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Optimal Schedule of Mobile Edge Computing Under Imperfect CSI

  • Libo Jiao
  • Hao Yin
  • Yongqiang Lyu
  • Haojun Huang
  • Jiaqing Dong
  • Dongchao Guo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)

Abstract

Mobile edge computing (MEC), as a prospective computing paradigm, can augment the computation capabilities of mobile devices through offloading the complex computational tasks from simple devices to MEC-enabled base station (BS) covering them. However, how to achieve optimal schedule remains a problem due to various practical challenges including imperfect estimation of channel state information (CSI), stochastic tasks arrivals and time-varying channel situation. By using Lyapunov optimization theory and Lagrange dual decomposition technique, we propose an optimal dynamic offloading and resource scheduling (oDors) approach to maximize a system utility balancing throughput and fairness under imperfect estimation of CSI. We derive the analytical bounds for the time-averaged data queues length and system throughput achieved by the proposed approach which depends on the channel estimation error. We show that without prior knowledge of tasks arrivals and wireless channels, oDors achieves a system capacity which can arbitrarily approach the optimal system throughput. Simulation results confirm the theoretical analysis on the performance of oDors.

Keywords

Mobile edge computing Imperfect CSI Channel estimation Stochastic optimization 

Notes

Acknowledgment

This work is supported in part by the National Key Research and Development Program under Grant no. 2016YFB1000102, in part by the National Natural Science Foundation of China under Grant no. 61672318, 61631013, 31501081, and in part by the projects of Tsinghua National Laboratory for Information Science and Technology (TNList).

References

  1. 1.
    Zhao, P., Tian, H., Qin, C., Nie, G.: Energy-saving offloading by jointly allocating radio and computational resources for mobile edge computing. IEEE Access. 5, 11255–11268 (2017)CrossRefGoogle Scholar
  2. 2.
    Lyu, X., et al.: Optimal schedule of mobile edge computing for Internet of Things using partial information. IEEE J. Sel. Areas Commun. 35(11), 2606–2615 (2017)CrossRefGoogle Scholar
  3. 3.
    Guo, Y., Yang, Q., Liu, J., Kwak, K.S.: Cross-layer rate control and resource allocation in spectrum-sharing OFDMA small-cell networks with delay constraints. IEEE Trans. Veh. Technol. 66(5), 4133–4147 (2017)Google Scholar
  4. 4.
    Zhang, H., Jiang, C., Beaulieu, N.C., Chu, X., Wen, X., Tao, M.: Resource allocation in spectrum-sharing OFDMA femtocells with heterogeneous services. IEEE Trans. Commun. 62(7), 2366–2377 (2014)CrossRefGoogle Scholar
  5. 5.
    Wong, I.C., Evans, B.L.: Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge. IEEE Trans. Commun. 57(1), 232–241 (2009)CrossRefGoogle Scholar
  6. 6.
    Awad, M.K., Mahinthan, V., Mehrjoo, M., Shen, X., Mark, J.W.: A dual-decomposition-based resource allocation for OFDMA networks with imperfect CSI. IEEE Trans. Veh. Technol. 59(5), 2394–2403 (2010)CrossRefGoogle Scholar
  7. 7.
    Wang, J.B., et al.: Imperfect CSI-based joint resource allocation in multirelay OFDMA networks. IEEE Trans. Veh. Technol. 63(8), 3806–3817 (2014)CrossRefGoogle Scholar
  8. 8.
    Sheng, M., Li, Y., Wang, X., Li, J., Shi, Y.: Energy efficiency and delay tradeoff in device-to-device communications underlaying cellular networks. IEEE J. Sel. Areas Commun. 34(1), 92–106 (2016)CrossRefGoogle Scholar
  9. 9.
    Xiang, X., Lin, C., Chen, X.: Toward optimal admission control and resource allocation for LTE-A femtocell uplink. IEEE Trans. Veh. Technol. 64(7), 3247–3261 (2015)Google Scholar
  10. 10.
    Liu, F., Yang, Q., He, Q., Park, D., Kwak, K.S.: Dynamic power and subcarrier allocation for downlink OFDMA systems under imperfect CSI. Wirel. Netw., 1–14 (2017)Google Scholar
  11. 11.
    Adireddy, S., Tong, L., Viswanathan, H.: Optimal placement of training for frequency-selective block-fading channels. IEEE Trans. Inf. Theory. 48(8), 2338–2353 (2002)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wu, Y., Louie, R.H., McKay, M.R.: Analysis and design of wireless ad hoc networks with channel estimation errors. IEEE Trans. Signal Process. 61(6), 1447–1459 (2013)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Neely, M.J.: Stochastic network optimization with application to communication and queueing systems. Synth. Lect. Commun. Netw. 3(1), 1–211 (2010)CrossRefGoogle Scholar
  14. 14.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Libo Jiao
    • 1
  • Hao Yin
    • 1
  • Yongqiang Lyu
    • 1
  • Haojun Huang
    • 2
  • Jiaqing Dong
    • 1
  • Dongchao Guo
    • 1
  1. 1.Tsinghua UniversityBeijingChina
  2. 2.China University of GeosciencesWuhanChina

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