In a 5G ultra-dense network, dynamic network topology and traffic patterns lead to excessive system overhead and complex radio resource conflicts. The cloud radio access network and the fog computing have the advantages of high computation capabilities and low transmission delays. Therefore, by taking full advantage of these two characteristics, this study proposes a novel radio resource coordination and scheduling scheme in an ultra-dense cloud-based small cell network. Interference among small cells (or remote radio heads) can be avoided by implementing centralized cooperative processing in the base band unit pool in advance. Resource sharing in coordination and transfer depend on fog computing to relieve the overloaded cloud processing platform and reduce transmission delays, thereby maximizing resource utilization and minimizing system overhead when the network topology and number of users change dynamically. The simulation shows that the proposed scheme can increase the system throughput by 20% compared with the clustering-based algorithm; it can also increase system throughput by 33% compared with the graph coloring algorithm, decrease the signaling overhead by about 50%, and improve network's quality of service.
The exploration about cluster structure in Complex Networks is crucial for analyzing and understanding Complex Networks. K-means algorithm is a widely used clustering algorithm. In this paper, a novel algorithm is proposed based on K-means. Considering, Complex Networks obeys Power-law Degree Distribution, this improved algorithm chooses nodes with high importance as the initial clustering centroids, and uses the distance to these key nodes as clustering measurement. The experiments prove that the new algorithm can conduct accurate clustering with acceptable performance.
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