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Performance characterization of modified random direction mobility model in UAVs cellular network

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Abstract

This paper deals with the characterization of proposed modified random direction (M-RD) mobility model in unmanned aerial vehicles (UAVs) deployed as base stations for serving terrestrial users within finite cellular network. This mobility model is the upgradation to classical random direction (RD) mobility model with inclusion of several stops within the path to improve the probability of serving users. The simulation results of proposed M-RD mobility model in terms of distribution, density and average rate are compared with prevalent mobility models, i.e., random walk (RW) and random waypoint (RWP). Distribution plots showing positive skewness for RW/RWP and negative skewness for M-RD, reveal a framework for obtaining uniformity in the distribution with an adjustment of proportion of UBSs following RWP and M-RD. The density of the network of interfering UBSs for M-RD exhibits better homogeneity at large time intervals. Also, the average rate achieved by a typical user located at different locations in the environment has shown remarkable improvement in M-RD as compared to RW and RWP, especially at large time intervals.

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References

  1. Mozaffari M, Saad W, Bennis M, Nam Y H and M Debbah 2019 A tutorial on UAVs for wireless networks: Applications, challenges, and open problems.IEEE Commun. Surv. Tutor. 21(3): 334–2360

    Article  Google Scholar 

  2. Zeng Y, Zhang R and Lim T J 2016 Wireless communications with unmanned aerial vehicles: opportunities and challenges. IEEE Commun. Mag. 54(5): 36–42

  3. 3GPP 2018 Enhancement for unmaned aerial vehicles (UAVs). Technical Report(TR) 22.829, 3rd Generation Partnership Project (3GPP), Version 0.0.0

  4. 3GPP. Enhanced LTE support for aerial vehicles 2018 Technical Report(TR) 36.777, 3rd Generation Partnership Project (3GPP), Version 1.1.0

  5. Lin X, Ganti R K, Fleming P J and Andrews J G 2013 Towards understanding the fundamentals of mobility in cellular networks. IEEE Trans. Wirel. Commun. 12(4): 1686–1698

    Article  Google Scholar 

  6. Banagar M and Dhillon H S 2019 Fundamentals of drone cellular network analysis under random waypoint mobility model. In 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6)

  7. Sichitiu M L 2009 Mobility models for ad hoc networks, in: Guide to wireless ad hoc networks. Computer Communications and Networks. Springer, London, 2009 (pp. 237–254)

  8. Jardosh A, Belding-Royer E M, Almeroth K C and Suri S 2003 Towards realistic mobility models for mobile ad hoc networks. In: Proceedings of the 9th Annual International Conference on Mobile Computing and Networking, MobiCom ’03, New York, NY, USA, 2003. Association for Computing Machinery (pp.217–229)

  9. Feeley M, Hutchinson N and Ray S 2004 Realistic mobility for mobile ad hoc network simulation. In: Nikolaidis I, Barbeau M and Kranakis E (eds.) Ad-Hoc, Mobile, and Wireless Networks. Springer, Berlin (pp. 324–329)

  10. Camp T, Boleng J and Davies V 2002 A survey of mobility models for ad hoc network research. Wirel. Commun. Mobile Comput. 2(5): 483–502

    Article  Google Scholar 

  11. Ribeiro A  G and Sofia R 2011 A survey on mobility models for wireless networks. Technical Report(TR) SITI-TR-11-01, SITI Technical Report, 02

  12. Zhi R, Gao F and Yang J 2009 Nonuniform property of random direction mobility model for manet. In: 5th International Conference on Wireless Communications, Networking and Mobile Computing (pp. 1–4)

  13. Zhan P, Yu K and Swindlehurst A L 2011 Wireless relay communications with unmanned aerial vehicles: Performance and optimization. IEEE Trans. Aerosp. Electron. Syst. 47(3): 2068–2085

    Article  Google Scholar 

  14. Al-Hourani A, Kandeepan S and Lardner S 2014 Optimal LAP altitude for maximum coverage. IEEE Wirel. Commun. Lett. 3(6): 569–572

    Article  Google Scholar 

  15. Chetlur V  V and Dhillon H S 2017 Downlink coverage analysis for a finite 3-D wireless network of unmanned aerial vehicles. IEEE Trans. Commun. 65(10): 4543–4558

    Google Scholar 

  16. Azari M M, Murillo Y, Amin O, Rosas F, Alouini M-S and Pollin S 2017 Coverage maximization for a poisson field of drone cells. In: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1–6)

  17. Fotouhi A, Ding M and Hassan M 2016 Dynamic base station repositioning to improve performance of drone small cells. In: 2016 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6)

  18. Mozaffari M, Saad W, Bennis M and Debbah M 2016 Efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage. IEEE Commun. Lett. 20(8): 1647–1650

    Article  Google Scholar 

  19. Lyu J, Zeng Y, Zhang R and Lim T J 2017 Placement optimization of UAV-mounted mobile base stations. IEEE Commun. Lett. 21(3): 604–607

    Article  Google Scholar 

  20. Mozaffari M, Saad W, Bennis M and Debbah M 2016 Unmanned aerial vehicle with underlaid device-to-device communications: Performance and tradeoffs. IEEE Trans. Wirel. Commun. 15(6): 3949–3963

    Article  Google Scholar 

  21. Enayati S, Saeedi H, Pishro-Nik H and Yanikomeroglu H 2019 Moving aerial base station networks: A stochastic geometry analysis and design perspective. IEEE Trans. Wirel. Commun. 18(6): 2977–2988

    Article  Google Scholar 

  22. Amer R,  Saad W and Marchetti  N 2020 Mobility in the sky: Performance and mobility analysis for cellular-connected UAVs. IEEE Trans. Commun. 68(5): 3229–3246

    Article  Google Scholar 

  23. Tabassum H, Salehi M and Hossain E 2019 Fundamentals of mobility-aware performance characterization of cellular networks: A tutorial. IEEE Commun. Surv. Tutor. 21(3): 2288–2308

    Article  Google Scholar 

  24. Fan B, Narayanan S  and Ahmed H  2003 The important framework for analyzing the impact of mobility on performance of routing protocols for adhoc networks. Ad Hoc Netw. 1(4): 383–403

    Article  Google Scholar 

  25. Jiang F  and Swindlehurst A  L 2012 Optimization of UAV heading for the ground-to-air uplink. IEEE J. Sel. Areas Commun. 30(5): 993–1005

    Article  Google Scholar 

  26. Khoshkholgh M  G, Navaie K, Yanikomeroglu H, Leungand V  C  M, Shin KG 2019 Coverage performance of aerial-terrestrial hetnets. In: 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring) (pp. 1–5)

  27. Guan X, Huang Y, Dong C and Wu Q 2020 User association and power allocation for uav-assisted networks: A distributed reinforcement learning approach. China Commun. 17(12): 110–122

    Article  Google Scholar 

  28. Banagar M and Dhillon H S 2019 3GPP-inspired stochastic geometry-based mobility model for a drone cellular network. In: 2019 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6)

  29. Banagar M and Dhillon H S 2020 Performance characterization of canonical mobility models in drone cellular networks. IEEE Trans. Wirel. Commun. 19(7): 4994–5009

    Article  Google Scholar 

  30. Sharma P  K and Kim D  I 2019 Coverage probability of 3-D mobile UAV networks. IEEE Wirel. Commun. Lett. 8(1): 97–100

    Article  Google Scholar 

  31. Sharma P  K and Kim D  I 2019 Random 3D mobile uav networks: Mobility modeling and coverage probability. IEEE Trans. Wirel. Commun. 18(5): 2527–2538

    Article  Google Scholar 

  32. Hu J  and Beaulieu N  C 2005 Accurate simple closed-form approximations to Rayleigh sum distributions and densities. IEEE Commun. Lett. 9(2): 109–111

    Article  Google Scholar 

  33. Coleman TF and Li Y 1993 An interior trust region approach for nonlinear minimization subject to bounds. Technical report, USA

    MATH  Google Scholar 

  34. Conn A, Gould N and Toint P 1988 Testing a class of methods for solving minimization problems with simple bounds on the variables. Math. Comput. 50: 05

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India

    Rajdeep Singh Sohal & Jaipreet Kaur

  2. Department of Electronics and Communication Engineering, Guru Nanak Dev University Regional Campus, Jalandhar, Punjab, India

    Vinit Grewal

  3. Department of Electrical and Instrumentation Engineering, Thapar Institute of Engineering and Technology, Patiala, India

    Manbir Kaur

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  1. Rajdeep Singh Sohal
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  2. Vinit Grewal
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  3. Jaipreet Kaur
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  4. Manbir Kaur
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Correspondence to Rajdeep Singh Sohal.

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Sohal, R.S., Grewal, V., Kaur, J. et al. Performance characterization of modified random direction mobility model in UAVs cellular network. Sādhanā 47, 97 (2022). https://doi.org/10.1007/s12046-022-01848-9

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  • DOI: https://doi.org/10.1007/s12046-022-01848-9

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