# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 1 Introduction
In terms of implementation, it is difficult to use a complex algorithm because a coordinator in a WBAN is usually limited in the computation power, storage, and energy. Therefore, a WBAN performs a relatively simple operation repeatedly to determine a channel to use. However, it takes time for the iterative process to converge to a stable point. Therefore, the inter-WBAN interference during the transient time degrades the data transmission quality within a WBAN. To resolve these issues, we take a machine learning approach and propose a dynamic channel allocation method operating in a centralized manner. We aim to increase the data rate of a WBAN by assigning channels in a way that the distance between WBANs using the same channel increases as much as possible. We model this problem as a combination of a graph partitioning problem and a graph coloring problem. To solve the problem, we devise two modules: a base module and an adaptive module. In the base module, a server collects the information from the WBANs and constructs an interference relationship graph among them. Then, a server partitions the graph by using the Louvain graph partitioning algorithm [19] so that its modularity is maximized. Then, for each cluster, the Welsh Powell graph coloring method is applied to the subgraph formed by the WBANs in the same cluster [20] to determine the channels for them. The adaptive module is designed to adjust to the gradual topology changes fast. It reuses the cluster information made by the base module to avoid the graph partitioning procedure. In contrast to the distributed method where messages are exchanged among WBANs through a narrow band channel, a cellular network providing high data transmission rate (such as LTE, 5G, etc.) is used for message exchanges between a server and WBANs. Therefore, a server acquires necessary information in time regardless of the density of the coexisting WBANs. We summarize our main contributions as follows. * We address the inter-WBAN interference problem and propose a centralized channel allocation algorithm, which resolves the problem by using both a graph partitioning method and a graph coloring method. Our method is practical and adaptable to dynamic environments because it operates in a self-configurable manner without human intervention. * We address the problem of topology changes caused by the mobility of a WBAN and propose an adaptive module. The adaptive module replaces the graph partitioning process with simple comparison operations. The performance of the adaptive module is comparable to that of our base module when the graph topology changes gradually. * We conduct extensive simulation studies to assess the performance of our method. Compared with the recent centralized channel allocation method, our method increases the average data rate of a WBAN by 2.2 times when the density of the WBANs is 0.5 WBANs per square meter. When the density of the coexisting WBANs increases to 2 WBANs per square meter, our method increases the average data rate by 5.4 times. In addition, compared with a distributed method involving an iterative process, our method reduces the channel decision time by 94.6%. The rest of the paper is organized as follows. In Section 2, we discuss the related works. After describing a system model in Section 3, we detail our channel allocation method in Section 4. We evaluate the performance of the proposed method through extensive simulation studies in Section 5. We conclude the paper in Section 6 with future research directions.