Unmanned aerial vehicle (UAV) has recently attracted a lot of attention as a candidate to meet the 6G ubiquitousconnectivity demand and boost the resiliency of terrestrialnetworks. Thanks to the high spectral efficiency and low latency, non-orthogonal multiple access (NOMA) is a potential access technique for future communication networks. In this paper, we propose to use the UAV as a moving base station (BS) to serve multiple users using NOMA and jointly solve for the 3D-UAV placement and resource allocation problem. Since the corresponding optimization problem is non-convex, we rely on the recent advances in artificial intelligence (AI) and propose AdaptSky, a deep reinforcement learning (DRL)-based framework, to efficiently solve it. To the best of our knowledge, AdaptSky is the first framework that optimizes NOMA power allocation jointly with 3D-UAV placement using both sub-6GHz and millimeter wave mmWave spectrum. Furthermore, for the first time in NOMA-UAV networks, AdaptSky integrates the dueling network (DN) architecture to the DRL technique to improve its learning capabilities. Our findings show that AdaptSky does not only exhibit a fast-adapting learning and outperform the state-of-the-art baseline approach in data rate and fairness, but also it generalizes very well.
翻译:无人驾驶航空飞行器(UAV)最近作为候选者吸引了大量关注,以满足6G无处不在的连通需求,并提升地面网络的恢复能力。由于光谱效率高且低延度,非横向多存(NOMA)是未来通信网络的潜在接入技术。在本文中,我们提议使用UAV作为移动基地站(BS)为多个用户提供服务,使用NOMA(NOMA)和3D-UAV(3D-UAV)定位和资源分配问题共同解决。由于相应的优化问题是非康维x,我们依靠人造情报(AI)的最新进展,并提议采用基于深加固(DRL)的架构,以有效解决这一问题。根据我们的知识,DASTSky是第一个将NOMA的电力配置与3D-UAVAV(3D-UAV)配置联合优化,同时使用5-6GHz和毫米波波频谱。此外,在NOMA-UAV网络中首次将相应网络(DN)整合,但不包含相应网络(DN)结构的公平性结构,我们在快速学习率方法中,也显示其快速学习能力的S-RDR-L(S-lad-lad-lad-lad-lax)方法。