Capacity Demand based Multiobjective Optimal Small Cell Placement under Realistic Deployment Scenario

To address operator's capacity challenges, efficient demand driven deployment of 4G and 5G small cells considering multiple targets of the operator is very important. To that end, multiobjective optimized small cell planning methodology has recently been proposed. Yet, the method does not provide straightforward mechanism to consider operator's spatial capacity demand that can be obtained from operator's existing network capacity and data market targets. In this work, we present a capacity demand based multiobjective optimal small cell placement method and its performance analysis for exemplary service area of Addis Ababa. To formulate spatial capacity demand, we use spatial user and traffic distribution data from network management system of existing network. As an input for Matlab based network simulation multiobjective optimization, propagation is computed using deterministic ray tracing model over 3D building and terrain map of the service area. The multiobjective optimization is performed for network capacity and cost objectives in this work using a Genetic Algorithm. Results show that the multiobjective placement method presents optimal small cell topologies that meet operator's spatial capacity demand while optimizing aggregate network capacity and cost. The optimization reduced 185-network topology to 45–130 optimal network topologies while significantly improving network capacity. The capacity improvement shows significant user throughput improvement effect. For instance, the 130-topology provides 57% gain in 90%-ile user throughput compared to the not optimized 185-topology.