Recently, visible light communication (VLC) based on LEDs has attracted much attention. In order to realize multiusers indoor VLC systems, a reasonable channel model considering random shadowing, which is first proposed to the best of our knowledge, is significant. Taking the nonquantitative obstructions with random movement into account, by introducing the Poisson process and the probability density function, we propose a weighting function and set up a channel model. The expressions of the received optical power, the horizontal illuminance and the impulse responses are given. The verification of the model's feasibility is presented through simulation. Moreover, based on the proposed channel model, the simulation analysis of the effect of random shadowing on communication performance is conducted, and the corresponding simulation optimization is also made.

This paper investigates a downlink power allocation scheme for application of non-orthogonal multiple access (NOMA) in visible light communication (VLC) systems. Aiming to achieve a flexible tradeoff between sum rate and user fairness depending on administrators’ subjective setting and acquire the optimal solution for any setting, we formulate two optimization problems, which are closely aligned with the practical application. Specifically, one is user fairness-guaranteed sum rate maximization, the other is sum rate-guaranteed user fairness maximization. Moreover, through classified discussion around the threshold guaranteeing user fairness or sum rate, we present rigorous mathematical derivation and optimization analysis, and two corresponding algorithms are proposed. Furthermore, through numerical simulation, the superiority of our proposed algorithms over conventional schemes is verified in terms of meeting the practical demand and performance gain in sum rate, user fairness, and coverage probability.

Recently, due to its higher spectral efficiency and enhanced user experience, non-orthogonal multiple access (NOMA) has been widely studied in visible light communication (VLC) systems. As a main concern in NOMA-VLC systems, the power allocation scheme greatly affects the tradeoff between the total achievable data rate and user fairness. In this context, our main aim in this work was to find a more balanced power allocation scheme. To this end, an adaptive power allocation scheme based on multi-attribute decision making (MADM), which flexibly chooses between conventional power allocation or inverse power allocation (IPA) and the optimal power allocation factor, has been proposed. The concept of IPA is put forward for the first time and proves to be beneficial to achieving a higher total achievable data rate at the cost of user fairness. Moreover, considering users’ mobility along certain trajectories, we derived a fitting model of the optimal power allocation factor. The feasibility of the proposed adaptive scheme was verified through simulation and the fitting model was approximated to be the sum of three Gaussian functions.

With the continuous development of multi-gateway multi-beam satellite communication technologies, the configurable frequency and beam resources in gateways and satellites are increasing rapidly. Thus, how to efficiently allocate satellite frequency bands to different beams has become a matter of great concern. This paper addresses the key issues in frequency plan design (FPD) for practical geosynchronous earth orbit (GEO) high-throughput multi-gateway multi-beam satellite systems, particularly focusing on the challenges of bandwidth allocation and the constraints of co-frequency interference. Initial attempts with classic heuristic algorithms showed inadequate performance in terms of resource utilization and co-frequency interference management. To overcome these challenges, we propose a two-layer heuristic information-driven simulated annealing (THISA) algorithm, which combines beam spectrum allocation (BSA) in the first layer and interference bandwidth minimization (IBM) in the second layer to tackle the constrained FPD problem. Numerical simulation results demonstrate that THISA significantly reduces interference bandwidth and enhances resource utilization efficiency. Under the provided datasets, THISA achieves 100% frequency spectrum utilization, outperforming classic heuristic algorithms. The proposed THISA algorithm has been successfully applied in practical multi-gateway multi-beam satellite systems.

Recently, the demand for high data traffic transmission is experiencing unstoppable growth, and multi-beam high-throughput satellite (HTS) systems have proven to be effective solutions. However, how to manage and eliminate the co-channel interference caused by frequency reuse in multi-beam HTS systems is still a challenging issue. Motivated by this background, this paper begins with discussing the interference mechanism in the uplink and downlink of HTS systems and then uses the quantification method of carrier-to-interference ratio (CIR), whereby the influencing factors of the CIR are accordingly analyzed. In addition, the common CIR selection strategies are summarized and compared with each other in terms of their advantages and disadvantages. Based on the proposed CIR quantification and statistical selection methods, two frequency plan cases are simulated for the scenario of 19 beams with 4-color reuse. The results show that the proposed quantification and selection methods are rather practical and offer a feasible approach for frequency plan adjustment based on CIR optimization. Finally, a simulation analysis of the CIR under different power spectral density (PSD) values is conducted, and the results show that the PSD has a substantial impact on the CIR.

With the continuous advancement of onboard digital payload technology, high-throughput satellites (HTSs) now possess the capability to dynamically reconfigure their onboard resources in response to user traffic demands. This flexibility is primarily enabled by the efficient design of the satellite’s frequency plan and the beam power allocation scheme. However, the joint optimization of frequency planning and power allocation is a typical mixed-integer nonlinear programming (MINLP) problem, which can not be directly solved using traditional optimization methods. To address this issue, we propose a two-stage optimization framework, aimed at bridging the gap between the beam capacity and the user traffic request, while simultaneously avoiding co-channel interference among different beams. In the first stage, we propose a multi-agent deep reinforcement learning framework based on proximal policy optimization. By modeling each beam as an agent, each beam can select its reuse group, starting frequency, bandwidth, and transmit power based on the current system state. To mitigate the impact of neural network randomness on the results, we further introduce a second-stage optimization algorithm using the difference of convex (DC) programming to refine the beam power allocation. Simulation results demonstrate that the proposed algorithm significantly reduces execution time compared to benchmark algorithms, such as genetic algorithms and particle swarm optimization, while also demonstrating considerable performance improvements.