基于NOMA的无人机辅助可见光通信速率最大化：群体智能与机器学习的结合
Sum-Rate Maximization for UAV-assisted Visible Light Communications using NOMA: Swarm Intelligence meets Machine Learning
摘要：
由于将无人机（UAV）集成到可见光通信（VLC）中可以为5G及更高级别的无线连接应用和服务提供许多好处，因此这项工作考虑使用非正交多址的UAV辅助VLC。更具体地说，我们制定了一个联合问题的功率分配和无人机的位置，以最大限度地提高所有用户的总和速率，受功率分配，服务质量的用户，和无人机的位置的约束。由于该问题是非凸的，一般是NP-困难的，很难得到最优解。此外，该问题不容易通过常规方法来解决，例如，坐标下降算法，由于VLC中的信道建模。因此，我们建议使用哈里斯霍克优化（HHO）算法来解决制定的问题，并获得一个有效的解决方案。然后，我们使用HHO算法与人工神经网络，提出了一种设计，可以用于实时应用程序，避免陷入“局部极小”陷阱，在传统的教练。数值结果验证了所提出的算法的有效性，并进一步表明，所提出的算法/HHO训练器是优于几个替代方案和现有的元启发式算法。
英文摘要：
As the integration of unmanned aerial vehicles (UAVs) into visible light communications (VLC) can offer many benefits for massive-connectivity applications and services in 5G and beyond, this work considers a UAV-assisted VLC using non-orthogonal 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 non-convex 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 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 which 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.
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