User-Centric UAV Group Power Allocation Algorithm

User-centric unmanned aerial vehicle group (UUAVG) organizes a dynamic user-centric access point group for each user, which can improve user service quality and throughput. However, in UUAVG, unmanned aerial vehicle user equipment (UUE) and device-to-device user equipment (DUE) using the same frequency will cause co-frequency interference and reduce system performance. In response to this problem, a power allocation algorithm of the two-level Stackelbreg game (PAOTLSG) is proposed. The algorithm builds a two-level Stackelberg game model based on UUE and DUE and uses power control coefficients and other parameters to control UAV adjust with the transmit power of DUE to achieve dynamic balance. The optimal transmit power of UAV and DUE is theoretically deduced to achieve the game Nash equilibrium and theoretically prove the existence and uniqueness of the Nash equilibrium solution. The simulation results show that compared with the pricing-based Stackelbreg game (PSG) algorithm, the PAOTLSG algorithm can effectively increase the throughput by 4.65%.

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Power Control Algorithm Based on a Cooperative Game in User-Centric Unmanned Aerial Vehicle Group

Complexity ◽

10.1155/2021/7108198 ◽

2021 ◽

Vol 2021 ◽

pp. 1-6

Author(s):

Yuexia Zhang ◽

Pengfei Zhang

Keyword(s):

Power Control ◽

Unmanned Aerial Vehicle ◽

Cooperative Game ◽

Lagrange Function ◽

Vehicle Group ◽

Entire System ◽

Communication Quality ◽

Aerial Vehicle ◽

User Centric

The quality of service (QoS) of a user in user-centric unmanned aerial vehicle group (UUAVG) is degraded by complex cochannel interference; hence, a cooperative game power control (CGPC) algorithm in UUAVG is proposed. The algorithm helps to establish a downlink power control model of the UUAVG, construct a product of the signal to interference noise ratio function of each user as a utility function of the cooperative game, and deduce the optimal power control scheme using the Lagrange function. This scheme reduces the interference of the service unmanned aerial vehicle (UAV) to edge users and improves the communication quality of all the users as well as the throughput of the entire system. Simulation results show that the average throughput of the CGPC algorithm improved by 10.32% compared with the traditional Stackelberg Game based Nonunified Pricing Power Control (SGNPPC) algorithm. This shows that the CGPC algorithm can effectively reduce the transmission power of the cooperative UAV and enhance the capacity of the entire system, ensuring communication quality.

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A Cauchy mutant pigeon-inspired optimization–based multi-unmanned aerial vehicle path planning method

Measurement and Control ◽

10.1177/0020294019885155 ◽

2020 ◽

Vol 53 (1-2) ◽

pp. 83-92 ◽

Cited By ~ 3

Author(s):

Bo Hang Wang ◽

Dao Bo Wang ◽

Zain Anwar Ali

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicle ◽

Optimization Algorithm ◽

Vehicle Group ◽

Flight Duration ◽

Planning Strategy ◽

Optimum Position ◽

Cauchy Mutation ◽

Aerial Vehicle ◽

Vehicle Path

To improve the performance of multi-unmanned aerial vehicle path planning in plateau narrow area, a control strategy based on Cauchy mutant pigeon-inspired optimization algorithm is proposed in this article. The Cauchy mutation operator is chosen to improve the pigeon-inspired optimization algorithm by comparing and analyzing the changing trend of fitness function of the local optimum position and the global optimum position when dealing with unmanned aerial vehicle path planning problems. The plateau topography model and plateau wind field model are established. Furthermore, a variety of control constrains of unmanned aerial vehicles are summarized and modeled. By combining with relative positions and total flight duration, a cooperative path planning strategy for unmanned aerial vehicle group is put forward. Finally, the simulation results show that the proposed Cauchy mutant pigeon-inspired optimization method gives better robustness and cooperative path planning strategy which are effective and advanced as compared with traditional pigeon-inspired optimization algorithm.

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