Abstract
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.
Highlights
In the past few years, fifth generation (5G) wireless communication has become the center of discussion for researchers in this area [1, 2]. 5G is entirely different from conventional standards in that additional improvements cannot meet their requirements
We evaluate the complexity and the achievable rate per small cell with the proposed algorithm and show that our algorithm achieves almost optimal performance, i.e., almost the same performance achieved in an exhaustive search, while significantly reducing the clustering process complexity
We focus on the clustering problem of small cells in this paper, the proposed algorithm can be applied to more general environments, e.g., multi-cell cellular networks or ad hoc sensor networks
Summary
In the past few years, fifth generation (5G) wireless communication has become the center of discussion for researchers in this area [1, 2]. 5G is entirely different from conventional standards in that additional improvements cannot meet their requirements. We select small cells sequentially from the first element of to the last one, which is equivalent to determining the clustering formation. STEP 1 : exhaustive search for all possible clustering formations is transformed to the sequential determination of each cluster in the same way as in a greedy algorithm STEP 2 : sequential determination of each cluster is transformed to the sequential selection of small cells. These transformations are conducted to narrow the search range, which results in reduced complexity. We derive the complexity of each algorithm quantitatively
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