An Improved Modelling of User Clustering for Small Cell Deployment in Heterogeneous Cellular Network

Users in practical cellular geographical areas are found to be non-uniformly distributed. Small cell (SC) deployments in heterogeneous user distribution in a cellular geographical area help to meet high data rate user demands for multimedia data communications in hot spots. SCs help to offload traffic burden from the macro cell (MC) base station, and also cater the data traffic need for the edge users where signal strength from the MC base station (BS) is very weak. For deployments of SCs along with the central MC BS (hence called HetNet) in such spatial heterogeneous user distribution, effective user grouping or clustering algorithm is required for appropriate and satisfactory service coverage. We call it service grouping or clustering of users to be put under a SC for data transmission and reception. It does not disturb the spatial positions of users in clustered non-uniform distribution. Efficient grouping or clustering of users and then deploying a SC at optimal location enhances the performance of the HetNet. It is found that the K-means algorithm used for such grouping of users to position SCs is not efficient. A novel and improved user grouping algorithm is proposed in this paper which performs much better compared to the k-means algorithm. The proposed algorithm of modelling of user clustering results in increase in the number of users under SCs, increase in more offloading of data traffic from MC BS thereby increasing data throughput of MC users. The algorithm also increases in the energy efficiencies of the SCs which is considered as one important performance metric. A doubly stochastic poison process (DSPP), also called Cox process, is assumed here for simulation of non-uniform user distributions. We consider Rayleigh distributed small scale fading model, large scale fading factor representing shadow fading, and users’ geographical distances from BSs to evaluate users’ data rates.