Details

Autor(en) / Beteiligte

• Shao, Hongxiang
• Sun, Youming
• Ji, Baofeng
• Shi, Nianfeng
• Luo, Haifeng
• Du, Tao

Titel

Resource Allocation for Tethered UAVs Aided NOMA Networks: A Location-Aware Air-Ground Collaborative Perspective

Ist Teil von

• IEEE access, 2023, Vol.11, p.134794-134803

Ort / Verlag

Piscataway: IEEE

Erscheinungsjahr

2023

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• The research of the mutli-UAV assisted NOMA networks has received a lot of attention because of its superior ability to improve spectrum efficiency and high maneuverability simultaneously. In this research, we take into account downlink NOMA networks with multiple tethered UAV assistance, and investigate the joint UAVs location, user scheduling, user pairing and power distribution problem. The design aims to maximize the sum-rate that can be achieved with a minimum rate restriction, as a complex problem involving mixed-integer programming. First, we determine the best user pairing and power distribution methods for established UAVs positions, which gives the closed-form solution for parameters. Afterwards, we formulate the UAV position optimization problem as a local altruistic game from the viewpoint of air-ground cooperation on the bias of interference graphs. It has been shown to be an exact potential game that permits more than one pure approach Nash equilibrium (PNE). A centralized-distributed iterative learning method is proposed to reach the ideal PNE as rapidly as possible, maximizing the specified network utility measure. The proposed algorithm performs better than the current techniques, according to simulation findings, and greatly boosts network utility. Simulation results show that, nearly 5% and 26% networks utility can be enhanced by the proposed method compared with "head-pairs-tail" and random schemes respectively.