A low-complexity cell clustering algorithm in dense small cell networks

Ryuma Seno, Tomoaki Ohtsuki, Wenjie Jiang, Yasushi Takatori

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Clustering plays an important role in constructing practical network systems. In this paper, we propose a novel clustering algorithm with low complexity for dense small cell networks, which is a promising deployment in next-generation wireless networking. Our algorithm is a matrix-based algorithm where metrics for the clustering process are represented as a matrix on which the clustering problem is represented as the maximization of elements. The proposed algorithm simplifies the exhaustive search for all possible clustering formations to the sequential selection of small cells, which significantly reduces the clustering process complexity. We evaluate the complexity and the achievable rate with the proposed algorithm and show that our algorithm achieves almost optimal performance, i.e., almost the same performance achieved by exhaustive search, while substantially reducing the clustering process complexity.

Original languageEnglish
Article number262
JournalEurasip Journal on Wireless Communications and Networking
Issue number1
Publication statusPublished - 2016 Dec 1


  • Cell clustering algorithm
  • Small cell networks

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science Applications
  • Computer Networks and Communications


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