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Interference Mitigation Methods for D2D Communication in 5G Network

  • Subhra S. SarmaEmail author
  • Ranjay Hazra
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1040)

Abstract

Interference poses to be a grave threat to wireless communication systems. Quest for higher data rate and higher energy efficiency has led to a paradigm shift. Since the inception of cellular technology in 1980, researchers are in search of a communication system which can exhibit high data rate, low latency, higher capacity, energy efficiency and spectrum efficiency. This led the researchers to explore 5G, operating in mmWave band (30–300 GHz) which is expected to support uncompressed video streaming, 3D telepresence, holographic communication and other multimedia applications. But all these technological advances suffer from a foe, i.e., cross-channel interference and co-channel interference. Thus, this paper chronologically discusses the various models applied to minimize or eradicate the problem of interference in 5G communication systems. Future research directions have also been discussed.

Keywords

Interference D2D communication 5G 

References

  1. 1.
    Swetha, G.D., Murthy, G.R.: Selective overlay mode operation for d2d communication in dense 5g cellular networks. In: Computers and Communications (ISCC) 2017 IEEE Symposium on IEEE, pp. 704–709 (2017)Google Scholar
  2. 2.
    Venugopal, K., Valenti, M.C., Heath, R.W.: Device-to-device millimeter wave communications: interference, coverage, rate, and finite topologies. IEEE Trans. Wirel. Commun. 15(9), 6175–6188 (2016)CrossRefGoogle Scholar
  3. 3.
    Yu, S., Ejaz, W., Guan, L., Anpalagan, A.: Resource allocation schemes in D2D communications: overview, classification, and challenges. Wirel. Pers. Commun. 96(1), 303–322 (2017)CrossRefGoogle Scholar
  4. 4.
    Zhang, R., Cheng, X., Yang, L., Jiao, B.: Interference graph-based resource allocation (InGRA) for D2D communications underlaying cellular networks. IEEE Trans. Veh. Technol. 64(8), 3844–3850 (2014)CrossRefGoogle Scholar
  5. 5.
    Qiao, J., Shen, X.S., Mark, J.W., Shen, Q., He, Y., Lei, L.: Enabling device-to-device communications in millimeter-wave 5G cellular networks. IEEE Commun. Mag. 53(1), 209–215 (2015)CrossRefGoogle Scholar
  6. 6.
    Wei, L., Hu, R.Q., Qian, Y., Wu, G.: Key elements to enable millimeter wave communications for 5G wireless systems. IEEE Wirel. Commun. 21(6), 136–143 (2014)CrossRefGoogle Scholar
  7. 7.
    Li, L., Niu, X., Chai, Y., Chen, L., Zhang, T., Cheng, D., Xia, H., Wang, J., Cui, T., You, X.: The path to 5G: mmWave aspects. J. Commun. Inf. Netw. 1(2), 1–18 (2016)CrossRefGoogle Scholar
  8. 8.
    Rappaport, T.S., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., Wong, G.N., Schulz, J.K., Samimi, M., Gutierrez, F.: Millimeter wave mobile communications for 5G cellular: it will work! IEEE Access 1, 335–349 (2013)CrossRefGoogle Scholar
  9. 9.
    Yin, R., Yu, G., Zhang, H., Zhang, Z., Li, G.Y.: Pricing-based interference coordination for D2D communications in cellular networks. IEEE Trans. Wirel. Commun. 14(3), 1519–1532 (2015)CrossRefGoogle Scholar
  10. 10.
    Yang, C., Li, J., Sheng, M., Anpalagan, A., Xiao, J.: Mean field game-theoretic framework for interference and energy-aware control in 5G ultra-dense networks. IEEE Wirel. Commun. 25(1), 114–121 (2017)CrossRefGoogle Scholar
  11. 11.
    Yang, C., Li, J., Semasinghe, P., Hossain, E., Perlaza, S.M., Han, Z.: Distributed interference and energy-aware power control for ultra-dense D2D networks: a mean field game. IEEE Trans. Wireless Commun. 16(2), 1205–1217 (2016)CrossRefGoogle Scholar
  12. 12.
    Mkiramweni, M.E., Yang, C., Li, J., Han, Z.: Game-theoretic approaches for wireless communications with unmanned aerial vehicles. IEEE Wirel. Communs. 25(6), 104–112 (2018)CrossRefGoogle Scholar
  13. 13.
    Gu, X., Zhang, X., Zhou, Z., Cheng, Y., Peng, J.: Game theory based interference control approach in 5g ultra-dense heterogeneous networks. In: Asia-Pacific Services Computing Conference, pp. 306–319. Springer, Cham (2016)Google Scholar
  14. 14.
    Zhou, Z., Dong, M., Ota, K., Shi, R., Liu, Z., Sato, T.: Game-theoretic approach to energy-efficient resource allocation in device-to-device underlay communications. IET Commun. 9(3), 375–385 (2015)CrossRefGoogle Scholar
  15. 15.
    Katsinis, G., Tsiropoulou, E.E., Papavassiliou, S.: Joint resource block and power allocation for interference management in device to device underlay cellular networks: a game theoretic approach. Mob. Netw. Appl. 22(3), 539–551 (2017)CrossRefGoogle Scholar
  16. 16.
    AlQerm, I., Shihada, B.: A cooperative online learning scheme for resource allocation in 5G systems. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–7 (2016)Google Scholar
  17. 17.
    Xu, J., Gu, X., Fan, Z.: D2D power control based on hierarchical extreme learning machine. In: 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–7 (2018)Google Scholar
  18. 18.
    Fan, Z., Gu, X., Nie, S., Chen, M.: D2D power control based on supervised and unsupervised learning. In: 2017 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 558–563 (2017)Google Scholar
  19. 19.
    Park, J., Choi, H.H., Lee, J.R.: Flocking-inspired transmission power control for fair resource allocation in vehicle-mounted mobile relay networks. IEEE Trans. Veh. Technol. 68(1), 754–764 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electronics & Instrumentation EngineeringNational Institute of Technology SilcharSilcharIndia

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