# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 3 Novel Rate Adaptation Method
### Proposed Novel Method for Data Streams Allocation
However, for \(\alpha~{}=~{}1\), the channel parameters were determined based on the empirical values that consider both the large-scale fading and the small-scale fading, which may adversely affect the obtained system loss prediction. In the next step, the difference, \(\Delta F\), of the instantaneous values of RSSI/TP and system loss given by (6) was calculated. Then, the variance \(\sigma^{2}\) of the normal distribution, \(N(0,\sigma^{2})\), of \(\Delta F\) was determined. The variance was calculated using the unbiased maximum likelihood estimator (MLE) [41], which can be expressed as follows:  
\[\sigma^{2}\ =\ \frac{1}{n-1}\sum_{i=1}^{n}\left(\Delta F_{i}-\overline{\Delta F }\ \right)^{2}\text{, for }n\ =\ 100\text{,} \tag{7}\]  
where \(\overline{\Delta F}\) is the mean of the obtained \(\Delta F\) values. Depending on the estimated conditions of the direct visibility, the data were grouped and the described methodology was applied to them, independently of each other. Consequently, the algorithm quickly reacts to the actual change of these conditions and adapts to them. The low-pass filtration before the data stream allocation block limits the impact of the small-scale fading on the system loss prediction, quickly reacting at the same time to the systematic changes of the attenuation, e.g., by the LOS/NLOS transition. The last element of the AMASD method is the data stream allocation block, which is based on the number of current and previous data bytes from data sources, and on the information about predicted radio channel parameters. It selects the bitrate for the RIs and allocates data for transmission in another time slot in accordance to the designated noise characteristics of both RIs for a PER at a level of \(10^{-3}\). For example, these characteristics can be approximated by the linear functions, i.e., the received power as a function of bitrate, then, the bitrate, \(R\), can be determined as follows:  
\[R\ =\ \frac{(P_{TR}-LPR)-b_{NC}}{a_{NC}} \tag{8}\]  
where \(P_{TR}\) is the power of the transmitted signal, \(LPR\) is the predicted value of the system loss, \(a_{NC}\) is the slope of the noise characteristic function, and \(b_{NC}\) is the intercept term of this function. If the noise characteristic is discontinuous, the obtained \(R_{NC}\) value calculated from (8), wherein \(a_{NC}\ =\ 1\), is compared with the values of this function and the maximum bitrate for the next transmission, satisfying the \(R\ <R_{NC}\) condition, is selected. The determined value of the bitrate (\(R\)) must be within a specified range, \([R_{\min};\ R_{\max}]\), depending on the given RI characteristic. In summary, the proposed AMASD method uses global information about the distance between the nodes and the LOS/NLOS conditions in the RIs. The RDM's are used to estimate the user's movement parameters and, based on the results, the adaptive low-pass filtration of the received signal power estimators from RIs and the LOS/NLOS conditions parameter values is performed. The obtained values are analyzed in the radio channel parameters estimation and prediction block, where the system loss exponent is determined dynamically and separately for the LOS/NLOS conditions. The predicted system loss is then sent to the data stream allocation block, where the bitrate of the next transmission is determined based on the noise characteristics of each RI.