Dynamic pricing for load-balancing in user-centric joint call admission control of next-generation wireless networks

Abstract

Next-generation wireless networks (NGWN) will be heterogeneous, comprising of a number of radio access technologies (RATs) co-existing in the same geographical area. In NGWN, joint call admission control (JCAC) algorithms are required to select the most appropriate RAT for each incoming call. It is envisaged that these JCAC algorithms will be user-centric (i.e. will consider users' preferences in making RAT selection decisions) in order to enhance user satisfaction. However, user-centric JCAC algorithms can lead to highly unbalanced traffic load among the available RATs in NGWN because users act independently, and most of them may prefer to be connected through a particular RAT. Highly unbalanced traffic load in NGWN will result in high overall call blocking/dropping probability and poor radio result utilization. To address this problem, we propose dynamic pricing for balancing traffic load among available RATs in heterogeneous wireless networks where users' preferences are considered in making RAT selection decisions. By dynamically adjusting the service price in each of the available RATs, the proposed user-centric JCAC scheme evens out the unbalanced traffic load caused by independent users' preferences. The JCAC scheme uses fuzzy multiple attribute decision-making (MADM) technique to select the most appropriate RAT for each incoming call. We develop a Markov model to evaluate the overall call blocking/dropping probability and percentage load in each RAT in heterogeneous wireless networks. Performance of the proposed JCAC scheme is compared with that of a scheme that does not use dynamic pricing. Simulation results are given to show the effectiveness of the proposed JCAC scheme.