Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air-sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of a SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the proposed algorithm achieves better EE than baselines.
翻译:将空间和航空网络与地面系统整合起来的非地球网络(NTN)是新兴第六代(6G)无线网络中的一个关键领域,作为6G的一部分,NTN必须提供广泛的各种设备,包括智能手机、车辆、传感器、机器人和海运用户的无线连接,然而,由于NTN的高流动性和部署,管理空间-空气(SAS)NTN资源,即能源、电力和频道分配,这是一个重大挑战。在本研究中调查了节能资源分配的SAS-NTN设计。目标是通过协作优化用户设备(UE)关联、电力控制、无人驾驶航空飞行器(UAVAV)的部署,尽可能减少UAAAA(引导和传输)拟议UAVS(建议)的总能源成本,同时满足EE的要求。混合-内部流程的不线性流程程序设计问题,然后开发一个持续变换式的等效的算法,然后用BRUA(自动变换式) 将A(自动变换为持续变式的版本,然后将ADUA(自动变式) 自动变换成B。