Benefit-tree Dynamic Clustering Algorithm Based on Degree of Wiliness to Cooperate for Base Station Cooperation

As the existing clustering algorithms are lack of effective guidable clustering model, an Degree of Wiliness to Cooperate (DWC) based clustering model is proposed, in which the clustering objective of maximizing the system sum rate is approximately to maximizing the sum of DWC between every two Base Stations (BS) in system. Based on this, the clustering issue is modeled as constructing benefit-trees of a connected graph with edge costs. Then a benefit-tree dynamic clustering algorithm is proposed. This algorithm simultaneously generates several clusters of dynamic size which could solve the limited-capacity problem caused by conventional orderly clustering scheme. Besides, the maximum sum of DWC in clustering results offers the approximately best system clustering capacity. Simulation results show that compared to the conventional greedy clustering algorithm, the system spectrum efficiency in this algorithm increases about 0.4 bit/Hz and the computational complexity is directly proportional to system size.