The mobile communication system has become increasingly complicated with the dramatic growth of user's requirement in quality of service (QoS). The high fluctuation of traffic data makes conventional cell association schemes difficult to guarantee satisfactory service in accord with traffic demand. In this study, the authors propose a novel QoS-aware cell association scheme in a heterogeneous network. Utilising traffic prediction achieved by support vector regression, the user can decide the best cell according to the future traffic demand. The authors aim at maximising the total throughput with consideration of channel gains and blocking probability in different cells and formulate the cell association as a binary combinatorial optimisation problem. Since users are selfish for their own benefits without the global information, the authors turn this problem into a game theoretical framework. To obtain the Nash equilibrium with low computation complexity, a heuristic dynamic selection algorithm is proposed by updating selection probability which enables each user to associate with the best cell independently. Numerical simulation results show that the proposed algorithm achieves a remarkable throughput gain. The number of satisfied users increases substantially under the different density of users compared with conventional cell association schemes.

References

1. 1)
  - 20. Sastry, P.S., Phansalkar, V.V., Thathachar, M.A.L.: ‘Decentralized learning of Nash equilibria in multi-person stochastic games with incomplete information’, IEEE Trans. Syst. Man. Cybern., 1994, 24, (5), pp. 769–777.
2. 2)
  - 1. ‘Cisco visual networking index: forecast and methodology, 2014–2019 white paper’, http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white-paper-c11-481360.pdf, accessed 26 May 2015.
3. 3)
  - 13. Wang, H., Ding, L., Wu, P., et al: ‘Dynamic load balancing and throughput optimization in 3GPP LTE networks’. ACM Int. Wireless Communications and Mobile Computing Conf., Caen, June 2010, pp. 939–943.
4. 4)
  - 14. Wang, L., Chen, W., Li, J.: ‘Congestion aware dynamic user association in heterogeneous cellular network: a stochastic decision approach’. IEEE Int. Conf. on Communications (ICC), Sydney, June 2014, pp. 2636–2640.
5. 5)
  - 17. Fudenberg, D., Tirole, J.: ‘Game theory’ (MIT Press, Cambridge, MA, 1991).
6. 6)
  - 11. Mlika, Z., Driouch, E., Ajib, W., et al: ‘A completely distributed algorithm for user association in HetSNets’. IEEE Int. Conf. on Communications (ICC), London, June 2015, pp. 2172–2177.
7. 7)
  - 4. Zhou, T., Huang, Y., Fan, L., et al: ‘Load-aware user association with quality of service support in heterogeneous cellular networks’, IET Commun., 2015, 9, (4), pp. 494–500.
8. 8)
  - 21. ‘Raw data – measuring broadband America 2015’, https://www.fcc.gov/reports-research/reports/measuring-broadband-america/raw-data-measuring-broadband-america-2015, accessed 30 December 2015.
9. 9)
  - 8. Oh, J., Han, Y.: ‘Cell selection for range expansion with almost blank subframe in heterogeneous networks’. IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications (PIMRC), Sydney, September 2012, pp. 653–657.
10. 10)
  - 5. Chou, G.T., Liu, K.H.S., Su, S.L.: ‘Load-based cell association for load balancing in heterogeneous cellular networks’. IEEE Int. Symp. on Personal, Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, August 2015, pp. 1681–1686.
11. 11)
  - 18. Moulin, H.: ‘Dominance solvability and cournot stability’, Math. Soc. Sci., 1984, 7, (1), pp. 83–102.
12. 12)
  - 15. Vapnic, V.N.: ‘The nature of statistical learning theory’ (Springer-Verlag, New York, 1995).
13. 13)
  - 19. Marden, J.R., Shamma, J.S.: ‘Revisiting log-linear learning: asynchrony, completeness and payoff-based implementation’. Allerton Conf. on Communication, Control, and Computing, Allerton, September 2010, pp. 1171–1172.
14. 14)
  - 7. Hajmohammad, S., Elbiaze, H.: ‘Unlicensed spectrum splitting between femtocell and WiFi’. IEEE Int. Conf. on Communications (ICC), Budapest, June 2013, pp. 1883–1888.
15. 15)
  - 3. Jo, H.S., Sang, Y.J., Xia, P., et al: ‘Heterogeneous cellular networks with flexible cell association: a comprehensive downlink SINR analysis’, IEEE Trans. Wirel. Commun., 2012, 11, (10), pp. 3484–3495.
16. 16)
  - 2. Andrews, J.G., Singh, S., Ye, Q., et al: ‘An overview of load balancing in hetnets: old myths and open problems’, IEEE Wirel. Commun., 2014, 12, (6), pp. 2706–2716.
17. 17)
  - 16. Elgendy, O.A., Ismail, M.H., Elsayed, K.: ‘On the relay placement problem in a multi-cell LTE-advanced system with co-channel interference’. IEEE Int. Conf. on Wireless and Mobile Computing, Networking and Communications (WiMob), Barcelona, October 2012, pp. 300–307.
18. 18)
  - 6. Yang, B., Mao, G., Ge, X., et al: ‘A new cell association scheme in heterogeneous networks’. IEEE Int. Conf. on Communications (ICC), London, June 2015, pp. 5627–5632.
19. 19)
  - 12. Zhou, T., Huang, Y., Yang, L.: ‘User association with jointly maximising downlink sum rate and minimising uplink sum power for heterogeneous cellular networks’, IET Commun., 2015, 9, (2), pp. 300–308.
20. 20)
  - 9. Jia, Y., Zhao, M., Zhou, W.: ‘Joint user association and eICIC for max–min fairness in HetNets’, IEEE Commun. Lett., 2016, 20, (3), pp. 546–549.
21. 21)
  - 10. Zhou, X., Feng, S., Han, Z., et al: ‘Distributed user association and interference coordination in HetNets using Stackelberg game’. IEEE Int. Conf. on Communications (ICC), London, June 2015, pp. 6431–6436.

QoS-aware cell association based on traffic prediction in heterogeneous cellular networks

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