Mobility: Designing Advanced Mobility Management and Utilization Framework for enabling mmWave Multi-Band Ultra-Dense Cellular Networks of Future (AM-MUF)

  • how to manage user mobility resource efficiently and seamlessly in future dense networks consisting of cells of varying sizes on a wide range of frequency bands with entirely different propagation characteristics?

    (1) Mobility management in the current networks requires continuous signaling to support handover (HO) preparation, execution, completion phases. In UDMN, with dramatically increased HOs, signaling overheads with current mobility management mechanisms will become unacceptably high.
    (2) Current networks already require extensive ongoing field trial based or semi-manual optimization of a myriad of mobility management parameters for each cell, such as: neighbor relationship tables, Cell Individual Offsets (CIO), hysteresis, Time to Trigger (TTT), thresholds for handover related events such as A1, A2, A3, A4 and A5, B1 and B2. With current approach, in UDMN this process may become too complex to be viable.
    (3) In LTE HO failure rate is targeted for below 5%. However, recent 3GPP study shows that adding only ten small cells per macro cell can push the HO failure rate to as high as 60%, indicating the breakdown of current mobility management mechanism in UDMN.
    (4) In UDMN given the much smaller average cell size and thus small user sojourn time, the time to complete a HO must be reduced significantly from the current LTE target of 65ms. New agile HO design is also needed to meet the ambitious low latency requirements in 5G.
    (5) To perform a HO in UDMN, mobile devices must discover small cells operating on very different frequency bands by periodically running an Inter Frequency Small Cell Discovery (ISCD) process. UDMN will require ISCD rate much higher than the current rate for LTE. This will exacerbate mobile battery life problem in UDMN.
    (6) Conventional cellular bands exhibit graceful signal decay and thus allow use of hysteresis for HO preparation phase and to avoid ping pong. However, mmWave cells in UDMN will have sharp (line of sight) and sudden (when link becomes non-line of sight) signal strength drops, requiring re-thinking of the way HOs are performed in current networks.
    (7) Unlike conventional band cells that have omni-directional or wide-beam sector antennas and thus can easily be discovered by an oncoming mobile user to start the HO process, mmWave cells will rely on narrow beams to overcome the high propagation losses. This means unless a mmWave cell has aligned its beam with an oncoming mobile user, it cannot discover the user, or be discovered by the user to start the HO process. This gives rise to a new type of cell/user discovery problem unseen in legacy networks making mobility management further challenging in UDMN.
    (8) Finally, these idiosyncrasies of UDMN render ineffective the currently proposed legacy network based designs of the two key and recently standardized mobility management Self-Organizing Network (SON) functions namely: Mobility Robust Optimization (MRO) and Mobility Load Balancing (MLB).

  • The main idea behind the proposed AM-MUF is to first develop robust models to predict certain attributes of user mobility in UMDN specific environments and then exploit these attributes for developing novel algorithms, protocols and solutions to address the challenges identified above. The output of this project will thus effectively set the foundations for the much needed next generation mobility management Proactive SON (P-SON) functions for 5G UDMN

    • Prof. Ali Imran (Principal Investigator- University of Oklahoma)
    • Prof. Pramode Verma (University of Oklahoma)
    • Prof. Hazem Refai (University of Oklahoma)
    • Prof. Muhammad Ali Imran (University of Surrey)
    • Prof. Rahim Tafazolli(University of Surrey)
    • Asad Zaidi (PhD candidate - University of Oklahoma)
    • Umair Hashmi (PhD candidate - University of Oklahoma)
    • Azar Taufique (PhD candidate - University of Oklahoma)
    • Hasan Farooq (PhD candidate - University of Oklahoma)
    • Ahmad Asghar (PhD candidate - University of Oklahoma)
    • Haneya Naeem Qureshi (PhD candidate - University of Oklahoma)
    • Arsalan Darbandi (Master's degree candidates - University of Oklahoma)
    • şinasi çetinkaya (Master's degree candidates - University of Oklahoma)
    • Marvin Manalastas (Master's degree candidates - University of Oklahoma)
    • Shruti Bothe (Master's degree candidates - University of Oklahoma)

  • AM-MUF will be developed through three interlinked research thrusts:
    (1) Developing scalable and low complexity Mobility Prediction Models for UDMN (MPM)
    (2) Designing Proactive –Mobility Robustness Optimization and Proactive-Mobility Load Balancing Functions (P-SON)
    (3) Validating UDMN specific Accuracy Limits of MPM and Performance Bounds of P-SON (ALB)

    To achieve ambitious goals of this aspiring project, the researchers will leverage a systematic methodology consisting of analytical modeling, system level simulations, synthetic data based training and testing, real data based validation, a full scale 5G test-bed based evaluations and field trials on a real network.

  • Intellectual Merit

    • Journal
    • 1. U. S. Hashmi, S. A. R. Zaidi and A. Imran, "User-Centric Cloud RAN: An Analytical Framework for Optimizing Area Spectral and Energy Efficiency," in IEEE Access, vol. 6, pp. 19859-19875, 2018. doi: 10.1109/ACCESS.2018.2820898.

      2. Onireti, O., Imran, A. & Imran, M., “Coverage and Rate Analysis in the Uplink of Millimeter Wave Cellular Networks with Fractional Power Control” in EURASIP Journal on Wireless Communications and Networking (2018): 195.

      3. Azar Taufique, M. Abdelrahim, Ali Imran, Rahim Tafazolli, “On Analytical Modelling for Mobility Signalling in Ultra-dense HetNets," in the press for publication in IEEE Transactions on Vehicular Technology, 2018. doi: 10.1109/TVT.2018.2864627.

      4. H. Farooq, A. Asghar and A. Imran, "Mobility Prediction based Autonomous Proactive Energy Saving (AURORA) Framework for Emerging Ultra-Dense Networks," in IEEE Transactions on Green Communications and Networking. doi: 10.1109/TGCN.2018.2858011.

      5. O. Onireti, A. Imran and M. A. Imran, "Coverage, Capacity, and Energy Efficiency Analysis in the Uplink of mmWave Cellular Networks," in IEEE Transactions on Vehicular Technology, vol. 67, no. 5, pp. 3982-3997, May 2018. doi: 10.1109/TVT.2017.2775520

    • Thesis
    • 1. One PhD thesis has been produced. This thesis presents mobility management solutions using CDSA architecture.

    Broader Impact
    • Course Delivered by PI
    • 1. Course titled “Emerging Topics in LTE-A and 5G”. This course was taught by PI in Spring 2018 and was taken by all students working on the project.
      2. CCourses on cellular system advance concepts including mobility management, machine learning, Big data analytics were arranged for project students in Fall 2017.

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

    • Tutorials
    • 1. PI organized a tutorial at IEEE PIMRC Paris, Oct 2017 on P-SON enabled by MPM. The material of this tutorial was also delivered in-house for project students.
      2. PI gave a presentation at the 5GIC university of Surrey, during his NSF IRES funded visit to 5GIC in July 2018.
      3. PI gave a presentation at the University of Glasgow, during his NSF IRES funded visit to the UK in July 2018.

    • Keynotes/Invited Talks
    • 1. “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.
      2. “Challenges in use of AI for network Automation and how to address these challenges”, invited talk at Fujitsu Laboratories, Europe, July 11, 2018, London, UK.
      3. “Leap Towards Zero Touch RAN Automation”, invited talk, at Telekom Austria HQ, Jun 25, 2018, Vienna, Austria.
      4. “On the role of AI in 5G and beyond”, invited plenary talk at 5G North America, May 14-16, 2018, Austin Texas.
      5. “How AI will transform the Future of RAN”, invited talk at AT&T Campus, April 17, 2018, San Romano, CA.
      6. “Towards next generation AI Enabled SON”, invited seminar at T-Mobile HQ, April 1, 2018, Seattle, WA.
      7. “Future of open source software-defined Big Data-Enabled RAN”, keynote at the 11th international conference on open source system and technologies, Dec 18-20, 2017, Lahore, Pakistan,
      8. “Next Generation Artificial Intelligence Based RAN”, invited talk in an industry panel at RAN USA, Dec 4, 2017, Silicon Valley, USA

    • K-12 Outreach Program
    • 1. To continue the K-12 outreach program PI is running and recruit new K-12 students to work on the REU component of this project, in Feb 2018, PI gave a presentation at Booker T Washington High School, Tulsa. This is a historically black school. One student was recruited as a result of this talk for Summer 2018.

    • International Collaboration Opportunities
    • 1. To give project GRAs an experience of international collaboration, they were introduced with PI’s collaborators at 5GIC, Surrey; the University of Glasgow and the University of Leads UK, via emails and video conference sessions.

    • Conference Attendance
    • 1. One GRA involved in the project was sent to present her paper and attend IEEE PIMRC held in Bologna, Italy, Sep 2018.

    • Internship
    • 1. Internships were arranged for two GRAs involved in the project with Bell Labs, NJ in Summer 2018.

    • Mentoring
    • 1. Project students were provided one to one mentoring by PIs through twice a week meetings for close guidance to conduct the proposed research.
      2. One of the project participant students who was eligible for participation in PI’s ongoing IRES project was sent to 5GIC, Surrey, the UK for summer 2018.

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