Self-organized Resource Allocation Based on Traffic Prediction for Load Imbalance in HetNets with NOMA

  • Jichen Jiang
  • Xi Li
  • Hong Ji
  • Heli Zhang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 234)


With the development of mobile communication technology, the data traffic of wireless cellular network has grown rapidly in the past decade. Because of the various bandwidth-eager applications and users movement, load imbalance has become an increasing severe problem, impacting the user experience and communication efficiency. Especially, it may lead to the degrading of resource utilization and network performance. In this paper, we investigate this problem and propose a self-organized resource allocation algorithm that allocates the resource to somewhere that the resource is needed to deal with the load imbalance problem. The typical heterogeneous network with non-orthogonal multiple access (NOMA) is discussed. A traffic prediction model is applied to the NOMA system. Then the self-organized resource allocation is formulated as a mixed integer non-linear programming (MINP) problem aiming at maximizing the overall throughput. The optimization problem is hard to tackle so we propose an algorithm to obtain a suboptimal solution via quantum-behaved particle swarm optimization (QPSO) algorithm. To evaluate how the resource is allocated according to the data traffic requirements, an indicator called evolved balance factor (EBF) is proposed to jointly consider the resource utility and the distribution of data traffic. Simulation results show that the proposed algorithm achieves a better performance in the overall throughput compared with exiting schemes.


Self-organized Resource allocation Traffic prediction NOMA Load imbalance 



This paper is jointly sponsored by the National Natural Science Foundation of China (Grant No. 61671088) and the National Natural Science Foundation of China for the Youth (Grant No. 61501047).


  1. 1.
    Datta, S.N., Kalyanasundaram, S.: Optimal power allocation and user selection in non-orthogonal multiple access systems. In: 2016 IEEE Wireless Communications and Networking Conference, pp. 1–6, April 2016Google Scholar
  2. 2.
    Dai, L., Wang, B., Yuan, Y., Han, S., I, C., Wang, Z.: Non-orthogonal multiple access for 5G: solutions, challenges, opportunities, and future research trends. IEEE Commun. Mag. 53(9), 74–81 (2015)CrossRefGoogle Scholar
  3. 3.
    Wang, Y., Ren, B., Sun, S., Kang, S., Yue, X.: Analysis of non-orthogonal multiple access for 5G. China Commun. 13(Supplement 2), 52–66 (2016)CrossRefGoogle Scholar
  4. 4.
    Fang, F., Zhang, H., Cheng, J., Leung, V.C.M.: Energy-efficient resource allocation for downlink non-orthogonal multiple access network. IEEE Trans. Commun. 64(9), 3722–3732 (2016)CrossRefGoogle Scholar
  5. 5.
    Liu, F., Mahonen, P., Petrova, M.: Proportional fairness-based user pairing and power allocation for non-orthogonal multiple access. In: 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pp. 1127–1131, August 2015Google Scholar
  6. 6.
    Lei, L., Yuan, D., Ho, C.K., Sun, S.: Joint optimization of power and channel allocation with non-orthogonal multiple access for 5G cellular systems. In: 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6, December 2015Google Scholar
  7. 7.
    Hagos, D.H., Kapitza, R.: Study on performance-centric offload strategies for LTE networks. In: 6th Joint IFIP Wireless and Mobile Networking Conference (WMNC), pp. 1–10, April 2013Google Scholar
  8. 8.
    Zhang, S., Gong, J., Zhou, S., Niu, Z.: How many small cells can be turned off via vertical offloading under a separation architecture? IEEE Trans. Wireless Commun. 14(10), 5440–5453 (2015)CrossRefGoogle Scholar
  9. 9.
    Jin, Z., Pan, Z., Liu, N., Li, W., Wu, J., Deng, T.: Dynamic pico switch on/off algorithm for energy saving in heterogeneous networks. In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), pp. 1–5, May 2015Google Scholar
  10. 10.
    Yao, C., Guo, J., Yang, C.: Achieving high throughput with predictive resource allocation. In: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp. 768–772, December 2016Google Scholar
  11. 11.
    Yu, H., Cheung, M.H., Huang, L., Huang, J.: Power-delay tradeoff with predictive scheduling in integrated cellular and Wi-Fi networks. IEEE J. Sel. Areas Commun. 34(4), 735–742 (2016)CrossRefGoogle Scholar
  12. 12.
    Liu, Y., Li, X., Ji, H., Zhang, H.: A multiple APs cooperation access scheme for energy efficiency in UUDN with NOMA. In: 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 1–5, May 2017Google Scholar
  13. 13.
    Sun, J., Fang, W., Wu, X., Palade, V., Xu, W.: Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection. Evol. Comput. 20(3), 349–393 (2012)CrossRefGoogle Scholar
  14. 14.
    Li, J., Wang, X., Yu, R., Jia, J.: The adaptive routing algorithm depending on the traffic prediction model in cognitive networks. In: 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, pp. 319–322, September 2010Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.Key Laboratory of Universal Wireless Communications, Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingPeople’s Republic of China

Personalised recommendations