# Channel Modeling for In-Body Optical Wireless Communications +
## 3 Results
### Verification  
In this subsection, we verify the validity of the extracted analytical expressions presented in Section 2 by comparing them with experimentally verified data from published works. In the following figures, the analytical expressions and the experimental results are depicted as continuous lines and geometric symbols, respectively. Starting with Figure 2a, experimental data for the absorption coefficient of each constituent are plotted against the analytical results extracted from (6) through (9). In particular, the experimental data for the oxygenated and de-oxygenated blood, water, melanin, and fat are represented by square, circle, star, triangle, and cross symbols, respectively, while the corresponding analytical expressions are drawn in black, red, green, blue, and purple colors. The validity of the proposed framework is proven based on the fact that the analytical and experimental results coincide. Moreover, the absorption coefficient of blood is not only higher than the rest of the constituents, between 400 and 600 nm, but is also among the most influential constituents regardless of the wavelength. Therefore, even with relatively low volume fraction, blood plays an important role in the total absorption coefficient of any generic tissue. Furthermore, it is evident that, as \(\lambda\) increases, the absorption coefficient increases as well, which highlights the importance of carefully selecting the transmission wavelength for tissues that are rich in water. In addition, the absorption of melanin is among the highest between the generic tissue constituents, as well as the most consistent. This illustrates the significance of the concentration of melanin in the tissue under investigation and, at the same time, the negligible effect of the transmission wavelength on the absorption due to melanin. Lastly, the absorption coefficient of fat has a somewhat stable impact throughout the visible spectrum due to the fact that it receives values around 1 cm\({}^{-1}\), with very small variations. It should be highlighted that the volume fraction of any of the constituents plays a very important role in the final form of the absorption coefficient. For instance, if a tissue has a high concentration in water, the impact of its absorption coefficient after it is multiplied by the water volume fraction can affect the total absorption coefficient significantly, even if the absorption coefficient of water seems insignificant on its own. On the other hand, the impact of a constituent with high absorption coefficient, such as melanin, can be diminished if it has a low volume fraction. Thus, although the absorption coefficients presented in Figure 2a play an important role in determining which of them affect the total absorption coefficient of the generic tissue, it is not an absolute metric. The rest of this subsection illustrates the performance of the proposed mathematical framework by comparing the estimation of the absorption and scattering coefficients of complex human tissues with experimental results from the open literature. Next, Figure 2b presents the absorption coefficients of complex tissues, such as skin, bone, brain, and breast, as a function of the transmission wavelength. The parameters that characterise the constitution of each tissue are provided in Table 3, alongside their sources. From Figure 2b, it can be observed that the analytical expression for the total absorption coefficient provide a very close fit to the experimental data, which not only verifies the extracted expressions but also complements the accuracy of the presented mathematical framework in describing the optical absorption in generic tissues. Moreover, the absorption coefficient of the skin has the most linear behavior out of all of the plotted tissues. This happens due to the increased concentration of melanin in the skin, which leads to increased absorption in higher wavelengths.