Overcoming 5G ultra-density with game theory: Alpha-beta pruning aided conflict detection


Bilen T., Canberk B.

PERVASIVE AND MOBILE COMPUTING, vol.63, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Review
  • Volume: 63
  • Publication Date: 2020
  • Doi Number: 10.1016/j.pmcj.2020.101133
  • Journal Name: PERVASIVE AND MOBILE COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC
  • Istanbul Technical University Affiliated: Yes

Abstract

The capacity and coverage needs of 5G NR (New Radio) networks are satisfied by deploying a high number of small cells in the coverage area of a macro cell. In 5G Dual Connectivity (DC), these small cells are represented as eNBs with 4G characteristics different from the macro cells which are the 5G NR gNBs. The mobile node (MN) can establish a connection with these cells in different technologies at the same time. To effectively manage this heterogeneity and density, the self-organizing networks can be used as a solution. The mobility load balancing (MLB) and mobility robustness optimization (MRO) are two different schemes in self-optimization to enable load balancing and handover management, respectively. These two functions affect the same parameters (e.g hysteresis, time-to-trigger, cell individual offset) from the opposite way. This conflict is further worsened in DC due to the heterogeneous ultra-dense architecture of 4G small cells at the 5G core. Accordingly, the frequent and ping-pong handovers with dropped and blocked calls are more observed because of this conflict. A key contribution of our work is timely detection of this MLB&MRO conflict through the alpha-beta pruning which is a game-theoretic approach to reduce these problems. This mechanism is based on the principle of pruning the unnecessary leaves on the tree. Thanks to this characteristic, we can overcome the ultra-density to reduce conflict detection time. To utilize the alpha-beta pruning, we consider the small cells as leaves on a tree with corresponding load values. To estimate these loads, we also utilize the G/G/1 queuing system. After finding the small cells which are probable for MLB&MRO conflict, we utilize the two-dimensional Markov chain to disable fluctuations causing this conflict. Results reveal that the proposed strategy reduces the call dropping and blocking probabilities 15% and 23% compared to the traditional MLB&MRO approach. (c) 2020 Elsevier B.V. All rights reserved.