# Channel Modeling for In-Body Optical Wireless Communications +
## 4 Future Research Directions  
The objective of this work was to build the basic pillar towards an accurate, tractable, easily-used, and general channel model for an optical wireless in-body and transdermal channel. To achieve this, as a first step, the basic propagation mechanisms in such a medium, namely (i) spreading loss and (ii) scattering, were identified. Next, low-complexity models that account the propagation medium particularities, i.e., the different tissue compositions, were introduced. The aforementioned models can open the door to a number of important studies, ranging from ray-tracing-based channel modeling to theoretical assessment of point-to-point to point-to-multi-point links and systems, as well as systems, transmission, and reception schemes and policies designed to optimize. In more detail, the following future research directions can be identified:  
* Stochastic channel modeling: As discussed in [2], transdermal and in-body optical wireless links suffer from wavelength-dependent particulate scattering. Within the skin, the main source of scattering is filamentous proteins (e.g., keratin in epidermis and collagen in dermis). Note that, since these particles are comparable to or larger than the wavelength, scattering can be approximated as a Mie solution to Maxwell's equations. On the other hand, in in-body applications, tissues, such as membranes, striations in collagen fibrils, macromolecules, lysosomes, vesicles, mitochondria, and nuclei are the main scatterers. Note that membranes are usually lower than 1/10 of the wavelength, while all the other structures are comparable to the wavelength. Thus, scattering in tissues can be modeled as a mixture of Rayleigh and Mie processes. By taking into consideration the inhomogeneities in the body content in light-absorbing and scattering, which lead to a variation of the reflective index along the transmission path, it becomes evident that the received power is expected to randomly fluctuate. To model this phenomenon, experimentally verified ray-tracing investigations that capitalize the models presented in the current contribution need to be conducted. Furthermore, except for line-of-sight links, non-line-of-sights scenarios, where diffusion may be the key player, also need to be investigated. * Theoretical investigation: Several different use cases, such as cochlear, gastric, cortical, retinal, foot drop implants, etc., have been identified [2]. However, only for a small number of them, a link budget analysis that supports their feasibility and reveals the architectural requirements that need to be accounted for has been conducted [6, 7]. This motivates the use of the presented contribution as a building block towards the performance assessment of existing and envisioned designs and system models, as well as networks. In particular, for low-distance links, in the orders of some cm, where the strength of line-of-sight components are expected to be considerably larger than the one of non-line-of-sight, due to the high directionality of the links, the channel variation from its expected values will be relatively low. As a result, the presented channel model will provide a very accurate estimation of the received signal strength. * Design and development of communication, energy harvesting, and neural stimulation modules: In order to select the optimal transmission and reception parameters and equipment, design energy and spectral efficient transmission waveforms, and reception filters and processes, develop low-complexity channel and error correction codes, devise suitable energy harvesting modules, and utilize energy transfer and harvesting policies, a low-complexity channel model that captures the inherent characteristics of the propagation medium is required. In more detail, and as illustrated in Section 3, the presented channel model can aid in identifying optimal transmission wavelengths, as well as transmission windows for different applications.