dense- Multi-Band Control and Data Plane Split Radio Access Networks of the Future.
Funding: This project is funded by the National Science Foundation of USA, under Award No. 1730650.
Duration: Three years (October 1, 2017 – September 30, 2021).
Funding Amount: $1,000,000.00

Project Goals

The goal of this project is to develop TurboRAN: Testbed for Ultra-Dense-Multi-Band Control and Data Plane Split Radio Access Networks of the Future. TurboRAN will cover an area of 360,000 square meters at The University of Oklahoma (OU) campus as well as an indoor lab area of 7,500 square feet. This novel testbed will have the ability to experimentally test the static and dynamic parameters associated with a multi-cell, multi-tier, multi-band cellular eco-system being envisioned for future wireless systems. To capture the envisioned deployment scenarios for beyond 5G networks and enable compelling new research on AI enabled proactive self-organizing networks (P-SON), control and data plane split architecture (CDSA) and database aided CDSA, TurboRAN will comprise of three layers of cells; operation in HF bands as well as in mm-Wave bands; conventional heterogeneous as well as C-RAN implementation; MIMO provision; mobile access points and UEs. In addition to end-to-end programmability and flexibility for modelling a variety of futuristic heterogeneous cellular system scenarios, another unique feature of the TurboRAN will be the integrated big data processing capability to explore the potential of untapped cellular control and user plane data for designing artificial intelligence enabled proactive rather than reactive next generation SON (i.e. P-SON). Together, these features make the planned testbed a powerful enabler for cutting-edge research towards future cellular networks.

Active Members

PI: Prof. Ali Imran (Principal Investigator – University of Oklahoma)

Co-PI: Prof. Thordur Runolfsson (Co-Principal Investigator – University of Oklahoma)

Co-PI: Prof. Hazem Refai (Co-Principal Investigator – University of Oklahoma)

Co-PI: Prof. Samuel Cheng (Co-Principal Investigator – University of Oklahoma)

Co-PI: Prof. Choon Yik Tang (Co-Principal Investigator – University of Oklahoma)

Post Doctoral Fellow: Dr. Muhammad Nabeel (Postdoctoral Research Fellow – University of Oklahoma)

GRA: Syed Asad Zaidi (Ph.D. Student – University of Oklahoma)

GRA: Haneya Naeem Qureshi (Ph.D. Student – University of Oklahoma)

GRA: Usama Masood (Ph.D. Student – University of Oklahoma)

GRA: Marvin Manalastas (Ph.D. Student – University of Oklahoma)

GRA: Muhammad Sajid Riaz (Ph.D. Student – University of Oklahoma)

Ph.D. Student: Anas Amaireh (Ph.D. Student – University of Oklahoma)

Ph.D. Student: Ahmed Elsebaay (Ph.D. Student – University of Oklahoma)

Past Members

Post Doctoral Fellow: Dr. Hasan Farooq

Fulbright Ph.D. Scholar: Dr. Umair Sajid Hashmi (Ph.D. graduate)

GRA: Dr. Ahmad Asghar (Ph.D. graduate)

Rahim Tafazolli (University of Surrey, UK)
Igor Alvarado (National Instrument)
Sinan Akkaya (AT&T)
Alan Johnson (US Cellular)
Farooq Khan (PHAZR)
Kevin Long (US Cellular)
Marcelo Pavoa (T-Mobile)

Integration of project outcomes into teaching

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

"Emerging Topics in 5G and Beyond" Offered in Sring 2018, 2019 and 2020.
"Telecommunication Technologies" Offered in Fall 2017, 2018, 2019.
Courses on machine learning, Big data analytics and stochastic processes were arranged for project GRAs in Fall 2017.

K-12 Outreach Program

PI is running a K-12 outreach program with Booker T. Washington high school, Tulsa. The majority of students in this school belong to underrepresented communities in STEM.
Through this program several high school students have participated in research being carried out as part of this project and other NSF funded projects.

For the details of this K-12 outreach program and how to participate see:
Internship opportunities for K-12

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.
Ali Imran, Muhammad Ali Imran, "LEAP for IoT- LEAN, Elastic, Agile and Proactive (LEAP) Wireless Networks for Enabling IoT", a half day tutorial at 2017 IEEE PIMRC, Montreal, 8-13 Oct 2017.
Ali Imran, "Can mmWave, massive MIMO and densification suffice to meet capacity and QoS requirements in emerging IoT?", half day seminar at International Conference on Communications Technologies (ComTech-2017), Pakistan, 19-21 October, 2017.
Ali Imran "CDSA and BSON: The Two Key Enablers of Lean, Elastic and Proactive Wireless Networks Needed for Future IoT", a half day tutorial to be delivered at 2016 IEEE 3rd World Forum on Internet of Things, Washington DC, 6-8 Dec, 2017.

Keynotes/Invited Talks

Ali Imran "Addressing the Hyper Parameterization Challenge in AI for Wireless Networks", panel talk at, IEEE Globecom, Dec, 9-13-2019
Ali Imran "AI enabled Zero Touch automation for 5G and beyond" invited talk, 5G NA, Silicon Valley, Nov 13-14, 2019.
Ali Imran "5G Networks, AI and Machine Learning Empowering the Enterprise Digital Transformation", keynote in AI for Telcom track at AIWorld, 23-25, Oct, 2019. Boston, MA, 2019.
Ali Imran "Role of AI in Emerging Networks: 5G and Beyond", keynote at 12th IEEE International Conference on Open Source Systems and Technologies, 19-21, Dec, 2018 (IEEE ICOSST 2018), Lahore, Pakistan.
Ali Imran "Network Automation: Fundamental Challenges, Solution Approaches and Opportunities", invited talk at Summer School Sponsored by IEEE ComSoc and HEC at LUMS, Aug 9, 2018, Lahore, Pakistan
Ali Imran "Towards Next Generation AI Enabled SON", invited seminar at T-Mobile HQ, April 16, 2018 Seattle, WA.
Ali Imran "Future of Open Source Software Defined Big Data Enabled RAN", keynote at 11th international conference on open source system and technologies, Dec 18-20, 2017, Lahore, Pakistan.
Ali Imran "Next Generation Artificial Intelligence Based RAN", invited talk in an industry panel at RAN USA, Dec 4, 2017, Silicon Valley, USA.

Awards

One of the theoretical solutions developed by the GRA working on the project won IEEE Green ICT Best Solution International.

International Collaboration Opportunities

Project students gained international collaboration experience by working with collaborators at:

5GIC, Surrey, UK.
The University of Glasgow. UK.
The University of Leeds, UK.

Conference Attendance by Students

Project students have attended and presented their work at following conferences
1. IEEE 17th Annual Consumer Communications and Networking Conference (CCNC), Las Vegas, NV, Jan 2020.
2. IEEE PIMRC, Bologna, Italy, Sep 2018.
3. IEEE ICC, Kansas, USA, 20-24 May, 2018.

Industry Internships

Project students have interned with the following industry collaborators:
1. New T-Mobile, CT in Spring 2020.
2. Sprint, CT in Fall 2019.
3. MobileComm, Dallas in Summer 2019.
4. Bell Labs, NJ in Summer 2018.
5. Phazr, Dallas in Fall 2017.

Journal Articles
1. M. Manalastas, M. U. B. Farooq, S. M. A. Zaidi, and A. Imran, "Towards the Development of 6G System Level Simulators: Addressing the Computational Complexity Challenge," IEEE Wireless Communications [Accepted], April 2023.
2. 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.
3. A. Ijaz, W. Raza, H. Farooq, M. Manalastas and A. Imran, "An AI-Enabled Framework to Defend Ingenious MDT-based Attacks on the Emerging Zero Touch Cellular Networks," IEEE Network, March 2023, doi: 10.1109/MNET.006.2100377.
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. Nabeel, A. Ijaz, S. M. A. Zaidi, U. Masood, H. N. Qureshi, H. Refai, and A. Imran, "Design Considerations and Deployment Challenges for TurboRAN 5G and Beyond Testbed," IEEE Access, pp. 1-1, April 2022, doi: 10.1109/ACCESS.2022.3166947.
7. H. N. Qureshi, M. Manalastas, A. Imran, and M. O. Al Kalaa, "Service Level Agreements for 5G-Enabled Healthcare Systems: Challenges and Considerations," IEEE Network, vol. 36, issue. 1, pp. 181 - 188, December 2021, doi: 10.1109/MNET.011.2100343.
8. 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.
9. H. N. Qureshi, and A. Imran, "Optimal Bin Width for Autonomous Coverage Estimation Using MDT Reports in the Presence of User Positioning Error," IEEE Communications Letters, vol. 23, no. 4, pp. 716-719, April 2019, doi: 10.1109/LCOMM.2019.2899094.
10. H. Farooq, A. Asghar, and A. Imran, "Mobility Prediction-Based Autonomous Proactive Energy Saving (AURORA) Framework for Emerging Ultra-Dense Networks," IEEE Transactions on Green Communications and Networking, vol. 2, no. 4, pp. 958-971, December 2018, doi: 10.1109/TGCN.2018.2858011. This solution won first prize in IEEE Green ICT International competition for Best Solution, held in Paris, 2017. The competing teams included participants from over 30 countries.
11. A. Asghar, H. Farooq, and A. Imran, "Concurrent Optimization of Coverage, Capacity, and Load Balance in HetNets Through Soft and Hard Cell Association Parameters," IEEE Transactions on Vehicular Technology, vol. 67, no. 9, pp. 8781-8795, September 2018, doi: 10.1109/TVT.2018.2846655.
12. A. Asghar, H. Farooq, and A. Imran, "Self-Healing in Emerging Cellular Networks: Review, Challenges, and Research Directions," IEEE Communications Surveys & Tutorials, vol. 20, no. 3, pp. 1682-1709, July 2018, doi: 10.1109/COMST.2018.2825786.
13. A. Zoha, A. Saeed, H. Farooq, A. Rizwan, A. Imran, and M. A. Imran, "Leveraging intelligence from network CDR data for interference aware energy consumption minimization," IEEE Transactions on Mobile Computing, vol. 17, no. 7, pp. 1569-1582, July 2018, doi: 10.1109/TMC.2017.2773609.
Peer Reviewed Conference Papers
1. 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.
2. 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.
3. 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.
4. 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.
5. J. Shodamola, U. Masood, M. Manalastas, and A. Imran, " A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters," BlackSeaCom, IEEE, May 2020.
6. M. Manalastas, H. Farooq, S. A. Zaidi, and A. Imran, "Where to Go Next? : A Realistic Evaluation of AI-Assisted Mobility Predictors for HetNets," IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), pp. 1-6, IEEE, Jan, 2020, doi: 10.1109/CCNC46108.2020.9045127.
7. U. Masood, H. Farooq, and A. Imran, "A Machine Learning based 3D Propagation Model for Intelligent Future Cellular Networks," IEEE Global Communications Conference (GLOBECOM), pp. 1-6, IEEE, December 2019, doi: 10.1109/GLOBECOM38437.2019.9014187.
8. H. Farooq, A. Imran, and M. Jaber, "AI empowered Smart User Association in LTE Relays HetNets," IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6, IEEE, August 2019, doi: 10.1109/ICCW.2019.8756942.
9. H. Gebre, H. Farooq, and A. Imran, "What machine learning predictor performs best for mobility prediction in cellular networks?," IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1-6, IEEE, August 2019.
10. A. Asghar, H. Farooq, and A. Imran, "Outage Detection for Millimeter Wave Ultra-Dense HetNets in High Fading Environments," IEEE International Conference on Communications (ICC), pp. 1-6, IEEE, August 2019, doi: 10.1109/ICC.2019.8762054.
11. U. Masood, A. Asghar, A. Imran, and A. N. Mian, "Deep Learning Based Detection of Sleeping Cells in Next Generation Cellular Networks," IEEE Global Communications Conference (GLOBECOM), pp. 206-212, IEEE, December 2018, doi: 10.1109/GLOCOM.2018.8647689.