## 4 The WBANs Communication Stack
### Mobility and Radio Channel Modeling for WBANs
#### 4.1.2 State-of-the-Art on WBAN Channel Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Scenarios** & **Path Loss** & **Propagation Effects** & **Link Conditions** & **Link Type** & **Mobility** \\ \hline Simulations-based space-time dependent channel model [38] & Intra-WBAN. & Combination of frequency, Indoor or Anechoic & Spatial and temporal characteristics based & & Hip to & \\ channel model [38] & Intra-WBAN. & of frequency, Indoor or Anechoic & Stationary features in free space & & & \\ channel model [39] & Chamber & and around the body & & & & \\ \hline Measurement-based time-varying model [39] & Intra-WBAN. & Time-frequency and scenario-based & Slow and fast fading & Hip to & & \\ based time-varying model & Anechoic & and scenario-based & Shadowing & LOS/NLOS & & \\ correlation between & & wrist/ right & & & \\ links. & & & & & \\ foot. _etc._ & & & & & \\ \hline Measurement & Intra-WBAN. & Distance and indoor or periodic & Slow and fast fading & & & \\ characteristics-based model & anechoic & periodic & along with Periodic & LOS and & & \\ based model [41] & anechoic & function & Correlation & & & \\ \hline Simulation-based On and & & Geometrical- & & & \\ Off Body Multi & Intra-WBAN. & based & Multipath cluster of & & \\ antenna-channel model [37] & Indoor & statistical & scatters & LOS/NLOS & & \\ model [37] & & model & & & \\ \hline IEEE proposed & Intra-WBAN. & Distance- & No spatial or temporal features & & & \\ models [31] & indoor or anechoic & based & considered. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of existing channel models. capacity gain of 0.97, almost reaching the upper MIMO capacity bound. Concerning off-body communications, an average capacity gain of 0.84 was achieved. After explaining the existing state-of-the art on WBANs channel models a comparative study is summarized as follows. Different parameters are considered to evaluate various models. This includes scenarios and environment, path loss, propagation effects, mobility, link conditions and its types. IEEE 802.15.6 proposed channel models provides the basic distance-based path loss static models without any time varying effects and correlations features. Concerning time-varying modeling aspects and characteristics, the authors of [38, 39] exploited spatial and temporal variations under dynamic environment and hence realized more accurate model. Periodic make-and-break links between moving parts of the body, such as to-and-fro motions of the arms in walking conditions required periodic time varying channel model. In [41] the authors proposed a RSSI-based path loss model with an addition of periodic characteristics on top of IEEE proposed channel models. It also provides a generic method to rapidly develop different path loss expression depending upon the specific user mobility and activity. To conclude on the channel modeling from the presented state-of-the-art, it can be noticed that most of the models are developed with medical health-care applications in mind, therefore, the indoor environment is an obvious choice, however with regards to rescue and critical operations the environment can be outdoors as well and therefore, would require more realistic and new measurement campaigns. Further, as shown in Table 7, the impact of Body-to-Body (or Inter-WBANs) mobility and signal propagation variations for given scenarios are required to investigate for the future development of the application-specific accurate channel modeling.