A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 7 Issue 2
Mar.  2020

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2020  >  7(2): 559-567
Hailong Huang, Chao Huang and Dazhong Ma, "A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 559-567, Mar. 2020. doi: 10.1109/JAS.2019.1911813
Citation: Hailong Huang, Chao Huang and Dazhong Ma, "A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks," IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 559-567, Mar. 2020. doi: 10.1109/JAS.2019.1911813
shu

A Method for Deploying the Minimal Number of UAV Base Stations in Cellular Networks

doi: 10.1109/JAS.2019.1911813

  • College of Information Science and Engineering, Northeastern University, Shenyang 110004, China

Funds:  This work was supported by the National Natural Science Foundation of China (61903076, 61773109) and Liaoning Revitalization Talents Program (XLYC1807009)
More Information
    Abstract
  • In this paper, we consider the scenario of using unmanned aerial vehicles base stations (UAV-BSs) to serve cellular users. In particular, we focus on finding the minimum number of UAV-BSs as well as their deployment. We propose an optimization model which minimizes the number of UAV-BSs and optimize their positions such that the user equipment (UE) covered ratio is no less than the expectation of network suppliers, the UEs receive acceptable downlink rates, and the UAV-BSs can work in a sustainable manner. We show the NP-hardness of this problem and then propose a method to address it. The method first estimates the range of the number of UAV-BSs and then converts the original problem to one which maximizes the UE served ratio, given the number of UAV-BSs within that range. We present a maximizing algorithm to solve it with the proof of convergence. Extensive simulations based on a realistic dataset have been conducted to demonstrate the effectiveness of the proposed method.

     

    • FullText(HTML)
  • loading
    • 1 A supplier defined constant.
    2 The definition of vertex is provided in Fig. 1.
    3 It is worth noticing that the range of n shown in (5) is to cover M street points instead of $ \alpha $ percent of UEs in the area. Further, it is based on the Assumption 1, which does not always hold in practical street graphs. Thus, it is only a rough range. However, we claim that this rough estimation is still reasonable in our problem setting. Firstly, the lower and upper bounds are theoretical results and n is unlikely to take either of them. Secondly, in practice the network supplier may expect $ \alpha $ to be close to 100%, e.g., 98%, which does not influence the bounds significantly.
    4 When looking for the position for the first UAV-BS, constraints (2) and (3) are not applied.
    References(32)
  • [1]
    H. Huang and A. V. Savkin, “Towards the internet of flying robots: a survey,” Sensors, vol. 18, no. 11, pp. 4038, 2018. doi: 10.3390/s18114038
    [2]
    E. Kalantari, H. Yanikomeroglu, and A. Yongacoglu, “On the number and 3D placement of drone base stations in wireless cellular networks,” in Proc. 84th IEEE Vehicular Technology Conf., Montral, QC, Canada, 2016, pp. 1–6.
    [3]
    U. Feige, “A threshold of ln n for approximating set cover,” J. ACM, vol. 45, no. 4, pp. 634–652, 1998. doi: 10.1145/285055.285059
    [4]
    T. Chen, M. A. Kaafar, and R. Boreli, “The where and when of finding new friends: analysis of a location-based social discovery network.” in Proc. 7th Int. Conf. Weblogs and Society Media, 2013, pp. 61–70.
    [5]
    K. Thilakarathna, S. Seneviratne, K. Gupta, M. A. Kaafar, and A. Seneviratne, “A deep dive into location-based communities in social discovery networks,” Computer Communications, vol. 100, pp. 78–90, 2017. doi: 10.1016/j.comcom.2016.11.008
    [6]
    A. Al-Hourani, S. Kandeepan, and A. Jamalipour, “Modeling air-toground path loss for low altitude platforms in urban environments,” in Proc. IEEE Global Communications Conf., Austin, TX, USA, 2014, pp. 2898–2904.
    [7]
    R. Amorim, H. Nguyen, P. Mogensen, I. Z. Kovács, J. Wigard, and T. B. Sørensen, “Radio channel modeling for UAV communication over cellular networks,” IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 514–517, 2017. doi: 10.1109/LWC.2017.2710045
    [8]
    A. Al-Hourani and K. Gomez, “Modeling cellular-to-UAV path-loss for suburban environments,” IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 82–85, 2018. doi: 10.1109/LWC.2017.2755643
    [9]
    M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Drone small cells in the clouds: design, deployment and performance analysis,” in Proc. IEEE Global Communications Conf., San Diego, CA, USA, 2015, pp. 1–6.
    [10]
    R. I. Bor-Yaliniz, A. El-Keyi, and H. Yanikomeroglu, “Efficient 3-D placement of an aerial base station in next generation cellular networks,” in Proc. IEEE Int. Conf. Communications, Kuala Lumpur, Malaysia, 2016, pp. 1–5.
    [11]
    M. Alzenad, A. El-Keyi, F. Lagum, and H. Yanikomeroglu, “3D placement of an unmanned aerial vehicle base station (UAV-BS) for energyefficient maximal coverage,” IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 434–437, 2017. doi: 10.1109/LWC.2017.2700840
    [12]
    A. Fotouhi, M. Ding, and M. Hassan, “Dynamic base station repositioning to improve spectral efficiency of drone small cells,” in Proc. 18th IEEE Int. Symp. A World of Wireless, Mobile and Multimedia Networks, Macao, China, 2017, pp. 1–9.
    [13]
    V. Sharma, M. Bennis, and R. Kumar, “UAV-assisted heterogeneous networks for capacity enhancement,” IEEE Communications Letters, vol. 20, no. 6, pp. 1207–1210, 2016. doi: 10.1109/LCOMM.2016.2553103
    [14]
    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, 2019. doi: 10.1109/LWC.2018.2872547
    [15]
    H. Huang and A. V. Savkin, “A method for optimized deployment of unmanned aerial vehicles for maximum coverage and minimum interference in cellular networks,” IEEE Trans. Industrial Informatics, vol. 15, no. 5, pp. 2638–2647, May 2019. doi: 10.1109/TII.2018.2875041
    [16]
    F. Lagum, I. Bor-Yaliniz, and H. Yanikomeroglu, “Strategic densification with UAV-BSs in cellular networks,” IEEE Wireless Communications Letters, vol. 7, no. 3, pp. 384–387, 2018. doi: 10.1109/LWC.2017.2779483
    [17]
    H. Huang, A. V. Savkin, M. Ding, M. A. Kaafar, and C. Huang, “On the problem of flying robots deployment to improve cellular user experience,” in Proc. 37th Chinese Control Conf., Jul. 2018, pp. 6356–6359.
    [18]
    H. Wu, X. Tao, N. Zhang, and X. Shen, “Cooperative UAV clusterassisted terrestrial cellular networks for ubiquitous coverage,” IEEE J. Selected Areas in Communications, vol. 36, no. 9, pp. 2045–2058, 2018. doi: 10.1109/JSAC.2018.2864418
    [19]
    Z. Zhou, J. Feng, B. Gu, B. Ai, S. Mumtaz, J. Rodriguez, and M. Guizani, “When mobile crowd sensing meets uav: Energy-efficient task assignment and route planning,” IEEE Trans. Communications, vol. 66, no. 11, pp. 5526–5538, Nov. 2018. doi: 10.1109/TCOMM.2018.2857461
    [20]
    M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage,” IEEE Communications Letters, vol. 20, no. 8, pp. 1647–1650, 2016. doi: 10.1109/LCOMM.2016.2578312
    [21]
    E. Yanmaz, S. Yahyanejad, B. Rinner, H. Hellwagner, and C. Bettstetter, “Drone networks: communications, coordination, and sensing,” Ad Hoc Networks, vol. 68, pp. 1–15, 2018. doi: 10.1016/j.adhoc.2017.09.001
    [22]
    A. V. Savkin and H. Huang, “A method for optimized deployment of a network of surveillance aerial drones,” IEEE Systems J., pp. 1–4, 2019.
    [23]
    H. Huang and A. V. Savkin, “An algorithm of reactive collision free 3D deployment of networked unmanned aerial vehicles for surveillance and monitoring,” IEEE Trans. Industrial Informatics, pp. 1–9, 2019.
    [24]
    A. V. Savkin and H. Huang, “Proactive deployment of aerial drones for coverage over very uneven terrains: a version of the 3D art gallery problem,” Sensors, vol. 19, no. 6, pp. 1438, 2019. doi: 10.3390/s19061438
    [25]
    H. Huang, A. V. Savkin, M. Ding, and C. Huang, “Mobile robots in wireless sensor networks: a survey on tasks,” Computer Networks, vol. 148, pp. 1–19, 2019. doi: 10.1016/j.comnet.2018.10.018
    [26]
    C. H. Liu, Z. Chen, J. Tang, J. Xu, and C. Piao, “Energy-efficient uav control for effective and fair communication coverage: a deep reinforcement learning approach,” IEEE J. Selected Areas in Communications, vol. 36, no. 9, pp. 2059–2070, 2018. doi: 10.1109/JSAC.2018.2864373
    [27]
    H. Huang and A. V. Savkin, “An algorithm of efficient proactive placement of autonomous drones for maximum coverage in cellular networks,” IEEE Wireless Communications Letters, vol. 7, no. 6, pp. 994–997, 2018. doi: 10.1109/LWC.2018.2846237
    [28]
    P. Yang, X. Cao, C. Yin, Z. Xiao, X. Xi, and D. Wu, “Proactive dronecell deployment: overload relief for a cellular network under flash crowd traffic,” IEEE Trans. Intelligent Transportation Systems, vol. 18, no. 10, pp. 2877–2892, 2017. doi: 10.1109/TITS.2017.2700432
    [29]
    (2012) 3GPP TR 36.828: Further enhancements to LTE time division duplex (TDD) for (DL-UL) interference management and traffic adaptation. [Online]. Available: http://www.qtc.jp/3GPP/Specs/36828-b00.pdf
    [30]
    A. Fotouhi, M. Ding, and M. Hassan, “Understanding autonomous drone maneuverability for internet of things applications,” in Proc. 18th IEEE Int. Symp. A World of Wireless, Mobile and Multimedia Networks, Macao, China, 2017, pp. 1–6.
    [31]
    F. Morbidi, R. Cano, and D. Lara, “Minimum-energy path generation for a quadrotor UAV,” in Proc. IEEE Int. Conf. Robotics and Automation, 2016, pp. 1492–1498.
    [32]
    A. N. Kolmogorov and S. V. Fomin, Introductory Real Analysis. Courier Corporation, 2012.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(5)  / Tables(2)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1177) PDF downloads(56) Cited by()

    Highlights

    • We mathematically formulate an optimization problem which minimizes the number of UAV-BSs subject to the three types of constraints: coverage, downlink rate, and the charging requirement.
    • We propose a solution to the formulated problem which involves the estimation of the bounds of the number of UAVBSs and a coverage maximizing algorithm to place UAV-BSs.
    • The convergence of such a coverage maximizing algorithm is proved.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return