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access icon free Decentralised resource allocation of position-based and full-duplex-based all-to-all broadcasting

© The Institution of Engineering and Technology
Received 17/11/2018, Accepted 07/06/2019, Revised 30/04/2019, Published 11/06/2019

The broadcasting services of vehicles in both vehicular networks and unmanned aerial vehicle (UAV) networks can be seen as the typical applications of all-to-all (A2A) scenario. In order to achieve efficient information broadcasting in A2A scenarios, this study proposes a two-stage resource allocation scheme. In the first stage, to improve the frequency spectrum reusability, the communication area is divided into as many non-overlapping square regions as possible based on the reliability requirements and power constraints of the vehicles, where the nonadjacent square regions share the same frequency spectrum. In the second stage, the vehicles belonging to the same region broadcast information with distributed time division multiplexing, and an improved coded slotted ALOHA scheme is proposed based on full-duplex, where each vehicle can detect whether its broadcasted information is successful. Thus, the number of retransmissions and collision probability can be reduced and therefore the system energy can be saved. Results indicate that the two-stage resource allocation scheme significantly improves the resource reusability while guaranteeing the communication reliability.

Inspec keywords: time division multiplexing; autonomous aerial vehicles; access protocols; resource allocation; mobile radio; probability

Other keywords: typical applications; two-stage resource allocation scheme; broadcasted information; nonadjacent square regions; ALOHA scheme; improved coded; frequency spectrum reusability; vehicular networks; unmanned aerial vehicle networks; broadcasting services; resource reusability; efficient information broadcasting; distributed time division multiplexing; decentralised resource allocation; nonoverlapping square regions; position-based

Subjects: Aerospace control; Mobile robots; Radio links and equipment; Protocols; Mobile radio systems

References

    1. 1)
      • 13. Zheng, K., Zheng, Q., Chatzimisios, P., et al: ‘Heterogeneous vehicular networking: a survey on architecture, challenges, and solutions’, IEEE Commun. Surv. Tutor., 2015, 17, (4), pp. 2377–2396.
    2. 2)
      • 31. Weber, S.P., Andrews, J.G., Yang, X.: ‘Transmission capacity of ad hoc networks with successive interference cancellation’, IEEE Trans. Inf. Theory, 2007, 53, (8), pp. 2799–2814.
    3. 3)
      • 16. Zeng, Y., Zhang, R., Lim, T.J.: ‘Wireless communications with unmanned aerial vehicles: opportunities and challenges’, IEEE Commun. Mag., 2016, 54, (5), pp. 36–42.
    4. 4)
      • 26. Luo, G., Li, J., Zhang, L., et al: ‘sdnMAC: a software-defined network inspired MAC protocol for cooperative safety in VANETs’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (6), pp. 2011–2024.
    5. 5)
      • 17. Lin, X., Huang, L., Guo, C., et al: ‘Energy-efficient resource allocation in TDMS-based wireless powered communication networks’, IEEE Commun. Lett., 2017, 21, (4), pp. 861–864.
    6. 6)
      • 10. Motlagh, N.H., Taleb, T., Arouk, O.: ‘Low-altitude unmanned aerial vehicles-based internet of things services: comprehensive survey and future perspectives’, IEEE Internet Things J., 2016, 3, (6), pp. 899–922.
    7. 7)
      • 24. Sun, Z., Xie, Y., Yuan, J., et al: ‘Coded slotted ALOHA for erasure channels: design and throughput analysis’, IEEE Trans. Commun., 2017, 65, (11), pp. 4817–4830.
    8. 8)
      • 11. Lei, L., Xu, H., Xiong, X., et al: ‘Joint computation offloading and multi-user scheduling using approximate dynamic programming in NB-IoT edge computing system’, IEEE Internet Things J., 2019, 6, (3), pp. 5345–5362.
    9. 9)
      • 25. Chen, S., Hu, J., Shi, Y., et al: ‘Vehicle-to-everything (v2x) services supported by LTE-based systems and 5G’, IEEE Commun. Std. Mag., 2017, 1, (2), pp. 70–76.
    10. 10)
      • 9. Ivanov, M., Brännström, F., Amat, A.G.i., et al: ‘Broadcast coded slotted ALOHA: a finite frame length analysis’, IEEE Trans. Commun., 2017, 65, (2), pp. 651–662.
    11. 11)
      • 14. Zheng, K., Hou, L., Meng, H., et al: ‘Soft-defined heterogeneous vehicular network: architecture and challenges’, IEEE Netw. Mag., 2016, 30, (4), pp. 72–80.
    12. 12)
      • 1. Nguyen, H.H., Jeong, H.Y.: ‘Mobility-adaptive beacon broadcast for vehicular cooperative safety-critical applications’, IEEE Trans. Intell. Transp. Syst., 2018, 19, (6), pp. 1996–2010.
    13. 13)
      • 29. Alshaer, H., Elmirghani, J.M.H.: ‘Road safety based on efficient vehicular broadcast communications’. Proc. IEEE Intelligent Vehicles Symp., Xi'an, China, June 2009, pp. 1155–1160.
    14. 14)
      • 7. Challita, U., Saad, W.: ‘Network formation in the sky: unmanned aerial vehicles for multi-hop wireless backhauling’. Proc. IEEE GLOBECOM, 2017, 4, (8), pp. 1–6.
    15. 15)
      • 27. Zhang, J., Lu, L., Sun, Y., et al: ‘PoC of SCMA-based uplink grant-free transmission in UCNC for 5G’, IEEE J. Sel. Areas Commun., 2017, 35, (6), pp. 1353–1362.
    16. 16)
      • 18. Zhao, L., Wang, F., Zheng, K., et al: ‘Joint optimization of communication and traffic efficiency in vehicular networks’, IEEE Trans. Veh. Technol., 2018, 68, (2), pp. 2014–2018.
    17. 17)
      • 33. Paolini, E., Liva, G., Chiani, M.: ‘Coded slotted ALOHA: a graph-based method for uncoordinated multiple access’, IEEE Trans. Inf. Theory, 2015, 61, (12), pp. 6815–6832.
    18. 18)
      • 23. Ivanov, M., Brännström, F., Amat, A.G.i., Popovski, P.: ‘All-to-all broadcast for vehicular networks based on coded slotted ALOHA’. Proc. IEEE Int. Conf. Communication Workshop, London, June 2015, pp. 2046–2050.
    19. 19)
      • 12. Cheng, X., Yang, L., Shen, X.: ‘D2D for intelligent transportation systems: a feasibility study’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 1784–1793.
    20. 20)
      • 15. Song, L., Niyato, D., Han, Z., et al: ‘Game-theoretic resource allocation methods for device-to-device communication’, IEEE Wirel. Commun., 2014, 21, (3), pp. 136–144.
    21. 21)
      • 3. Alshaer, H., Horlait, E.: ‘An optimized adaptive broadcast scheme for inter-vehicle communication’. Proc. 61st IEEE Vehicular Technology Conf. (VTC), Stockholm, Sweden, June 2005, pp. 2840–2844.
    22. 22)
      • 5. Sharma, V., Jayakody, D.N.K., You, I., et al: ‘Secure and efficient context-aware localization of drones in urban scenarios’, IEEE Commun. Mag., 2018, 56, (4), pp. 120–128.
    23. 23)
      • 32. Yang, C., Xia, B., Xie, W., et al: ‘Interference cancelation at receivers in cache-enabled wireless networks’, IEEE Trans. Veh. Technol., 2018, 67, (1), pp. 842–846.
    24. 24)
      • 22. Sun, W., Brannstrom, F., Ström, E.G.: ‘Network synchronization for mobile device-to-device systems’, IEEE Trans. Commun., 2017, 65, (3), pp. 1193–1206.
    25. 25)
      • 20. Botsov, M., Klügel, M., Kellerer, W., et al: ‘Location dependent resource allocation for mobile device-to-device communications’. Proc. IEEE Wireless Communications and Networking Conf. (WCNC), Istanbul, Turkey, April 2014, pp. 1679–1684.
    26. 26)
      • 8. Jawhar, I., Mohamed, N., Al-Jaroodi, J., et al: ‘Communication and networking of UAV-based systems: classification and associated architectures’, J. Netw. Comput. Appl., 2017, 84, pp. 93–108.
    27. 27)
      • 19. Liu, F., Zheng, K., Xiang, W., et al: ‘Design and performance analysis of an energy-efficient uplink carrier aggregation scheme’, IEEE J. Sel. Areas Commun., 2014, 32, (2), pp. 197–207.
    28. 28)
      • 30. Ndikumana, A., Tran, N.H., Hong, C.S.: ‘Deep Learning based caching for self-driving car in multi-access edge computing’. ArXiv preprint. arXiv:1810.01548.
    29. 29)
      • 34. Jakovetic, D., Bajovic, D., Vukobratovic, D., et al: ‘Cooperative slotted ALOHA for multi-base station systems’, IEEE Trans. Commun., 2015, 63, (4), pp. 1443–1456.
    30. 30)
      • 6. Yuan, X., Feng, Z., Xu, W., et al: ‘Capacity analysis of UAV communications: cases of random trajectories’, IEEE Trans. Veh. Technol., 2018, 67, (8), pp. 7564–7576.
    31. 31)
      • 2. Zheng, K., Meng, H., Chatzimisios, P., et al: ‘An SMDP-based resource allocation in vehicular cloud computing systems’, IEEE Trans. Ind. Electron., 2015, 62, (12), pp. 7920–7928.
    32. 32)
      • 21. Qashi, R.: ‘Analysis of packet throughput and delay in IEEE 802.11 WLANs with TCP traffic’, GSTF J. Comput., 2012, 1, (4), pp. 101–105.
    33. 33)
      • 28. Campolo, C., Molinaro, A., Berthet, A.O., et al: ‘Full-duplex radios for vehicular communications’, IEEE Commun. Mag., 2017, 55, (6), pp. 182–189.
    34. 34)
      • 4. Qazi, B.R., Alshaer, H., Elmirghani, J.: ‘Development of a motorway simulator for vehicular multimedia communications’. Proc. IEEE Vehicular Technology Conf. (VTC) Fall, Calgary, BC, Canada, September 2008, pp. 1–5.
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