Sujan Rajbhandari, Zabih Ghassemlooy, Maia Angelova,
Volume 5, Issue 2 (6-2009)
Abstract
Artificial neural network (ANN) has application in communication engineering in diverse areas such as channel equalization, channel modeling, error control code because of its capability of nonlinear processing, adaptability, and parallel processing. On the other hand, wavelet transform (WT) with both the time and the frequency resolution provides the exact representation of signal in both domains. Applying these signal processing tools for channel compensation and noise reduction can provide an enhanced performance compared to the traditional tools. In this paper, the slot error rate (SER) performance of digital pulse interval modulation (DPIM) in diffuse indoor optical wireless (OW) links subjected to the artificial light interference (ALI) is reported with new receiver structure based on the discrete WT (DWT) and ANN. Simulation results show that the DWT-ANN based receiver is very effective in reducing the effect of multipath induced inter-symbol interference (ISI) and ALI.
F. Dabbagh Kashani, M. R. Hedayati Rad, E. Kazemian, A. Kahrizi, M. R. Mahzoun,
Volume 10, Issue 1 (3-2014)
Abstract
In this paper, we investigate the effects of auto-tracking subsystem together with different beam divergences on SNR, BER and stability of FSO communication links. For this purpose we compute the values of power, SNR and BER on receiver, based on analytic formula of Gaussian beam on receiver plane. In this computation the atmospheric effects including absorption, scattering and turbulence are considered. Using mentioned computed values, the laser link stability and its reliability in presence of auto-tracking subsystems are evaluated. The results of simulation and computation are shown with the help of figures and tables.
Reza Bayat Rizi, Amir R. Forouzan, Farshad Miramirkhani, Mohamad F. Sabahi,
Volume 20, Issue 4 (11-2024)
Abstract
Visible Light Communication, a key optical wireless technology, offers reliable, high-bandwidth, and secure communication, making it a promising soloution for a variety of applications. Despite its many advantages, optical wireless communication faces challenges in medical environments due to fluctuating signal strength caused by patient movement. Smart transmitter structures can improve system performance by adjusting system parameters to the fluctuating channel conditions. The purpose of this research is to examine how adaptive modulation performs in a medical body sensor network system that uses visible light communication. The analysis focuses on various medical situations and investigates machine learning algorithms. The study compares adaptive modulation based on supervised learning with that based on reinforcement learning. The findings indicate that both approaches greatly improve spectral efficiency, emphasizing the significance of implementing link adaptation in visible light communication-based medical body sensor networks. The use of the Q-learning algorithm in adaptive modulation enables real-time training and enables the system to adjust to the changing environment without any prior knowledge about the environment. A remarkable improvement is observed for photodetectors on the shoulder and wrist since they experience more DC gain.
