Skip to main content
SpringerLink
Log in

Deep learning-based edge caching for multi-cluster heterogeneous networks

  • Multi-access Edge Computing Enabled Internet of Things
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. We consider a two-step content placement probability optimization. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. And we transform this optimization problem into a GP problem. At the following partial cache refreshing optimization, we take the time–space evolution into consideration and derive a convex optimization problem subjected to the cache capacity constraint and the backhaul limit constraint. We exploit the redundant information in different content popularity using the deep neural network to avoid the repeated calculation because of the change in content popularity distribution at different time slots. Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. Numerical results demonstrate the great approximation to the optimum and generalization ability.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Cisco (2017) Cisco visual networking index: global mobile data traffic forecast update: 2016–2021 white paper

  2. Araniti G, Orsino A, Militano L (2017) Context-aware information diffusion for alerting messages in 5G mobile social networks. IEEE Internet Things J 4(2):427–436

    Article  Google Scholar 

  3. Jalaeian B, Zhu R, Samani H (2016) An optimal cross-layer framework for cognitive radio network under interference temperature model. IEEE Syst J 10(1):293–301

    Article  Google Scholar 

  4. An J, Yang K, Wu J (2017) Achieve sustainable ultra-dense heterogeneous networks for 5G. IEEE Commun Mag 55(12):84–90

    Article  Google Scholar 

  5. Zhou L, Wu D, Dong Z (2017) When collaboration hugs intelligence: content delivery over ultra-dense networks. IEEE Commun Mag 55(12):91–95

    Article  Google Scholar 

  6. Wang X, Chen M, Taleb T, Ksentini A, Leung V (2014) Cache in the air: exploiting content caching and delivery techniques for 5G systems. IEEE Commun Mag 52(2):131–139

    Article  Google Scholar 

  7. Liu X, Zhu R, Jalaian B (2015) Dynamic spectrum access algorithm based on game theory in cognitive radio networks. Mob Netw Appl 20(6):817–827

    Article  Google Scholar 

  8. Tatar A, Amorim MDD, Fdida S (2014) A survey on predicting the popularity of web content. J Internet Serv Appl 5(1):8–28

    Article  Google Scholar 

  9. Chen Y, Ding M, Li J, Lin Z, Mao G, Hanzo L (2017) Probabilistic small-cell caching: performance analysis and optimization. IEEE Trans Veh Technol 66(5):4341–4354

    Google Scholar 

  10. Zhai D, Zhang R, Cai L, Li B, Jiang Y (2018) Energy-efficient user scheduling and power allocation for NOMA based wireless networks with massive IoT devices. IEEE Internet Things J 5:1857–1868

    Article  Google Scholar 

  11. Oh E, Krishnamachari B, Liu X (2011) Toward dynamic energy-efficient operation of cellular network infrastructure. IEEE Commun Mag 49(6):56–61

    Article  Google Scholar 

  12. Bryant RE, O’Hallaron DR (2015) Computer systems: a programmer’s perspective. Carnegie Mellon University, Pittsburgh

    Google Scholar 

  13. Avrachenkov K, Bai X, Goseling J (2017) Optimization of caching devices with geometric constraints. Perform Eval 113:68–82

    Article  Google Scholar 

  14. Weng CA, Psounis K (2015) Distributed caching and small cell cooperation for fast content delivery. In: The ACM international symposium. ACM, pp 127–136

  15. Che H, Tung Y, Wang Z (2015) Hierarchical web caching systems: modeling, design and experimental results. IEEE J Sel Areas Commun 20(7):1305–1314

    Article  Google Scholar 

  16. Zhu J, Song Y, Jiang D, Song H (2018) A new deep-Q-learning-based transmission scheduling mechanism for the cognitive internet of things. IEEE Internet Things J 5(4):2375–2385. https://doi.org/10.1109/JIOT.2017.2759728

    Article  Google Scholar 

  17. Chae SH, Wan C (2016) Caching placement in stochastic wireless caching helper networks: channel selection diversity via caching. IEEE Trans Wirel Commun 15(10):6626–6637

    Article  Google Scholar 

  18. Yang C, Yao Y, Xia B, Huang K, Xie W, Zhao Y (2017) Interference cancellation at receivers in cache-enabled wireless networks. IEEE Trans Veh Technol 67:842–846

    Article  Google Scholar 

  19. Yang C, Yao Y, Chen Z (2016) Analysis on cache-enabled wireless heterogeneous networks. IEEE Trans Wirel Commun 15(1):131–145

    Article  Google Scholar 

  20. Serbetci B, Goseling J (2017) On optimal geographical caching in heterogeneous cellular networks. In: Wireless communications and networking conference, IEEE, San Francisco, CA, USA

  21. Krishnan S, Dhillon HS (2016) Distributed caching in device-to-device networks: a stochastic geometry perspective. In: Asilomar conference on signals, systems and computers, 2015. IEEE, pp 1280–1284

  22. Wang Y, Tao X, Zhang X (2017) Cooperative caching placement in cache-enabled D2D underlaid cellular network. IEEE Commun Lett 5(5):1151–1154

    Article  Google Scholar 

  23. Rao J, Feng H, Yang C, Chen Z, Xia B (2016) Optimal caching placement for D2D assisted wireless caching networks. In: IEEE international conference on communications (ICC)

  24. Cui Y, Jiang D, Wu Y (2016) Analysis and optimization of caching and multicasting in large-scale cache-enabled wireless networks. IEEE Trans Wirel Commun 15(7):5101–5112

    Google Scholar 

  25. Cui Y, Jiang D (2017) Analysis and optimization of caching and multicasting in large-scale cache-enabled heterogeneous wireless networks. IEEE Trans Wirel Commun 16(1):250–264

    Article  MathSciNet  Google Scholar 

  26. Xu X, Tao M Analysis and optimization of probabilistic caching in multi-antenna small-cell networks. https://arxiv.org/pdf/1709.00664.pdf

  27. Machado K, Boukerche A, Cerqueira E et al (2017) A socially-aware in-network caching framework for the next generation of wireless networks. IEEE Commun Mag 55(12):38–43

    Article  Google Scholar 

  28. Garetto M, Leonardi E, Traverso, S (2015) Efficient analysis of caching strategies under dynamic content popularity. In: 2015 IEEE conference on computer communications (INFOCOM), pp 2263–2271

  29. Li S, Xu J, Schaar MVD (2016) Trend-aware video caching through online learning. IEEE Trans Multimedia 18(12):2503–2516

    Article  Google Scholar 

  30. Leconte M, Paschos G, Gkatzikis L et al (2016) Placing dynamic content in caches with small population. IEEE INFOCOM 2016:1–9

    Google Scholar 

  31. Bastug E, Bennis M, Debbah M (2015) A transfer learning approach for cache-enabled wireless networks. In: 2015 13th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks (WiOpt). Mumbai, India

  32. Sengupta A, Amuru SD, Tandon R (2014) Learning distributed caching strategies in small cell networks. In: International symposium on wireless communications systems. IEEE, pp 917–921

  33. Zhou L, Wu D, Chen J (2017) When computation hugs intelligence: content-aware data processing for industrial IoT. IEEE Internet Things J 5:1657–1666

    Article  Google Scholar 

  34. Song H, Srinivasan R, Sookoor T (2017) Smart cities: foundations, principles and applications. Wiley, Hoboken, pp 1–906. ISBN: 978-1-119-22639-0

  35. Song H, Rawat D, Sabina Jeschke (2016) Cyber-physical systems: foundations, principles and applications. Academic Press, Boston, pp 1–514. ISBN: 978-0-12-803801-7

  36. Liu W, Zhang J, Liang Z (2017) Content popularity prediction and caching for ICN: a deep learning approach with SDN. IEEE Access 6:5075–5089

    Article  Google Scholar 

  37. Tsai K, Wang L, Han Z (2018) Mobile social media networks caching with convolutional neural network. In: 2018 IEEE wireless communications and networking conference workshops (WCNCW)

  38. Lei L, You L, Dai G (2017) A deep learning approach for optimizing content delivering in cache-enabled HetNet. In: 2017 international symposium on wireless communication systems (ISWCS)

  39. Chen B, Yang C (2018) Caching policy for cache-enabled D2D communications by learning user preference. Inf Theory 66:1–32

    Google Scholar 

  40. Sadeghi A, Sheikholeslami F, Giannakis GB (2017) Optimal and scalable caching for 5G using reinforcement learning of space–time popularities. IEEE J Sel Top Signal Process 12(1):180–190

    Article  Google Scholar 

  41. Sadeghi A, Sheikholeslami F, Giannakis G (2018) Optimal dynamic proactive caching via reinforcement learning. In: 2018 IEEE 19th international workshop on signal processing advances in wireless communications (SPAWC)

  42. Saha C, Afshang M, Dhillon HS (2017) Poisson cluster process: bridging the gap between PPP and 3GPP HetNet models. In: Information theory and applications workshop (ITA), San Diego, CA, USA

  43. Wen J, Huang K, Yang S et al (2017) Cache-enabled heterogeneous cellular networks: optimal tier-level content placement. IEEE Trans Wirel Commun 16(9):5939–5952

    Article  Google Scholar 

  44. Zhu R, Zhang X, Liu X (2015) ERDT: Energy-efficient reliable decision transmission for cooperative spectrum sensing in industrial IoT. IEEE Access 3:2366–2378

    Article  Google Scholar 

  45. Francois B, Blaszczyszyn B (2009) Stochastic geometry and wireless networks: volume I theory. Now Publishers Inc., Hanover

    MATH  Google Scholar 

  46. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  47. Haykin S (2008) Neural networks and learning machines. Prentice Hall, Upper Saddle River

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No. 61871283), the Foundation of Pre-Research on Equipment of China (No. 61403120103) and the Joint Foundation of Pre-Research on Equipment from the Education Department of China (No. 6141A02022336).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Jiachen Yang, Jipeng Zhang & Chaofan Ma

  2. Department of Engineering, Jacksonville University, Jacksonville, FL, 32211, USA

    Huihui Wang

  3. Nankai University, No. 94 Weijin Road, Nankai District, Tianjin, 300071, China

    Juping Zhang

  4. Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough, LE113TU, UK

    Gan Zheng

Authors
  1. Jiachen Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jipeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaofan Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Huihui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Juping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jipeng Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, J., Zhang, J., Ma, C. et al. Deep learning-based edge caching for multi-cluster heterogeneous networks. Neural Comput & Applic 32, 15317–15328 (2020). https://doi.org/10.1007/s00521-019-04040-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04040-z

Keywords

Search

Navigation