## 4 The WBANs Communication Stack
### Mobility and Radio Channel Modeling for WBANs
#### 4.1.2 State-of-the-Art on WBAN Channel Models  
The body area channel is very different from other wireless channels in the sense that the antenna and human body is an integral part of the channel so that the directional channel modeling approaches (such as [36]) which are getting more popular in multi-antenna and ultra wideband systems cannot be applicable. However, latest work such as [37], which is explained in this section, exploited a small multi-antenna based WBAN system and has shown that channel capacity and the performance can significantly improve with multi-antenna systems in WBANs. The following section presents the latest work on WBAN channel modeling. \begin{table}
\begin{tabular}{c c} \hline \hline
**Small-Scale Fading** & \(\textit{K}_{\text{dB}}\;=\textit{K}_{\text{0}}\;-\;\textit{m}_{\text{K}}\, \textit{P}_{\text{dB}}\;+\;\sigma_{\text{K}}\,\textit{n}_{\text{K}}\) \\ \hline \(K_{0}\;[dB]\) & 30.6 \\ \(m_{\text{K}}\;[dB]\) & 0.43 \\ \(\sigma_{\text{K}}\;[dB]\) & 3.4 \\ \multicolumn{3}{c}{Parameters of the mean value of the delay spread} \\ Distance [cm] & t\({}_{\text{rms}}\) [ns] \\
15 & 6 \\
45 & 16 \\ \multicolumn{3}{c}{Parameters of the 90\% cumulative value of the delay spread} \\ Distance [cm] & t\({}_{\text{rms}}\) [ns] \\
15 & 11 \\
45 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Small-scale fading using Ricean distribution [31]. It is important in radio channel modeling for WBANs to take into account human mobility for a realistic representation of the propagation phenomena. Biomechanical modeling clearly plays a central role in WBAN mobility assessment. Modeling the human body motion during pedestrian motion -on its own- is a pretty old topic, already investigated in computer graphics and robotics. It is however still hardly covered in the context of on-body radio communications. In particular, the relationship between body mobility and physical radio abstractions is a rather recent field of research. In [38] for instance, a cylinder-based biomechanical model is proposed to generate realistic mobility patterns as a function of time under typical pedestrian mobility. The model provides the true dynamic positions of on-body nodes, along with the corresponding obstruction conditions per link for arbitrary deployment scenarios. This model has been coupled further with macroscopic pedestrian mobility models (e.g., applying a random Gauss-Markov model to the body centroid) to evaluate the performance of localization algorithms. A dynamic channel model based on extensive measurement campaign with different human subjects is proposed in [39]. The channel model is based on a scenario-based approach, where a scenario represents a specific TX and RX antenna location on the body, an environment, and a human body mobility condition. The mean path loss was expressed as a function of the scenario and not as a unique model as a function of antenna separation. This model was proven to be closer to reality that a distance-dependent model. The model allows taking into account the variation for different bodies, highlighting the existence of two main phenomena in BAN channel. First, the slow fading variation which strongly depends on the antenna locations and movement, and represents the masking effect of the human body itself. Second, the fast fading which take into account the multi-path effect in indoor environment [39]. An experimental study on how the signal properties vary as the person moves is presented in [40]. In this work sideways arm movement and marching on the spot movements are considered. The results obtained were used to derive a model for the behavior of the Body Coupled Channel (BCC) under moving body conditions. It was found that different body movements result in significantly different BCC channel behaviors. The type and speed of movement was found to affect the channel properties. It was concluded that the sideways arm movement is accurately modeled by a Lognormal distribution whilst the marching-on-the-spot movement is modeled by a Generalized Extreme Value distribution.