We present a lightwave power transfer-enabled cooperative non-orthogonal multiple access aided uplink underwater optical wireless communication system. In this system, several practical assumptions including misalignment at the relay are considered. We optimize the instantaneous time-splitting parameter, such that the uplink achievable rate is maximized. Specifically, the optimum time-splitting parameter for the full-duplex (FD) relaying mode is derived and compared with the half-duplex mode. Further, two schemes are presented to compensate the rate decrement due to the relay misalignment. Numerical results show that the instantaneous rate optimization, FD operation, and misalignment compensation schemes deliver a superior performance.

In this article, we consider a simultaneous lightwave information and power transfer-enabled indoor visible light/radio frequency (RF) hybrid communication system. In this system, several luminaries are mounted on the ceiling of a room and sensors receive lightwave power and information from luminaries. Each sensor harvests energy from lightwave signals and uses them for uplink communication using RF signals. In general, blockages due to the movement of humans are common in indoor systems which results in severe performance degradation. We consider blockages due to such human movements in our system. An optimization problem is formulated to maximize the uplink weighted sum rate considering uplink orthogonal multiple access (OMA) and non-OMA cases. To find the optimal beamforming matrix and time allocation parameters, a lightweight artificial neural network architecture is proposed. Our solution is capable of predicting the human blockages and accordingly optimizing the beamforming matrices and time allocation parameters leading to a near-optimal uplink sum rate. Specifically, up to 30% rate improvement is observed compared to zero-forcing beamforming when the number of blockages is more than five. Further, the use of NOMA for uplink results in up to 25% sum rate improvement.

In this paper, we consider a cooperative non-orthogonal multiple access (NOMA) aided underwater optical wireless system in which the source transmits to two users where the near user serves as a relay node to the far user. Our proposed system consists of multiple narrow-angle light-emitting diode (LED)/photodiode (PD) elements at the source, near user, and far user. In order to achieve communication, our system selects a single LED/PD at each node. We propose several low complexity LED/PD selection schemes that aim to maximize the link throughput and in addition consider optimal and random LED/PD selection for benchmarking. In order to characterize the performance of each scheme, bounds and closed-form tight approximations on the average achievable sum rates are presented. The use of multi element nodes and NOMA increase the average sum rate significantly over conventional orthogonal access. Moreover, near-optimal throughput can be achieved using channel gain based and line-of-sight based LED/PD selection schemes in the medium-to-high transmit power regimes. The derived expressions are also useful to investigate the impact of key system and channel parameters such as the source transmit power, power allocation factor, node placement, and the number of elements at each node.

We consider a spatial modulation aided indoor visible light communication system with user mobility and random receiver orientation. Two artificial neural networks (ANNs) are proposed which are able to predict the channel state information (CSI) with high accuracy and resolution. These architectures use estimated CSI at pilot instances obtained using least square or minimum mean square error estimation and predict CSI at intermediate locations. Moreover in ANN 2, predicted user position information is used to improve the performance. Numerical results show that the proposed ANNs deliver a better bit error rate compared to a benchmark spline interpolation-based method. Further, ANN 2 is shown to perform robustly in a high mobility scenario.

In this paper, we consider a simultaneous lightwave and power transfer-enabled indoor visible light communication system and aim to investigate how to manage the transmission power from multiple transmitters to provide both information and energy harvesting. We formulate three different optimization problems, all aiming to minimize the total average transmit power at the luminaries, assuming different performance constraints, such as data rate, energy harvest, and illumination requirements. The first problem aims to find the optimal beamforming matrix and the transmit powers at light emitting diodes (LEDs), while the second problem aims to use zero-forcing beamforming and finds the optimal transmit powers. Finally, the third problem aims to find the minimum number of LEDs required to satisfy the given constraints. Relying on a Machine Learning approach, our solution is capable of predicting the user mobility patterns, and receiver orientation angles and accordingly optimizing parameters leading to a near-optimal result under different blockage conditions with low computational complexity. Moreover, a comparison with other approaches shows the effectiveness of the proposed solution in terms of significantly reducing the transmit power in a wide range of orientation errors. Specifically, up to 50% of the average transmit power can be minimized using the presented approach.