论文标题
无人机辅助NOMA-VLC系统的聚类和功率分配：群智能方法
Clustering and Power Allocation for UAV-assisted NOMA-VLC Systems: A Swarm Intelligence Approach
论文作者
论文摘要
将无人驾驶汽车（UAV）集成到非正交的多重访问（NOMA）可见光通信（VLC）都暴露了VLC和NOMA-VLC系统的许多潜力。在这种情况下，当用户数量较大时，用户分组对于降低NOMA解码的复杂性至关重要。但是，现有研究尚未考虑此问题。在本文中，我们旨在通过在下行链路NOMA-VLC系统中共同优化无人机放置，用户分组和功率分配来最大化所有用户的加权总数。我们首先考虑有效的用户聚类策略，然后采用群智能方法，即哈里斯·霍克（Harris Hawk）优化（HHO），以解决联合无人机的放置和电力分配问题。仿真结果表明，与四个替代方案相比，所提出的算法的表现要出色：NOMA，NOMA，NOMA-VLC，具有固定无人机放置的Noma-VLC和随机用户聚类。
Integrating unmanned aerial vehicles (UAV) to non-orthogonal multiple access (NOMA) visible light communications (VLC) exposes many potentials over VLC and NOMA-VLC systems. In this circumstance, user grouping is of importance to reduce the NOMA decoding complexity when the number of users is large; however, this issue has not been considered in the existing study. In this paper, we aim to maximize the weighted sum-rate of all the users by jointly optimizing UAV placement, user grouping, and power allocation in downlink NOMA-VLC systems. We first consider an efficient user clustering strategy, then apply a swarm intelligence approach, namely Harris Hawk Optimization (HHO), to solve the joint UAV placement and power allocation problem. Simulation results show outperformance of the proposed algorithm in comparison with four alternatives: OMA, NOMA without pairing, NOMA-VLC with fixed UAV placement, and random user clustering.
