Underwater wireless optical communication (UWOC) has generated a much interest in the research community thanks to its wide range of applications, including submarine navigation, seafloor exploration, and military operation. Turbulence, scattering, and absorption phenomena generally can severely affect the performance of UWOC systems. The relaying methods have been shown as a promising technique to alleviate turbulence-induced and short-range fading. In this paper, under varying channel conditions, we present a unified framework to analyze the performance of multi-hop UWOC systems with both amplify-and-forward and decode-and-forward relaying techniques. In particular, for the end-to-end signal-to-noise ratio (SNR), we use the univariate and bivariate Fox-H functions to derive both the probability density function (PDF) and cumulative distribution function (CDF). Furthermore, for the performance of systems under both types of relaying techniques, we derive tight mathematical expressions for the outage probability and bit error probability. Besides, to highlight and provide some prominent engineering insights into the diversity order, an asymptotic analysis for the outage likelihood and average bit error rate (BER) is given. Consequently, results from Monte-Carlo simulations are used to verify the correctness of our derived mathematical expression.

Optical wireless networks, particularly free-space optical (FSO) in outdoor environments and visible light communication (VLC) in indoor environments, suffer from severe deterioration in signal quality due to various factors such as the availability of line-of-sight link, the turbulence of the atmosphere, or pointing errors. To enable high-quality service point-to-point transmission through optical wireless communication, an efficient and reliable backhaul link is essential. Towards this objective, this paper proposes a novel optical backhauled non-orthogonal multiple access (NOMA) in VLC systems. Unlike earlier works, our model considers the effects of various transmission parameters in a mixed cooperative FSO/radio over a fiber backhaul link with NOMA-VLC to serve two users. The closed-form expressions of outage probability, sum throughput, ergodic sum capacity, and average bit error rate are delivered for a variety of channel conditions using Meijer's G and Gaussian hypergeometric functions. More specifically, both the theoretical and simulation results demonstrate that the performance gain of the proposed optical-based NOMA systems can improve the performance of the users in comparison with the conventional OMA systems at medium to high SNR ranges. Furthermore, a deep learning-based framework is proposed to accurately estimate the ergodic capacity of the proposed system and significantly reduce the complexity cost of the theoretical analysis.

In this paper, we propose a technique called joint power allocation and orientation of light-emitting diodes (LEDs) to improve the illuminance quality of an indoor visible light communication (VLC) system. The optimization problem for LEDs, considering power allocation coefficients and normal vectors, is solved through an iterative algorithm. The simulation results with various LED geometries show that the proposed scheme can achieve a lower received power variance and a higher quality factor compared to previous power allocation schemes.

In multiple-input multiple-output (MIMO) visible light communication (VLC) systems, collaborative constellation design (CC) is effective in improving performance while significantly reducing the total optical power. This letter presents the first attempt to exploit deep learning (DL) in the signal design for MIMO VLC systems using CC, namely CCNet. More specifically, we present a deep neural network with fully connected layers to design an optimal constellation which can be derived conventionally at a high computational cost using the convex optimization solving procedure. Moreover, to enhance the performance of the proposed CCNet, instead of employing only the ordinary channel state information (CSI), the input is efficiently preprocessed before entering the network. By employing training samples collected in simulations, CCNet is first trained offline with the objective of minimizing the mean square error (MSE), and the online CC designing process can then be effectively deployed using the trained model. Simulation results show that CCNet can achieve a near-optimal MSE with a remarkably lower running time than the previous CC designing scheme.

In this letter, we propose light-emitting diode (LED) selection that can be applied not only to the conventional multiple-input multiple-output (MIMO) case but also to a larger MIMO configuration of a generalized LED index modulation (GLIM) system with optical orthogonal frequency division multiplexing in visible light communication. Moreover, we derive an implementation of the maximum a posteriori (MAP) detector when the number of LEDs is an even number larger than four. Simulation results show that the performance of MAP and LED selection is better than other detection algorithms for larger even numbers of LEDs and conventional GLIM for 4×4 transmission, respectively.

In this paper, we propose a receiver orientation model for spatial modulation (SM) in a visible light communication (VLC) system with line-of-sight (LOS) characteristics. The proposed technique efficiently mitigates the optical channel correlations across the multiple light-emitting diodes (LEDs), which are utilized in the SM system transmitter by altering the orientations of a photodiode (PD). Specifically, we express the LOS channel coefficients in the form of a PD normal vector and solve the optimization problems to obtain the optimal PD normal vectors. The bit error rate (BER) and channel capacity performances are also presented to validate the advantages of the proposed methods. The results demonstrate that the proposed designs offer a relatively low BER and high channel capacity in various LOS locations.