Dynamic Network Access for Multi-UAV Networks: A Cloud-Assisted Learning Algorithm
In this paper, we study the strategy of UAV dynamic network access in the large-scale UAVs swam. We model the master UAV providing communication coverage for the small UAVs which transformed the large-scale UAVs communication problem into the optimization problem. Compared to the traditional ground user network access, the characteristic of UAV’s mobility have been considered and each UAV have chance to move to any master UAV for better service. We propose a joint optimization for the throughput and flight loss. Due to the limitation of flight loss, the UAVs can not fly to different networks many times for learning. We set up a load aggregator cloud to help the UAVs simulate the results of each decision. We propose a dynamic network access algorithm based on SLA which is proved to achieve stable solutions with dynamic and incomplete information constraint. The simulation results show that this algorithm can converge to the optimal solution. Also, it is shown that the algorithm has strong robustness and can get good utility than other algorithms regardless of how the environment changing.
KeywordsDynamic network access Multi-UAV communication Cloud-assisted SLA
This work was supported by the National Science Foundation of China under Grant No. 61771488, No. 61671473, No. 61631020 and No. 61401508, the in part by Natural Science Foundation for Distinguished Young Scholars of Jiangsu Province under Grant No. BK20160034, and in part by the Open Research Foundation of Science and Technology on Communication Networks Laboratory.
- 2.Kuriki, Y., Namerikawa, T.: Formation control with collision avoidance for a multi-UAV system using decentralized MPC and consensus-based control. In: European Control Conference (ECC), Linz, pp. 3079–3084 (2015)Google Scholar
- 3.Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Drone small cells in the clouds: design, deployment and performance analysis. In: IEEE Global Communications Conference (GLOBECOM), San Diego, CA, pp. 1–6 (2015)Google Scholar
- 7.Bor-Yaliniz, R.I., El-Keyi, A., Yanikomeroglu, H.: Efficient 3-D placement of an aerial base station in next generation cellular networks. In: 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1–5 (2016)Google Scholar
- 9.Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Optimal transport theory for power-efficient deployment of unmanned aerial vehicles. In: 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1–6 (2016)Google Scholar
- 10.Wu, Q., Zeng, Y., Zhang, R.: Joint trajectory and communication design for UAV-enabled multiple access. In: 2017 IEEE Global Communications Conference, GLOBECOM 2017, Singapore, pp. 1–6 (2017)Google Scholar
- 11.Wu, Q., Zeng, Y., Zhang, R.: Joint trajectory and communication design for multi-UAV enabled wireless networks. IEEE Trans. Wirel. Commun. PP(99), 1Google Scholar
- 16.Mozaffari, M., Saad, W., Bennis, M., Debbah, M.: Optimal transport theory for power-efficient deployment of unmanned aerial vehicles. In: 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, pp. 1–6 (2016)Google Scholar