LTE Coverage Planning Based on Improved Grey Wolf Optimization

Automatic planning and dimension optimization of LTE is one of the crucial tasks in the mobile networking community. It is well known that this process is an NP-hard issue that requires huge computing resources. We also noticed that the actual proposed solutions are still inefficient in terms of scalability (handling a large number of eNodeBs) and runtime effectiveness. Moreover, SINR handling and variability of propagation loss models with respect to areas' types further complicate the coverage planning task. In this paper, we propose a swarm intelligence-based method for effectively placing and configuring the eNodeBs of an LTE network. In particular, we propose two variants of grey wolf optimizer (GWO), namely a discrete version of GWO (DGWO) and an improved version of GWO (IGWO) for LTE coverage planning. The improved version consists of an additional local search rule that allows for exploring regions closer to the promising solutions. The approaches are simulated on an urban area with many types of clutter. The IGWO technique had a coverage of 99.0% of 10 dB SINR rate and 95.1% of 12 dB SINR rate. The obtained results show that IGWO is more effective than the discrete one and other existing metaheuristics in terms of cost and coverage rates. More specifically, it ensures a coverage improvement (with respect to 10 dB SINR rate) of 10.6%, 10.5%, and 2.6 % in comparison to DGWO, Tabu search (TS), and discrete particle swarm optimization (DPSO) respectively.