LTE-advanced cell capacity estimation model and algorithm for voice service

Abstract

Voice over long term evolution (VoLTE) in long term evolution-advanced (LTE-A) is gaining more and more popularity these days. However, the increasing demands of subscribers raise new challenges in wireless network to support large number of concurrent active users while maintaining the desired delay requirements. To realize the issue, this paper proposes an LTE-A capacity or eNodeB (eNB) capacity estimation model, i.e. defined as the number of simultaneous users for location-dependent diverse radio conditions. This proposed model incorporates different bundling mechanisms to utilize the radio resources efficiently. Then, optimal transmission parameters $\alpha$, $\alpha ^{'}$ using packet bundling, $\beta$, $\beta ^{'}$ using transmission time interval (TTI) bundling, and $\eta$, $\eta ^{'}$ for mixed radio condition is derived to enhance the accommodation of a large number of users within an LTE-A cell. There has been almost no consideration in the existing research on LTE-A capacity estimation based on these aspects. Therefore this paper presents novel algorithms based on the proposed models that enable capacity estimation and optimization under diverse radio conditions. The LTE-A cell capacity obtained using the proposed algorithms can be further used as an important parameter for designing call admission control (CAC) in packet-switched wireless networks.