RESUMEN
In this paper, we develop an artificial neural network (ANN)-based algorithm for signal classification, i.e., data demodulation, and the estimation of channel characteristics, such as channel DC gain and turbulence distribution parameters, in free-space ultraviolet (UV) communication systems. In this scheme, the ANN-based receiver adaptively tracks the UV channel variation and is directly trained using the data generated from UV channel models. To evaluate the performance of the proposed algorithm across all turbulence regimes, log-normal, Gamma-Gamma, and negative exponentially distributed UV turbulence channels are considered. We demonstrate that the proposed algorithm is robust to perform accurate and reliable signal classification and UV channel estimation without knowing underlying UV channel models or channel state information (CSI). In addition, time complexity analysis is presented. It is shown that, even under the strong turbulence regime, the accuracy of the proposed algorithm is found to be higher than 97% in terms of correct classification. It is also demonstrated that the classification error rate performance of the proposed ANN-based detector is superior to that of a maximum-likelihood (ML)-based detector with perfect CSI.
RESUMEN
In this paper, a patient mobility support scheme for indoor non-directed optical body area networks (OBAN) is presented. The OBAN is an optical healthcare system where medical sensors are installed on various parts of the patient's body and are connected to an optical coordinator for transmitting the physiological signals via optical wireless links. In the proposed scheme, a white light-emitting diode (LED) was employed as the optical coordinator that was mounted on the patient body, while a photodetector (PD) was used as the receiver installed at the ceiling. We considered three practical mobility scenarios in terms of the location of the coordinator: (i) Shoulder, (ii) wrist, and (iii) both shoulder and wrist. The analytical channel model for multiple reflections in a non-directed OBAN was developed and validated in the form of simulations. In addition, experiments were carried out to verify the effectiveness of the proposed mobility scheme. It was found that the third scenario (shoulder and wrist) performed best, showing a bit error rate (BER) of 1.2 × 10-6 at a distance of 1.25 m. The experimental results demonstrated that the proposed mobility support scheme in the OBAN added an additional degree of freedom to patients with reliable performances.
