近來，將無線網絡的二維環境擴展成三維（3D）已被認為是下一代無線通信網絡的革命性突破。 正如文獻所揭示的，由於地面上的低能效（EE）BS（由GBS 表示）要關閉，因此調度無人飛行器（UAV）輔助基站（UBS）可以填補覆蓋範圍的漏洞。 事實上，除了彌補覆蓋漏洞之外，在具有不同睡眠深度的睡眠策略中，正確部署UBS 可以進一步擴大用戶切換和交換過程的靈活性。 因此，在本文中，我們提出了一種用於小蜂窩網絡（SCN）的低複雜度UBS 輔助睡眠策略。 通過仿真結果驗證，發現使用提出的算法部署適當數量的功能性UBS（即具有足夠的用戶可容納性）可以顯著提高3D 小細胞網路的能量使用效益。

Extending the two-dimensional topology of wireless networks to the three-dimensional (3D) structure has recently been recognized as a revolutionary breakthrough for the next generation of wirelesss communication networks. As revealed in the literature, dispatching the unmanned aerial vehicle (UAV) assisted base-station (UBS) can ll in the coverage holes as the low energy-e ciency (EE) BSs on the ground (denoted by GBSs) are switched o . Defacto, in addition to mending the coverage holes, properly deploying the UBSs can further enlarge the exibility of the user handover and exchange procedures in the sleep strategy with variant sleep depths. Thus, in this paper, we propose a low-complexity UBS-assisted sleep strategy for the small-cell network (SCN). Veri ed by the simulation results, it is found that deploying a proper number of capable UBSs (i.e., with enough user accommodability) using the proposed algorithm can remarkably raise the EE of the 3D SCNs.

[1] Y. Zeng and R. Zhang, Energy-e cient uav communication with trajectory optimization," IEEE Transactions on Wireless Communications, vol. 16, no. 6, pp. 3747 { 3760, June 2017.
[2] J. Zhang, Y. Zeng, and R. Zhang, Spectrum and energy e ciency maximization in uav-enabled mobile relaying," in 2017 IEEE International Conference on Communications, 2017, pp. 1{6.
[3] L. Wang, B. Hu, and S. Chen, Energy e cient placement of a drone base station for minimum required transmit power," IEEE Wireless Communications Letters, vol. 16, no. 4, pp. 1 { 1, February 2018.
[4] M. Alzenad, A. El-Keyi, and H. Yanikomeroglu, 3-d placement of an unmanned aerial vehicle base station for maximum coverage of users with di erent qos requirements," IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 38 { 41, February 2018.
[5] A. V. Savkin and H. Huang, Deployment of unmanned aerial vehicle base stations for optimal quality of coverage," IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 321 { 324, February 2019.
[6] M. Alzenad, A. El-Keyi, F. Lagum, and H. Yanikomeroglu, 3-d placement of an unmanned aerial vehicle base station (uav-bs) for energy-e cient maximal coverage," IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 434 { 437, February 2017.
[7] J. Yu, R. Zhang, Y. Gaa, and L.-L. Yang, Modularity-based dynamic clustering for energy e cient uavs-aided communications," IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 728 { 731, October 2018.
[8] Y. Zeng, R. Zhang, and T. J. Lim, Wireless communications with unmanned aerial vehicles: Opportunities and challenges," IEEE Communications Magazine, vol. 54, pp. 36 { 42, May 2016.
[9] G. Zhang, H. Yan, Y. Zeng, M. Cui, and Y. Liu, Trajectory optimization and power allocation for multi-hop uav relaying communications," IEEE Access, pp. 48 566 { 48 576, September 2018.
[10] C. Zhan, Y. Zeng, and R. Zhang, Trajectory design for distributed estimation in uav-enabled wireless sensor network," IEEE Transactions on Vehicular Technol- ogy, vol. 67, no. 10, pp. 10 155 { 10 159, October 2018.
[11] Z. Xiao, B. Zhu, Y. Wang, and P. MIAO, Low-complexity path planning algorithm for unmanned aerial vehicles in complicated scenarios," IEEE Access, vol. 6, no. 10, pp. 10 155 { 10 159, October 2018.
[12] M. A. Abdel-Malek, A. S. Ibrahim, and M. Mokhtar, Optimum uav positioning for better coverage-connectivity tradeo ," in 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), 2017, pp. 1{6.
[13] X. Xi, X. Cao, P. Yang, J. Chen, T. Quek, and D. Wu, Joint user association and uav location optimization for uav-aided communications," IEEE Wireless Communications Letters (accepted for publication in 23 August. 2019 and now available in IEEE Wireless Communications Letters).
[14] P. Yang, X. Cao, X. Xi, Z. Xiao, and D. Wu, Three-dimensional drone-cell deployment for congestion mitigation in cellular networks," IEEE Transactions on Vehicular Technology, vol. 67, no. 10, pp. 9867 { 9881, October 2018.
[15] A. Alsharoa, H. Ghazzai, A. Kadri, and A. E. Kamal, Energy management in cellular hetnets assisted by solar powered drone small cells," in 2017 IEEE Wireless Communications and Networking Conference, 2017, pp. 1{6.
[16] W. Chang, W.-Y. Cheng, Z.-T. Meng, and S.-L. Su, Energy-e cient sleep strategy with variant sleep depths for open-access femtocell networks," IEEE Commu-nications Letters, vol. 23, no. 4, pp. 708 { 711, April 2019.
[17] C. Liu, B. Natarajan, and H. Xia, Small cell base station sleep strategies for energy e ciency," IEEE Trans. Veh. Technol, vol. 65, no. 3, pp. 1652{1661, Mar. 2016.
[18] J. Kim, W. S. Jeon, and D. G. Jeong, Base-station sleep management in openaccess femtocell networks," IEEE Trans. Veh. Technol, vol. 65, no. 5, pp. 3786{ 3791, May 2016.
[19] Y. Xu, J. Chen, D. Wu, and W. Xu, Toward 5G: A novel sleeping strategy for green distributed base stations in small cell networks," in Proc. International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Dec. 2016, pp. 115{ 119.
[20] M. Kashef, M. Ismail, E. Serpedin, and K. Qaraqe, Balanced dynamic planning in green heterogeneous cellular networks," IEEE J. Select. Areas Commun, vol. 34, no. 12, pp. 3299{3312, Dec. 2016.
[21] A. H. Arani, A. Mehbodniya, M. J. Omidi, F. Adachi, W. Saad, and Ismail, Distributed learning for energy-e cient resource management in self-organizing heterogeneous networks," IEEE Trans. Veh. Technol., vol. 66, no. 10, pp. 9287{9303, Oct. 2017.
[22] M. Oikonomakou, A. Antonopoulos, L. Alonso, and C. Verikoukis, Evaluating cost allocation imposed by cooperative switching o in multioperator shared Het-Nets," IEEE Trans. Veh. Technol, vol. 66, no. 12, pp. 11 352{11 365, Dec. 2017.
[23] N. Yu, Y. Miao, L. Mu, H. Du, H. Huang, and X. Jia, Minimizing energy cost by dynamic switching On/O base stations in cellular networks," IEEE Trans. Wireless Commun, vol. 15, no. 11, pp. 7457{7469, Nov. 2016.
[24] L. Pei, J. Huilin, P. Zhiwen, and Y. Xiaohu, Energy-delay tradeo in ultra-dense networks considering bs sleeping and cell association," IEEE Trans. Veh. Technol, vol. 67, no. 1, pp. 734{751, Jan. 2018.
[25] Q.-N. Le-The, T. Beitelmal, F. Lagum, S. S. Szyszkowicz, and H. Yanikomeroglu, Cell switch-o algorithms for spatially irregular base station deployments," IEEE Wireless Commun. Lett, vol. 6, no. 3, pp. 354{358, June 2017.
[26] J. Kim, H.-W. Lee, and S. Chong, Tra c-aware energy-saving base station sleeping and clustering in cooperative networks," IEEE Trans. Wireless Commun, vol. 17, no. 2, pp. 1173{1186, Feb. 2018.
[27] H. Nabuuma, E. Alsusa, and W. Pramudito, A load-aware base station switch-o technique for enhanced energy e ciency and relatively identical outage probability," in Proc. IEEE Vehicular Technology Conference (VTC), May 2015, pp. 1{5.
[28] H. Claussen, I. Ashraf, and L. T. W. Ho, Dynamic idle mode procedures for femtocells," Bell Labs Tech. J, vol. 15, no. 2, pp. 95{116, Sept. 2010.