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

  • 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.

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