Sum-Rate Maximization for UAV-Assisted Visible Light Communications Using NOMA: Swarm Intelligence Meets Machine Learning

• Published in: IEEE internet of things journal Vol. 7; no. 10; pp. 10375 - 10387
• Main Authors: Pham, Quoc-Viet, Huynh-The, Thien, Alazab, Mamoun, Zhao, Jun, Hwang, Won-Joo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-10-2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 5G mobile communication; Algorithms; Artificial neural network (ANN); Artificial neural networks; Harris hawks optimization (HHO); Heuristic methods; Light emitting diodes; Machine learning; NOMA; Nonorthogonal multiple access; nonorthogonal multiple access (NOMA); Optimization; Particle swarm optimization; Resource management; sum-rate maximization; Swarm intelligence; Unmanned aerial vehicles; unmanned aerial vehicles (UAVs); visible light communications (VLCs); Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.2988930

As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLCs) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this article considers a UAV-assisted VLC using nonorthogonal multiple-access. More specifically, we formulate a joint problem of power allocation and UAV's placement to maximize the sum rate of all users, subject to constraints on power allocation, quality of service of users, and UAV's position. Since the problem is nonconvex and NP-hard in general, it is difficult to be solved optimally. Moreover, the problem is not easy to be solved by conventional approaches, e.g., coordinate descent algorithms, due to channel modeling in VLC. Therefore, we propose using the Harris hawks optimization (HHO) algorithm to solve the formulated problem and obtain an efficient solution. We then use the HHO algorithm together with artificial neural networks to propose a design that can be used in real-time applications and avoid falling into the "local minima" trap in conventional trainers. Numerical results are provided to verify the effectiveness of the proposed algorithm and further demonstrate that the proposed algorithm/HHO trainer is superior to several alternative schemes and existing metaheuristic algorithms.