Modeling LTE-advanced cell capacity estimation using packet bundling and carrier aggregation

The increasing demand for mobile usage raises many challenges for service providers. Satisfying subscribers by providing a better quality of service (QoS) is one of the major concerns of an operator. One way to solve this problem is to adopt an efficient radio resource use mechanism. In this context, smart network planning management requires the estimation and enhancement of capacity in terms of the number of subscribers within a cell for better QoS provisioning through radio resource usage optimization. The model proposed in this article explicitly uses some long term evolution (LTE)-Advanced (LTE-A)-specific enhancements such as carrier aggregation (CA) and channel quality indication (CQI)-based resource allocation. Further, this study proposes a CQI-based clustering approach with packet bundling and CA to optimize radio resource utilization and enhance LTE-A cell capacity. LTE-A cell is logically divided into clusters such as the Silver class, Platinum class, Gold class, and Diamond class based on the CQI from the user end to eNodeB (eNB). Further, cell capacity (CCa) estimation algorithms are proposed in a simplistic scenario as well as in each cluster using packet bundling factor ω considering CA. From the result analysis, it is found that appropriate modulation and voice codecs can be used in appropriate clusters to enhance the cell capacity. Furthermore, it is observed that the packet bundling factor helps in improving the radio resource usage and thereby improving the capacity of a cell. The research work proposed in this article can be extended further to estimate the user capacity in the context of the 5th generation cellular network.