# Channel Modeling and Analysis for the Sensor Network Inside Tower Buildings
## 1 Introduction  
Wireless sensor networks (WSN) are widely used in various fields, such as military reconnaissance, health care, and industrial automation, due to their low cost, low power consumption, and easy of deployment in a large area [1, 2, 3, 4]. Meanwhile, tower structure has been widely used in a lot of buildings and bridges due to its excellent anti-seismic performance and low cost [5]. However, the communication link interruption often occurs in these tower buildings, which is extremely important for some special applications like rocket lunching. Since there is large proportion of metal material inside the tower building, the radio signal experiences strong reflection and serious multipath effect [6]. In addition, the tower building has a typical semi-closed and hollow-carved structure, where the signal can directly pass through. Therefore, the propagation channel inside tower building is different from the indoor channel [7, 8, 9, 10], outdoor channel [11, 12, 13] and outdoor to indoor (O2I) channel [14]. On the other hand, the field measurement in the tower building would consume a lot of manpower and material resources, how to efficiently and economically use theoretical modeling to evaluate the communication system has become an important topic. Symmetry-based digital twin technology can reproduce the communication channel of a real scene in virtual network space by establishing an accurate channel model, which could play an important role in system design, optimization, and evaluation of WSN. Since the communication between different sensor nodes has limited time resolution, some research works approximated the rays with similar time-of-arrival (ToA) as one cluster [15, 16, 17]. Meanwhile, the clustering method has a great influence on the cluster-basedchannel model, which has gained a lot of attentions [18; 19; 20; 21; 22; 23]. At the very beginning, a visual inspection method was employed to identify clusters in [18], but this method was only suitable for low dimensions or small number of rays. Due to the complexity of channel data, several machine learning (ML)-based algorithms were presented, i.e., sparsity-based algorithm [19], density-based spatial clustering of applications with noise (DBSCAN) algorithm [20], and k-means algorithm [22; 23; 24; 25; 26]. Note that both the sparsity-based algorithm and DBSCAN algorithm requires prior cluster information, i.e., initial clustering reference and density threshold. However, it is difficult to exact the prior information from the realistic channel data. Recently, the k-means algorithm is widely adopted for its easy implementation and robustness. For example, the authors in [24] conducted the clustering analysis of multidimensional wind speed data based on Euclidean distance, but the traditional Euclidean distance cannot be used in the channel clustering directly since it did not consider the characteristic of channel parameters. Furthermore, the authors in [26] presented a multipath component distance (MCD) for channel clustering based on multiple dimensions, i.e., time of arrival, azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA). However, the weight of different dimensions in the MCD was not taken into account. On the other hand, the analysis of communication performance is important for the WSN node placement. Several related works can be addressed in [27; 28; 29; 30; 31; 32; 33]. In [27; 28; 29], the authors proposed three different optimization algorithms to optimize the number and location of WSN nodes. The bit error rate (BER) with different modulation modes were studied in [30; 31; 32]. The authors in [33] discussed the influence of channel clustering on the channel capacity. However, the adopted channel in these literatures is pure additive white Gaussian noise channels or fading channels under other scenarios. To the best of our knowledge, the analysis of communication performance inside tower buildings is rarely studied. This paper intends to fill this research gap.