Abstract
Unmanned aerial vehicles (UAVs) have become an inevitable choice due to their advantages of flexible movement and rapid deployment for ensuring emergency communication services. The endurance of UAVs may be limited due to the inability to charge in time in emergency communication scenarios. In this paper, we present a nonorthogonal multiple access (NOMA) based multiUAV dispatching mobile edge computing (MEC) offloading for emergency communication networks, where each UAV is equipped with MEC server to provide computing services for terrestrial users, and NOMA technology is used to increase spectrum utilization and reduce task processing energy consumption. The main goal is to maximize the energy efficiency of UAVMEC systems by jointly optimizing computing resources, power control, and UAV dispatch in this paper. The system energy efficiency (SEE) optimization problem is complex and nonconvex, to solve this, we first adopt the Dinkelbach method to transform the original problem into an equivalent problem. Then, we decompose the equivalent problem into a resource allocation subproblem based on a given UAV dispatching strategy and UAV dispatching subproblem for a given resource allocation strategy. To address the resource allocation problem, we derive closedform solutions for computing resources and power allocation with the Lagrangian dual method. Next, we propose a threestage UAV energyefficient dispatching scheme based on the global KMeans (GKM) algorithm to optimize the dispatching of UAVs. Numerical results demonstrate the effectiveness of the proposed scheme.
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Data Availability
The datasets generated and analyzed during the current study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from the corresponding author on reasonable request.
References
Ada, S., Sharman, R., Han, W., & Brennan, J. A. (2016). Factors impacting the intention to use emergency notification services in campus emergencies: an empirical investigation. IEEE Transactions on Professional Communication, 59(2), 89–109. https://doi.org/10.1109/TPC.2016.2527248
Lei, C., Lin, W., & Miao, L. (2015). A stochastic emergency vehicle redeployment model for an effective response to traffic incidents. IEEE Transactions on Intelligent Transportation Systems, 16(2), 898–909. https://doi.org/10.1109/TITS.2014.2345480
Zhou, J., Jia, J. Y., Zhang, Z., & Chen, S. W. (2020). Key technologies for emergency communication based on 5G networked UAVs. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 32(4), 511–518.
Dong, C., Shen, Y., & Qu, Y. B. (2020). A survey of UAVbased edge intelligent computing. Chinese Journal of Intelligent Science and Technology, 2(3), 227–239.
Liu, Y., Qin, Z., Cai, Y., Gao, Y., Li, G. Y., & Nallanathan, A. (2019). UAV communications based on nonorthogonal multiple access. IEEE Wireless Communications, 26(1), 52–57. https://doi.org/10.1109/MWC.2018.1800196
Senadhira, N., Durrani, S., Zhou, X., Yang, N., & Ding, M. (2020). Uplink NOMA for cellularconnected UAV: impact of UAV trajectories and altitude. IEEE Transactions on Communications, 68(8), 5242–5258. https://doi.org/10.1109/TCOMM.2020.2995373
Feng, W., et al. (2020). NOMAbased UAVaided networks for emergency communications. China Communications, 17(11), 54–66. https://doi.org/10.23919/JCC.2020.11.005
Mei, W., & Zhang, R. (2019). Uplink cooperative NOMA for cellularconnected UAV. IEEE Journal of Selected Topics in Signal Processing, 13(3), 644–656. https://doi.org/10.1109/JSTSP.2019.2899208
Ji, J., Zhu, K., Yi, C., & Niyato, D. (2021). Energy consumption minimization in UAVassisted mobileedge computing systems: joint resource allocation and trajectory design. IEEE Internet of Things Journal, 8(10), 8570–8584. https://doi.org/10.1109/JIOT.2020.3046788
Liu, M., Yang, J., & Gui, G. (2019). DSFNOMA: UAVassisted emergency communication technology in a heterogeneous internet of things. IEEE Internet of Things Journal, 6(3), 5508–5519. https://doi.org/10.1109/JIOT.2019.2903165
Wang, Z. D., Zhang, T. K., Xu, W. J., & Yang, L. W. (2020). Dynamic Caching Placement and Resource Allocation in UAV Emergency Communication Networks. Journal of Beijing University of Posts and Telecommunications, 43(6), 42–50. https://doi.org/10.13190/j.jbupt
Zhou, F., Wu, Y., Hu, R. Q., & Qian, Y. (2018). Computation rate maximization in UAVenabled wirelesspowered mobileedge computing systems. IEEE Journal on Selected Areas in Communications, 36(9), 1927–1941. https://doi.org/10.1109/JSAC.2018.2864426
Hu, X., Wong, K., Yang, K., & Zheng, Z. (2019). UAVassisted relaying and edge computing: scheduling and trajectory optimization. IEEE Transactions on Wireless Communications, 18(10), 4738–4752. https://doi.org/10.1109/TWC.2019.2928539
Dang, T., Liu, C., & Peng, M. (2022). Lowlatency mobile virtual reality content delivery for unmanned aerial vehicleenabled wireless networks with energy constraints. IEEE Transactions on Vehicular Technology. https://doi.org/10.1109/TVT.2022.3212868
Liao, N., He, P., Zhang, Y., et al. (2022). Trajectory and resource allocation optimization method for UAVrelaying communication. Mobile Communications, 46(S9), 1–7.
Tang, R., Feng, W., Chen, Y., & Ge, N. (2021). NOMAbased UAV communications for maritime coverage enhancement. China Communications, 18(4), 230–243. https://doi.org/10.23919/JCC.2021.04.017
Dai, M., Wu, Y., Qian, L., Su, Z., Lin, B., & Chen, N. (2022). UAVassisted multiaccess computation offloading via hybrid NOMA and FDMA in marine networks. IEEE Transactions on Network Science and Engineering. https://doi.org/10.1109/TNSE.2022.3205303
Li, G. Q., Lin, J. Z., Xu, Y. J., Huang, Z. W., & Liu, T. (2020). User grouping and power allocation algorithm for UAVaided NOMA network. Journal on Communications, 41(9), 21–28.
Zhou, X., Huang, L., Ye, T., & Sun, W. (2022). Computation bits maximization in UAVassisted MEC networks with fairness constraint. IEEE Internet of Things Journal, 9(21), 20997–21009. https://doi.org/10.1109/JIOT.2022.3177658
He, Y., Gan, Y., Cui, H., & Guizani, M. (2023). Fairnessbased 3D multiUAV trajectory optimization in multiUAVassisted MEC system. IEEE Internet of Things Journal, 10(13), 11383–11395. https://doi.org/10.1109/JIOT.2023.3241087
Wang, W., Ni, W., Tian, H., & Song, L. (2022). Intelligent Omnisurface enhanced aerial secure offloading. IEEE Transactions on Vehicular Technology, 71(5), 5007–5022. https://doi.org/10.1109/TVT.2022.3150769
Budhiraja, I., Kumar, N., Tyagi, S., & Tanwar, S. (2021). energy consumption minimization scheme for NOMAbased mobile edge computation networks underlaying UAV. IEEE Systems Journal, 15(4), 5724–5733. https://doi.org/10.1109/JSYST.2021.3076782
Zhang, X., Zhang, J., Xiong, J., et al. (2020). Energy efficient multiUAVenabled multiaccess edge computing incorporating NOMA. IEEE Internet of Things Journal, 7(4), 5613–5627.
Wang, Y., Ru, Z.Y., Wang, K., & Huang, P.Q. (2020). Joint deployment and task scheduling optimization for largescale mobile users in multiUAVenabled mobile edge computing. IEEE Transactions on Cybernetics, 50(9), 3984–3997. https://doi.org/10.1109/TCYB.2019.2935466
Mozaffari, M., Saad, W., Bennis, M., & Debbah, M. (2017). Mobile unmanned aerial vehicles (UAVs) for energyefficient internet of things communications. IEEE Transactions on Wireless Communications, 16(11), 7574–7589. https://doi.org/10.1109/TWC.2017.2751045
Ruan, L., et al. (2018). Energyefficient multiUAV coverage deployment in UAV networks: A gametheoretic framework. China Communications, 15(10), 194–209. https://doi.org/10.1109/CC.2018.8485481
Alzenad, M., ElKeyi, A., & Yanikomeroglu, H. (2018). 3D placement of an unmanned aerial vehicle base station for maximum coverage of users with different QoS requirements. IEEE Wireless Communications Letters, 7(1), 38–41. https://doi.org/10.1109/LWC.2017.2752161
Qin, J., Wei, Z., Qiu, C., & Feng, Z. (2019). Edgeprior placement algorithm for UAVmounted base stations. IEEE Wireless Communications and Networking Conference (WCNC), 2019, 1–6. https://doi.org/10.1109/WCNC.2019.8885992
Nouri, N., Abouei, J., Sepasian, A. R., Jaseemuddin, M., Anpalagan, A., & Plataniotis, K. N. (2022). Threedimensional multiUAV placement and resource allocation for energyefficient IoT communication. IEEE Internet of Things Journal, 9(3), 2134–2152. https://doi.org/10.1109/JIOT.2021.3091166
AlHourani, A., Kandeepan, S., & Lardner, S. (2014). Optimal LAP altitude for maximum coverage. IEEE Wireless Communications Letters, 3(6), 569–572. https://doi.org/10.1109/LWC.2014.2342736
Dorling, K., Heinrichs, J., Messier, G. G., & Magierowski, S. (2017). ‘Vehicle routing problems for drone delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), 70–85.
Islam, S. M. R., Avazov, N., Dobre, O. A., & Kwak, K. (2017). Powerdomain nonorthogonal multiple access (NOMA) in 5G systems: potentials and challenges. IEEE Communications Surveys & Tutorials, 19(2), 721–742. https://doi.org/10.1109/COMST.2016.2621116
Zhou, F., & Hu, R. Q. (2020). Computation efficiency maximization in wirelesspowered mobile edge computing networks. IEEE Transactions on Wireless Communications, 19(5), 3170–3184. https://doi.org/10.1109/TWC.2020.2970920
Dinkelbach, W. (1967). On nonlinear fractional programming. Management Science, 13, 492–498.
Zhang, X., Zhang, J., Xiong, J., Zhou, L., & Wei, J. (2020). Energyefficient multiUAVenabled multiaccess edge computing incorporating NOMA. IEEE Internet of Things Journal, 7(6), 5613–5627. https://doi.org/10.1109/JIOT.2020.2980035
Xiao, Z., Liu, H., Havyarimana, V., Li, T., & Wang, D. (2016). Analytical study on multitier 5G heterogeneous small cell networks: Coverage performance and energy efficiency. Sensors, 16(11), 1854.
Muñoz, O., PascualIserte, A., & Vidal, J. (2013). Joint allocation of radio and computational resources in wireless application offloading. Future Network & Mobile Summit, 2013, 1–10.
Acknowledgements
This work is supported by the Natural Science Foundation of Gansu Province, China (No. 20JR10RA182). This work is supported by the Gansu Provincial Key R&D ProgramIndustrial Project under Grant No. 23YFGA0062.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Xiangrui Guan and Jianbin Xue. The first draft of the manuscript was written by Xiangrui Guan and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
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Appendices
Appendix 1: The Derivation of Eq. (4)
From (3), for \(m = 1\), we obtain
For \(m = 2\), we have
by analogy, for \(m = M_{u}\), we have
Expand \(O_{u} [k] = \sum\limits_{m = 1}^{{M_{u} }} {O_{mu} [k]}\), we can obtain
Substitute (46), (47), and (48) into (49), we have
According to the properties of logarithmic functions, the total throughput of users to UAV u at the kth time slot can be expressed as
The Eq. (4) is achieved.
Appendix 2: Prove Lemma 1
For \(\min \left\{ {\tau B\log_{2} \left( {1 + \sum\limits_{m = 1}^{{M_{u} }} {\frac{{p_{mu} [k]g_{mu} [k]}}{{\sigma^{2} }}} } \right),\sum\limits_{m = 1}^{{M_{u} }} {\frac{{\tau f_{mu} [k]}}{{C_{u} }}} } \right\}\), there are three cases:

(i)
\(\tau B\log_{2} \left( {1 + \sum\limits_{m = 1}^{{M_{u} }} {\frac{{p_{mu} [k]g_{mu} [k]}}{{\sigma^{2} }}} } \right) > \sum\limits_{m = 1}^{{M_{u} }} {\frac{{\tau f_{mu} [k]}}{{C_{u} }}} ;\)

(ii)
\(\tau B\log_{2} \left( {1 + \sum\limits_{m = 1}^{{M_{u} }} {\frac{{p_{mu} [k]g_{mu} [k]}}{{\sigma^{2} }}} } \right) < \sum\limits_{m = 1}^{{M_{u} }} {\frac{{\tau f_{mu} [k]}}{{C_{u} }}} ;\)

(iii)
\(\tau B\log_{2} \left( {1 + \sum\limits_{m = 1}^{{M_{u} }} {\frac{{p_{mu} [k]g_{mu} [k]}}{{\sigma^{2} }}} } \right) = \sum\limits_{m = 1}^{{M_{u} }} {\frac{{\tau f_{mu} [k]}}{{C_{u} }}} .\)
Next, we would use contradiction analysis to prove that (iii) holds. We assume that there is another set of solutions besides the optimal solution. Let \(\Lambda\) be the objective function of problem P32, then we can define the corresponding objective functions for the solution \(\{ p_{mu} [k]^{*} ,f_{mu} [k]^{*} \}\) and \(\{ p_{mu} [k]]^{\prime},f_{mu} [k]^{\prime}\}\) as \(\Lambda^{*}\) and \(\Lambda^{\prime}\), respectively. Suppose (i) holds, and \(f_{mu} [k]^{*} = f_{mu} [k]{\prime}\), \(p_{mu} [k]^{*} \ge p_{mu} [k]{\prime}\), and \(\sum\nolimits_{u = 1}^{{\widehat{U}}} {\sum\nolimits_{m = 1}^{{M_{u} }} {p_{mu} [k]^{*} } } > \sum\nolimits_{u = 1}^{{\widehat{U}}} {\sum\nolimits_{m = 1}^{{M_{u} }} {p_{mu} [k]{\prime} } }\). Obviously, the objective function \(\Lambda\) decreases with \(p_{mu} [k]\) increasing, thus we have \(\Lambda^{*} < \Lambda^{\prime}\), the case (i) is not satisfied.
Similarly, suppose (ii) holds, and \(p_{mu} [k]^{*} = p_{mu} [k]{\prime}\), \(f_{mu} [k]^{*} \ge f_{mu} [k]{\prime}\). It is obvious that the objective function decreases with increasing, thus we have \(\Lambda^{*} < \Lambda{\prime}\), the case (ii) is not satisfied.
Based on the above analysis, case (iii) holds, and Lemma 1 is proved.
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Guan, X., Xue, J. EnergyEfficient Computing Offloading Based on MultiUAV Dispatch via NOMA in Emergency Communication Networks. Wireless Pers Commun 133, 199–226 (2023). https://doi.org/10.1007/s1127702310764y
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DOI: https://doi.org/10.1007/s1127702310764y