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