Enhancing Cell Coverage and Capacity in LTE Heterogenous Network Using Intelligent Base Station

Abstract

The high data traffic demand by mobile users has tremendously increased in recent times, thus creating the need to further improve the capacity and coverage of already existing systems. Recent advancements in self-organizing and self driving Base stations are being exploited to bridge this gap. Although the paradigm have already evolved in 2G, 3G and 4G, the automation is realized by predefined policies, rather than interact with the environment to make smart decisions. In this paper, the eNB's of LTE networks have been designed in such a way that makes them self-aware, self-adaptable and intelligent using the Ant Colony Optimization algorithm. The ACOA was used to minimize the path trailed by the moving eNB's employing the SON capability of LTE systems. Three different scenarios were considered; results showed that in the first scenario, which is considered to be an ideal case, the optimized system improved the system throughput by 19.7% after 150s. The packet loss for this scenario was also reduced 38% at 150s. The call blocking probability outperformed the conventional system by 45.9% as at 120s and by 22.2% when the time was 150s. The second scenario considered analyzed the system performance when one cell is overloaded while a nearby cell is idle. Simulation results showed that the ACOA resulted to about 820% utilization of the idle cell, while each UE achieved an improved throughput compared to the conventional system. Results also showed that the throughput improvement provided by the ACOA in one of the eNB's was about 27Mbps when compared to the conventional system, and 55Mbps in another eNB when all the cells were loaded to congestion.