This chapter focuses on network selection for multiple user cases. Since users’ network selection decision determines the load distribution of networks, users’ decision-making is interacted. In particular, when heterogeneous user demand is considered, the solution of the optimal match between users and networks becomes a challenge. Centralized solutions could achieve a fair performance at a high optimization cost. Distributed solutions incur less cost but commonly result in low efficiency due to user competition. Different from centralized approaches or distributed approaches, we propose a local improvement algorithm, where networks that share users, called coupled network pairs (CNPs), cooperatively re-associate users with user demand awareness. Under a novel localized self-organization game formulation, we proved that the local improvement algorithm can achieve promising performance. To speed up the convergence of the algorithm, we further exploit the spatial independence among CNPs and propose an enhanced local improvement algorithm. Finally, simulation results indicate that the proposed algorithms achieve much better performance with relatively short convergence time, compared with three distributed algorithms.
This is a preview of subscription content, log in to check access.
Xue P, Gong P, Park J et al (2012) Radio resource management with proportional rate constraint in the heterogeneous networks. IEEE Trans Wirel Commun 11(3):1066–1075CrossRefGoogle Scholar
Prasad N, Zhang H, Zhu H et al (2014) Multiuser scheduling in the 3GPP LTE cellular uplink. IEEE Trans Mob Comput 13(1):130–145CrossRefGoogle Scholar
Keshavarz-Haddad A, Aryafar E, Wang M, Chiang M (2017) HetNets selection by clients: convergence, efficiency, and practicality. IEEE ACM Trans Netw 25(1):406–419CrossRefGoogle Scholar
Malanchini I, Cesana M, Gatti N (2013) Network selection and resource allocation games for wireless access networks. IEEE Trans Mob Comput 12(12):2427–2440CrossRefGoogle Scholar
Feng X, Gan X et al (2017) Distributed cell selection in heterogeneous wireless networks. Comput Commun 109:13–23CrossRefGoogle Scholar
Nguyen DD, Nguyen HX, White LB (2017) Reinforcement learning with network-assisted feedback for heterogeneous RAT selection. IEEE Trans Wirel Commun 16(9):6062–6076CrossRefGoogle Scholar
Du Z, Wu Q, Yang P, Yuhua Xu, Yao YD (2014) User-demand-aware wireless network selection: a localized cooperation approach. IEEE Trans Veh Technol 63(9):4492–4507CrossRefGoogle Scholar
Rakocevic V, Griffiths J, Cope G (2001) Performance analysis of bandwidth allocation schemes in multiservice IP networks using utility functions. In: Proceedings of the 17th international teletraffic congress (ITC)Google Scholar
Deb S, Nagaraj K, Srinivasan V (2011) MOTA: engineering an operator agnostic mobile service. MobiCom 2011Google Scholar
Arnborg S (1985) Efficient algorithms for combinatorial problems on graphs with bounded decomposability-a survey. BIT Numer 25(1):1–23MathSciNetCrossRefGoogle Scholar
Niyato D, Hossain E (2009) Dynamics of network selection in heterogeneous wireless networks: an evolutionary game approach. IEEE T Veh Technol 58(4):2008–2017CrossRefGoogle Scholar
Costa-Pérez X et al (2013) Latest trends in telecommunication standards. ACM Comput Commun RevGoogle Scholar