# Clustering-Based Channel Allocation Method for Mitigating Inter-WBAN Interference
## 2 Related Works
### Channel Allocation Methods
Thus, our method can determine channels faster than the method proposed in [10]. Compared with the method in [23], our method is more adaptive to the dynamic environment because our method does not have a hyperparameter such as \(K\) in [23] that needs to be determined before an algorithm is applied. In [27], a hybrid method is introduced. They formulate the channel selection problem as a Stackelberg game with pricing. An AP acts as a leader in the game and configures the price of the resources to limit the interference caused by WBANs. WBANs act as followers in the game and try to maximize the utility by using the resource price determined by an AP. However, they consider a static environment, and it is not clear how to systematically configure hyperparameters that lead to an optimal result in the situation where both the number and the positions of WBANs change. Our method differs with the hybrid method in that our method does not require both a pre-configured hyperparameter and the competition among the WBANs. In [27], WBANs play a game given the resource price configured by an AP. Therefore, it takes multiple iterations for the WBANs to reach an equilibrium state. Since our method does not involve such an iteration, it can determine channels faster than the method in [27]. On behalf of WBANs, a server determines the channels in the centralized channel allocation method. A server is supposed to have much higher computing capability than a coordinator in a WBAN. Therefore, a server is expected to determine the channels faster than a coordinator even in the case where the time complexity of the channel allocation algorithm in a server is bigger than that running in a coordinator. A server exploits the information obtained from WBANs to determine the channels. In other words, in contrast to the distributed method where messages are exchanged among WBANs, messages are exchanged between a server and WBANs in the centralized method. Generally, a coordinator in a WBAN uses a cellular network such as LTE and 5G to send messages to a server. Since a cellular link is more reliable than the signaling link among WBANs, a server is able to collect necessary information reliably and determine channels without iteration even when the density of WBANs is high. In [24], a server constructs an interference matrix by using the information gathered from WBANs and determines channels by exploiting the matrix. However, they do not consider the situation where the number of available channels is less than the number of interfering WBANs. In contrast, we consider the situation where the number of the coexisting WBANs is larger than the number of available channels. In this environment, we aim to maximize the total data rate. The authors in [25] propose a channel hopping method by exploiting the spatial and temporal regularity in human trajectories. A server measures both the trajectory and the channel hopping sequence of each WBAN in an area. Then, a server allocates a channel hopping sequence to a WBAN according to the human trajectory similarities and the channel hopping sequence similarities in the area. Since the channel assignment method is based on the regularity in human trajectory, it may not be applicable when a set of humans in an area changes. In contrast, we consider an environment where a set of WBANs in an area changes over time. Since we do not rely on the regularity in human trajectory, our method can adjust to the dynamic changes in the set of WBANs in an area. The authors in [26] propose a machine learning approach to resolve the channel allocation problem. They cluster the coexisting WBANs by using the K-nearest neighbor (KNN) method. After formulating clusters, they assign a channel to each cluster by using a graph coloring method. The WBANs in the same cluster share the bandwidth of the channel allocated to the cluster in an FDMA manner. Since the data rate depends on the amount of bandwidth, the data rate achievable by a WBAN in a cluster decreases as the number of WBANs in a cluster increases.