Skip to main content

Advertisement

SpringerLink
Log in

A resource allocation scheme for D2D communications with unknown channel state information

  • Published:
Peer-to-Peer Networking and Applications Aims and scope Submit manuscript

Abstract

Device-to-device (D2D) communication is a dramatic departure from the conventional cellular architecture as it allows for user equipment (UE) in a cellular network to act as transmission relays without the involvement of network infrastructure, effectively realizing a co-existing massive ad-hoc network. While a hybrid D2D-cellular architecture can enhance the spectral efficiency, resource allocation in such a two-tier system is faced with unique challenges to ensure minimal impact on the performance of existing cellular users (CUs). In this paper, we address the D2D resource allocation problem under unknown channel state information (CSI). CSI-free schemes are substantial given that certain practical limitations (e.g., finite CSI feedback delay) make the knowledge of instantaneous CSI impossible in systems with fading channels. Also, statistical CSI needs pre-deployment training/model extraction and becomes invalid as the operating environment changes. Our proposed D2D resource optimization scheme for unknown CSI settings is applicable to a sub-class of scenarios which can be framed as the graph-theoretic weighted bipartite matching (WBM) problem. In the absence of CSI, WBM becomes a combinatorial problem with unknown random edge weights and generally unknown distributions. To compensate for this lack of knowledge, we resort to the formalism of combinatorial multi-armed bandit (CMAB) from machine learning theory. A CMAB-based network controller is able to reach optimal D2D configuration by following efficient rules which strike a balance between exploring alternative matchings and exploiting the gradually gained experience. We formulate and numerically experiment with two problems as “proof of concept”, namely (i) D2D relay selection, and (ii) joint mode selection and channel allocation. We also compare our results with existing/baseline schemes with various flavors of CSI availability assumptions, including perfect instantaneous CSI, perfect statistical CSI, erroneous CSI, and static CSI.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data Availability

Data sharing not applicable – no new data generated.

References

  1. Ahmad M, Azam M, Naeem M, Iqbal M, Anpalagan A, Haneef M (2017) Resource management in d2d communication: An optimization perspective. J Netw Comput Appl 93

  2. Alamouti SM, Sharafat AR (2014) Resource allocation for energy-efficient device-to-device communication in 4g networks

  3. Alamouti SM, Sharafat AR (2016) Resource allocation for device-to-device communications in multi-cell lte-advanced wireless networks with c-ran architecture. in ITU Kaleidoscope: ICTs for a Sustainable World (ITU WT). IEEE pp. 1–8

  4. Ansari RI, Hassan SA, Chrysostomou C (2016) Energy efficient relay selection in multi-hop d2d networks. In 2016 International Wireless Communications and Mobile Computing Conference (IWCMC) pp. 620–625

  5. Burghal D, Molisch AF (2013) Location aware training scheme for d2d networks. In 2013 Asilomar Conference on Signals, Systems and Computers pp. 1705–1708

  6. Cao Y, Jiang T, Wang C (2015) Cooperative device-to-device communications in cellular networks. IEEE Wirel Commun 22(3):124–129

    Article  Google Scholar 

  7. Chen N, Tian H, Wang Z (2014) Resource allocation for intra-cluster d2d communications based on kuhn-munkres algorithm

  8. Chen W, Wang L, Zhao H, Zheng K (2020) Combinatorial semi-bandit in the non-stationary environment. CoRR abs/2002.03580

  9. Chen W, Wang Y, Yuan Y (2013) Combinatorial multi-armed bandit: General framework and applications

  10. Chen W, Wang Y, Yuan Y, Wang Q (2016) Combinatorial multi-armed bandit and its extension to probabilistically triggered arms. J Mach 17

  11. Chithra R, Bestak R, Patra SK (2015) Hungarian method based joint transmission mode and relay selection in device-to-device communication 8:261–268

  12. Feng D, Lu L, Yuan-Wu Y, Li GY, Feng G, Li S (2013) Device-to-device communications underlaying cellular networks. IEEE Trans Commun 61(8):3541–3551

    Article  Google Scholar 

  13. Gai Y, Krishnamachari B, Jain R (2012) Combinatorial network optimization with unknown variables: Multi-armed bandits with linear rewards and individual observations. IEEE/ACM Trans Netw 20(5):1466–1478

    Article  Google Scholar 

  14. Gandotra P, Jha RK (2016) Device-to-device communication in cellular networks: A survey. J Netw Comput Appl 71:99–117

    Article  Google Scholar 

  15. Gao P, Yang Z, Pei L, Du J, Chen M (2018) Energy-efficient mode selection and resource allocation for relay-assisted d2d communications

  16. Grover A, Markov T, Attia P, Jin N, Perkins N, Cheong B, Chen M, Yang Z, Harris S, Chueh W, Ermon S (2018) Best arm identification in multi-armed bandits with delayed feedback. In Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics 84:833–842

    Google Scholar 

  17. Han J, Cui Q, Yang C, Tao X (2014) Bipartite matching approach to optimal resource allocation in device to device underlaying cellular network. Electron Lett 50:3

    Article  Google Scholar 

  18. He R, Ai B, Stüber GL, Zhong Z (2017) Non-stationary mobile-to-mobile channel modeling using the gauss-markov mobility model. In 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP) pp. 1–6

  19. He Y, Zhang Z, Yu FR, Zhao N, Yin H, Leung VCM, Zhang Y (2017) Deep-reinforcement-learning-based optimization for cache-enabled opportunistic interference alignment wireless networks. IEEE Trans Veh Technol 66(11):10433–10445

    Article  Google Scholar 

  20. Hoang TD, Le LB, Le-Ngoc T (2016) Joint mode selection and resource allocation for relay-based d2d communications. IEEE Commun Lett 21(2):398–401

    Article  Google Scholar 

  21. Jameel F, Hamid Z, Jabeen F, Zeadally S, Javed MA (2018) A survey of device-to-device communications: Research issues and challenges. IEEE Commun Surv Tutorials 20(3):2133–2168

    Article  Google Scholar 

  22. Jang J, Yang HJ (2020) Deep reinforcement learning-based resource allocation and power control in small cells with limited information exchange. IEEE Trans Veh Technol 69:11

    Article  Google Scholar 

  23. Jayakumar S, Nandakumar S (2021) A review on resource allocation techniques in d2d communication for 5g and b5g technology. Peer-to-Peer Netw Appl 14:1

    Article  Google Scholar 

  24. Kim T, Dong M (2014) An iterative hungarian method to joint relay selection and resource allocation for d2d communications. IEEE Wirel Commun Lett 3(6):625–628

    Article  Google Scholar 

  25. Kuhn HW (1955) The hungarian method for the assignment problem. Nav 2(1):83–97

    MathSciNet  MATH  Google Scholar 

  26. Li R, Hong P, Xue K, Zhang M, Yang T (2020) Energy-efficient resource allocation for high-rate underlay d2d communications with statistical csi: A one-to-many strategy. IEEE Trans Veh Technol 69:4

    Article  Google Scholar 

  27. Li X, Zhang W, Zhang H, Li W (2016) A combining call admission control and power control scheme for d2d communications underlaying cellular networks. China Commun 13:10

    Google Scholar 

  28. Li Y, Zhang Z, Wang H, Yang Q (2018) Sers: Social-aware energy-efficient relay selection in d2d communications. IEEE Trans Veh Technol 67(6):5331–5345

    Article  Google Scholar 

  29. Liang L, Li GY, Xu W (2017) Resource allocation for d2d-enabled vehicular communications. IEEE Trans Commun 65:7

    Google Scholar 

  30. Liu Z, Li X, Yuan Y, Guan X (2020) Power control of d2d communication based on quality of service assurance under imperfect channel information. Peer-to-Peer Netw Appl 13:5

    Google Scholar 

  31. Luo L, Chai R (2020) Cost-efficient uav deployment for content fetching in cellular d2d systems. in IEEE Vehicular Technology Conference

  32. Ma B, Shah-Mansouri H, Wong VWS (2016) A matching approach for power efficient relay selection in full duplex d2d networks. In IEEE International Conference on Communications ICC pp. 1–6

  33. Ma R, Chang Y-J, Chen H-H, Chiu C-Y (2017) On relay selection schemes for relay-assisted d2d communications in lte-a systems. IEEE Trans Veh Technol 66(9):8303–8314

    Article  Google Scholar 

  34. Ma, R., Xia, N., Chen, H.-H., Chiu, C.-Y., and Yang, C.-S. Mode selection, radio resource allocation, and power coordination in d2d communications. IEEE Wirel. Commun. 24, 3 (2017), 112–121

    Article  Google Scholar 

  35. Maghsudi S, Stanczak S (2015) Hybrid centralized-distributed resource allocation for device-to-device communication underlaying cellular networks. IEEE Trans Veh Technol 65(4):2481–2495

    Article  Google Scholar 

  36. Moussaid A, Jaafar W, Ajib W, Elbiaze H (2018) Deep reinforcement learning-based data transmission for d2d communications. In 2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) pp. 1–7

  37. Omri A, Hasna MO (2018) A distance-based mode selection scheme for d2d-enabled networks with mobility. IEEE Trans Wirel Commun 17:7

    Google Scholar 

  38. Qiu Y, Ji Z, Zhu Y, Meng G, Xie G (2018) Joint mode selection and power adaptation for d2d communication with reinforcement learning

  39. Rappaport TS (2001) Wireless Communications: Principles and Practice. Prentice Hall, Communications Engineering and Emerging Technologies Series

    MATH  Google Scholar 

  40. Shamganth K, Sibley MJ (2017) A survey on relay selection in cooperative device-to-device (d2d) communication for 5g cellular networks. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) pp. 42–46

  41. Sklar B (1997) Rayleigh fading channels in mobile digital communication systems part i: Characterization. IEEE Commun Mag 35:9

    Google Scholar 

  42. Sutton RS, Barto AG (1998) Reinforcement learning: An introduction. IEEE Trans Neural Netw 9:5

    Article  Google Scholar 

  43. Tekin C, Liu M (2015) Online learning methods for networking. Found Trends Netw 8(4):281–409

    Article  Google Scholar 

  44. Wang R, Zhang J, Song SH, Letaief KB (2016) Optimal qos-aware channel assignment in d2d communications with partial csi. IEEE Trans Wirel Commun 15(11):7594–7609

    Article  Google Scholar 

  45. Xu X, Zhang Y, Sun Z, Hong Y, Tao X (2016) Analytical modeling of mode selection for moving d2d-enabled cellular networks. IEEE Commun Lett 20(6):1203–1206

    Article  Google Scholar 

  46. Zhang S, Peng Y (2020) D2d communication relay selection algorithm based on game theory. Procedia Computer Science 166:563–569. Proceedings of the 3rd International Conference on Mechatronics and Intelligent Robotics (ICMIR-2019)

Download references

Author information

Authors and Affiliations

  1. Center of Excellence in Future Networks, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Vesal Hakami & Ziba Arefinezhad

  2. Faran Mehre Danesh e-learning Higher Education Institute, Tehran, Iran

    Hadi Barghi

  3. Computer Engineering Department, Yazd University, Yazd, Iran

    Seyedakbar Mostafavi

  4. School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

    Ziba Arefinezhad

Authors
  1. Vesal Hakami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hadi Barghi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seyedakbar Mostafavi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ziba Arefinezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vesal Hakami.

Ethics declarations

Ethics approval

This article does not include any studies with human participants or animals performed by any of the authors.

Conflicts of interest

All authors declare that they have no conflict of interest.

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

Hakami, V., Barghi, H., Mostafavi, S. et al. A resource allocation scheme for D2D communications with unknown channel state information. Peer-to-Peer Netw. Appl. 15, 1189–1213 (2022). https://doi.org/10.1007/s12083-021-01265-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12083-021-01265-5

Keywords

Search

Navigation