Skip to main content

Energy-efficient design for mmWave-enabled NOMA-UAV networks

Abstract

Owing to the recent advances of non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave), these two technologies are combined in unmanned aerial vehicle (UAV) networks in this paper. However, energy efficiency has become a significant metric for UAVs owning to their limited energy. Thus, we aim to maximize the energy efficiency for mmWave-enabled NOMA-UAV networks by optimizing the UAV placement, hybrid precoding and power allocation. However, the optimization problem is complicated and intractable, which is decomposed into several sub-problems. First, we solve the UAV placement problem by approximating it into a convex one. Then, the hybrid precoding with user clustering is performed to better reap the multi-antenna gain. Particularly, three schemes are proposed, where the cluster head selection algorithm is adopted while considering different equivalent channels of users. Finally, the power allocation is optimized to maximize the energy efficiency, which is converted to convex and solved via an iterative algorithm. Simulation results are provided to evaluate the performance of the proposed schemes.

This is a preview of subscription content, access via your institution.

References

  1. Luo S X, Zhang Z S, Wang S, et al. Network for hypersonic UCAV swarms. Sci China Inf Sci, 2020, 63: 140311

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Zhao N, Lu W D, Sheng M, et al. UAV-assisted emergency networks in disasters. IEEE Wirel Commun, 2019, 26: 45–51

    Article  Google Scholar 

  4. Cheng F, Gui G, Zhao N, et al. UAV-relaying-assisted secure transmission with caching. IEEE Trans Commun, 2019, 67: 3140–3153

    Article  Google Scholar 

  5. You C S, Zhang R. 3D trajectory optimization in rician fading for UAV-enabled data harvesting. IEEE Trans Wirel Commun, 2019, 18: 3192–3207

    Article  Google Scholar 

  6. Wu Q Q, Xu J, Zhang R. Capacity characterization of UAV-enabled two-user broadcast channel. IEEE J Sel Areas Commun, 2018, 36: 1955–1971

    Article  Google Scholar 

  7. Lyu J B, Zeng Y, Zhang R, et al. Placement optimization of UAV-mounted mobile base stations. IEEE Commun Lett, 2017, 21: 604–607

    Article  Google Scholar 

  8. Sun Y, Xu D F, Ng D W K, et al. Optimal 3D-trajectory design and resource allocation for solar-powered UAV communication systems. IEEE Trans Commun, 2019, 67: 4281–4298

    Article  Google Scholar 

  9. Zhao N, Pang X W, Li Z, et al. Joint trajectory and precoding optimization for UAV-assisted NOMA networks. IEEE Trans Commun, 2019, 67: 3723–3735

    Article  Google Scholar 

  10. Zeng Y, Lyu J B, Zhang R. Cellular-connected UAV: potential, challenges, and promising technologies. IEEE Wirel Commun, 2019, 26: 120–127

    Article  Google Scholar 

  11. Amorim R, Nguyen H, Wigard J, et al. Measured uplink interference caused by aerial vehicles in LTE cellular networks. IEEE Wirel Commun Lett, 2018, 7: 958–961

    Article  Google Scholar 

  12. Xiao H L, Zhang Z S. Swarm intelligence approaches to power allocation for downlink base station cooperative system in dense cellular networks. Sci China Inf Sci, 2020, 63: 169302

    Article  Google Scholar 

  13. Mei W D, Wu Q Q, Zhang R. Cellular-connected UAV: uplink association, power control and interference coordination. IEEE Trans Wirel Commun, 2019, 18: 5380–5393

    Article  Google Scholar 

  14. Lu Z Y, Sun L L, Zhang S, et al. Optimal power allocation for secure directional modulation networks with a full-duplex UAV user. Sci China Inf Sci, 2019, 62: 080304

    Article  Google Scholar 

  15. Mei W D, Zhang R. Uplink cooperative NOMA for cellular-connected UAV. IEEE J Sel Top Signal Process, 2019, 13: 644–656

    Article  Google Scholar 

  16. Ding Z G, Lei X F, Karagiannidis G K, et al. A survey on non-orthogonal multiple access for 5G networks: research challenges and future trends. IEEE J Sel Areas Commun, 2017, 35: 2181–2195

    Article  Google Scholar 

  17. Liu Y W, Qin Z J, Cai Y L, et al. UAV communications based on non-orthogonal multiple access. IEEE Wirel Commun, 2019, 26: 52–57

    Article  Google Scholar 

  18. Zhao N, Li Y X, Zhang S, et al. Security enhancement for NOMA-UAV networks. IEEE Trans Veh Technol, 2020, 69: 3994–4005

    Article  Google Scholar 

  19. Sohail M F, Leow C Y, Won S. Non-orthogonal multiple access for unmanned aerial vehicle assisted communication. IEEE Access, 2018, 6: 22716–22727

    Article  Google Scholar 

  20. Pang X W, Gui G, Zhao N, et al. Uplink precoding optimization for NOMA cellular-connected UAV networks. IEEE Trans Commun, 2020, 68: 1271–1283

    Article  Google Scholar 

  21. Xiao Z Y, He T, Xia P F, et al. Hierarchical codebook design for beamforming training in millimeter-wave communication. IEEE Trans Wirel Commun, 2016, 15: 3380–3392

    Article  Google Scholar 

  22. Xing C W, Zhao X, Xu W, et al. A framework on hybrid MIMO transceiver design based on matrix-monotonic optimization. IEEE Trans Signal Process, 2019, 67: 3531–3546

    MathSciNet  Article  Google Scholar 

  23. Shen W Q, Bu X Y, Gao X Y, et al. Beamspace precoding and beam selection for wideband millimeter-wave MIMO relying on lens antenna arrays. IEEE Trans Signal Process, 2019, 67: 6301–6313

    MathSciNet  Article  Google Scholar 

  24. Wang B C, Dai L L, Wang Z C, et al. Spectrum and energy-efficient beamspace MIMO-NOMA for millimeter-wave communications using lens antenna array. IEEE J Sel Areas Commun, 2017, 35: 2370–2382

    Article  Google Scholar 

  25. Zhang C Y, Zhang W Z, Wang W, et al. Research challenges and opportunities of UAV millimeter-wave communications. IEEE Wirel Commun, 2019, 26: 58–62

    Article  Google Scholar 

  26. Xiao Z Y, Xia P F, Xia X G. Enabling UAV cellular with millimeter-wave communication: potentials and approaches. IEEE Commun Mag, 2016, 54: 66–73

    Article  Google Scholar 

  27. Zhao J W, Gao F F, Kuang L L, et al. Channel tracking with flight control system for UAV mmWave MIMO communications. IEEE Commun Lett, 2018, 22: 1224–1227

    Article  Google Scholar 

  28. Rupasinghe N, Yapici Y, Guvenc I, et al. Angle feedback for NOMA transmission in mmWave drone networks. IEEE J Sel Top Signal Process, 2019, 13: 628–643

    Article  Google Scholar 

  29. Xu D F, Sun Y, Ng D W K, et al. Multiuser MISO UAV communications in uncertain environments with no-fly zones: robust trajectory and resource allocation design. IEEE Trans Commun, 2020, 68: 3153–3172

    Article  Google Scholar 

  30. Shakhatreh H, Khreishah A. Optimal placement of a UAV to maximize the lifetime of wireless devices. In: Proceedings of 2018 14th International Wireless Communications & Mobile Computing Conference, Limassol, 2018. 1225–1230

  31. Dinkelbach W. On nonlinear fractional programming. Manage Sci, 1967, 13: 492–498

    MathSciNet  Article  Google Scholar 

  32. Kha H H, Tuan H D, Nguyen H H. Fast global optimal power allocation in wireless networks by local D.C. programming. IEEE Trans Wirel Commun, 2012, 11: 510–515

    Article  Google Scholar 

  33. Sediq A B, Gohary R H, Schoenen R, et al. Optimal tradeoff between sum-rate efficiency and Jain’s fairness index in resource allocation. IEEE Trans Wirel Commun, 2013, 12: 3496–3509

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61871065, 61971194).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nan Zhao.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pang, X., Tang, J., Zhao, N. et al. Energy-efficient design for mmWave-enabled NOMA-UAV networks. Sci. China Inf. Sci. 64, 140303 (2021). https://doi.org/10.1007/s11432-020-2985-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2985-8

Keywords

  • energy efficiency
  • hybrid precoding
  • millimeter-wave
  • non-orthogonal multiple access
  • placement optimization
  • power allocation
  • unmanned aerial vehicle