# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 1 Introduction  
The aspiration to share information in real-time between co-located body area networks has led to the creation of body-to-body wireless networks (BBWNs). In such a network, wireless devices worn by one person will transmit information wirelessly to a device worn by another person. This kind of communication will find applications in a range of areas such as team sports, emergency services, military as well as other social networking experiences [1]. As in on-body and off-body communications, BBWNs are subject to time-varying body movement and shadowing effects at both ends of the link as all transceivers will be attached to the user in one way or the other. To ensure reliable communication in such time-variant channel conditions, robust hardware and correct decision making tools throughout the protocol stack should be carefully engineered. Such engineering can only be achieved with a greater understanding of the communications channel [2].  
An empirical study on the signal characteristics of outdoor body-to-body communication channels was conducted in [3] to assess the impact of typical human body movements. The movements taken into consideration are rotation, tilt, walking in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. A similar study in an indoor environment was conducted in [4] and it highlighted how a specific movement resulted in different effects on the channel dynamic properties. The study in [5] takes it further, by comparing deterministic and semi-deterministic approaches in the simulation of on-body and body-to-body networks, a semi-deterministic approach was found to be the bestoption. A more specific study was conducted in [6] to analyze body-to-body communications channels susceptible to shadowed fading. The statistical model proposed in [6] showed an improved fit to the signal fading compared to established models such as Lognormal, Nakagami and Rice. Unlike [6], the work in [2] focused on LOS cases in different indoor environments and showed that the communication channels have considerable variability depending on the local propagation conditions. To mitigate human body shadowing in outdoor body-to-body communication, diversity combining schemes were investigated in [7] and have shown some promising results.  
While the studies performed in [2; 3; 4; 5; 6; 7] cover everyday activities, there has not been significant research into more specific applications such as sport activities. In this paper, for the first time, the body-to-body communication channel characteristics, particularly during running and cycling in an outdoor environment, are investigated. Naturally, these kinds of activities provide their own unique set of channel dynamics due to the movements and posture changes at both ends of the link. This paper presents statistical channel characteristics of two different running and cycling scenarios at the 2.4 GHz industrial, scientific, and medical (ISM) band that is utilized in several standards, which are suitable for short range BBWNs. Due to the imperfection of utilizing single standard distributions in modeling BBWNs channels, [4; 8], we investigate the application of the two-state semi-Markov model (TSSMM) in the characterization of such channels. A simulation model based on TSSMM is developed and validated using the measurement data. The measurement results could also provide physical layer parameters for network simulators such as those developed in [5]. Furthermore, we analyze the performance of the investigated scenarios in terms of channel capacity and outage probability and present the results.  
The rest of the paper is organized as follows: Section 2 describes the measurement campaign, presenting the body worn transceivers used and the investigated scenarios. Experimental data analysis are discussed in Section 3, followed by simulation model and its validation in Section 4. Performance analysis is presented in Section 5, and Section 6 concludes the paper.