Swarm of Robotic Aerial Base Stations for mmWave Multi-Hop Backhauling (2312.10373v1)
Abstract: Robotic aerial base stations (RABSs) that are able to anchor at tall urban landforms are expected to bring further flexibility to millimeter-wave (mmWave) multi-hop backhaul networks in highly dense urban environments. In this paper, a swarm of RABSs are deployed to construct a dynamic mmWave backhaul network according to the traffic spatial distribution, and relocate their positions in subsequent time epochs according to the traffic temporal dynamic. The overall energy efficiency of the proposed framework is maximized by determining the RABS deployment, relocation and route formation under the channel capacity and hop constraints. The problem is formulated as a mixed-integer linear fractional programming (MILFP) and a two-stage method is developed to overcome the computational complexity. A wide set of numerical investigations reveal that compared to fixed small cells, only half as many RABSs are required to cover the same volume of traffic demand.
- V. Friderikos, “Airborne urban microcells with grasping end effectors: A game changer for 6G networks?” in Proc. IEEE Int. Medit. Conf. Commun. Netw. (MeditCom), 2021, pp. 336–341.
- Y. Liao and V. Friderikos, “Optimal deployment and operation of robotic aerial 6G small cells with grasping end effectors,” IEEE Trans. Veh. Technol., vol. 72, no. 9, pp. 12 248–12 260, 2023.
- Y. Liao, V. Friderikos, and H. Yanikomeroglu, “Robust deployment and resource allocation for robotic aerial base station enabled OFDM integrated sensing and communication,” IEEE Wireless Commun. Lett., vol. 12, no. 10, pp. 1766–1770, 2023.
- J. Lun et al., “Millimetre wave backhaul/fronthaul deployments for ultra-dense outdoor small cells,” in Proc. IEEE Wireless Commun. and Netw. Conf. Workshops (WCNCW), 2016, pp. 187–192.
- N. Palizban, S. Szyszkowicz, and H. Yanikomeroglu, “Automation of millimeter wave network planning for outdoor coverage in dense urban areas using wall-mounted base stations,” IEEE Wireless Commun. Lett., vol. 6, no. 2, pp. 206–209, 2017.
- J. McMenamy, A. Narbudowicz, K. Niotaki, and I. Macaluso, “Hop-constrained mmwave backhaul: Maximising the network flow,” IEEE Wireless Commun. Lett., vol. 9, no. 5, pp. 596–600, 2019.
- W. Pu, X. Li, J. Yuan, and X. Yang, “Traffic-oriented resource allocation for mmWave multi-hop backhaul networks,” IEEE Commun. Lett., vol. 22, no. 11, pp. 2330–2333, 2018.
- O. Semiari, W. Saad, M. Bennis, and Z. Dawy, “Inter-operator resource management for millimeter wave multi-hop backhaul networks,” IEEE Trans. Wireless Commun., vol. 16, no. 8, pp. 5258–5272, 2017.
- M. Gapeyenko et al., “Flexible and reliable UAV-assisted backhaul operation in 5G mmWave cellular networks,” IEEE J. Sel. Areas Commun., vol. 36, no. 11, pp. 2486–2496, 2018.
- U. Challita and W. Saad, “Network formation in the sky: Unmanned aerial vehicles for multi-hop wireless backhauling,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), 2017, pp. 1–6.
- C. Qiu et al., “Joint resource allocation, placement and user association of multiple UAV-mounted base stations with in-band wireless backhaul,” IEEE Wireless Commun. Lett., vol. 8, no. 6, pp. 1575–1578, 2019.
- Y. Zeng, J. Xu, and R. Zhang, “Energy minimization for wireless communication with rotary-wing UAV,” IEEE Trans. Wireless Commun., vol. 18, no. 4, pp. 2329–2345, 2019.
- S. Wang et al., “An approach for spatial-temporal traffic modeling in mobile cellular networks,” in Proc. 27th Int. Teletraffic Congr. (ITC), 2015, pp. 203–209.
- J. Feng, X. Chen, R. Gao, M. Zeng, and Y. Li, “DeepTP: An end-to-end neural network for mobile cellular traffic prediction,” IEEE Netw., vol. 32, no. 6, pp. 108–115, 2018.
- F. You et al., “Dinkelbach’s algorithm as an efficient method to solve a class of MINLP models for large-scale cyclic scheduling problems,” Comput. Chem. Eng., vol. 33, no. 11, pp. 1879–1889, 2009.
- M. E. Lübbecke and J. Desrosiers, “Selected topics in column generation,” Oper. Res., vol. 53, no. 6, pp. 1007–1023, 2005.