Data-Driven Resource Management for Ultra-Dense Small Cells: An Affinity Propagation Clustering Approach

Li-Chun Wang, Shao Hung Cheng

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Deploying dense small cells is the key to providing high capacity, but raise the serious issue of energy consumption and inter-cell interference. To understand the behaviors of ultra-dense small cells (UDSC) with dynamic interference and traffic patterns, this paper presents a data-driven resource management (DDRM) framework to implement power control and channel rearrangement in UDSC. We find that the inter-cell interference can be used to describe the affinity of cells. Thus, we propose an unsupervised learning algorithm for UDSC, called affinity propagation power control (APPC) mechanism. In principle, APPC first groups small cells into different clusters and identifies cluster centers. Next, the transmission power of a cluster center is decreased to reduce the interference to the neighboring cells' users in this cluster. Since lowering transmission power of a cluster center cell may cause the performance degradation to the users at the cell edge, a victim-aware channel rearrangement (VACR) mechanism is further designed to adjust the channel usage bandwidth of the neighboring cells in order to guarantee the quality of service of these victimized users. Our simulation results show that the DDRM framework can significantly improve energy efficiency and throughput in UDSC compared to the existing approaches.

Original languageEnglish
Pages (from-to)267-279
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Volume6
Issue number3
DOIs
StatePublished - Jul 2019

Keywords

  • Affinity propagation clustering
  • Clustering algorithms
  • Data-driven
  • Energy efficiency
  • Interference
  • Power control
  • Quality of service
  • Resource management
  • Throughput
  • Ultra-dense small cells
  • Unsupervised learning

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