Base stations locations optimisation in an airport environment using genetic algorithms

Traditionally the locations of the base stations (BSs) in an indoor environment are optimised based on either traffic demand, signal to noise ratio (SNR), energy consumption or coverage area. However, considering only one of these parameters does not yield an optimum design, which is needed for efficient cost-effective planning with the required quality of service (QoS). Moreover, this problem becomes extremely challenging in highly dynamic environments such as airports, shopping malls and train stations where both spatial and temporal traffic variations are considerably high. Due to the continuous growth in international air traffic and the dynamic behaviour of passengers in an airport environment, providing reliable and cost-effective communication facilities to passengers and staff becomes even more difficult at different times and locations. Using data from Heathrow Terminal 4 (T4), we, in this paper, develop T4 passenger flow models which take both the spatial and temporal variations into account and help us accurately determine the traffic demand (TD), coverage area and path loss and thus outage and energy consumption. Moreover, we propose a multi-objective genetic algorithm (GA) which serves traffic demand and minimises both outage and energy consumption of the whole network. This eventually minimises the number of BSs while optimising their locations. The results reveal that only a few (i.e. 1-4) more base stations are required when we consider all three parameters together compared to the TD only. However our proposed GA, considering TD, outage and energy consumption, achieves lower outage and consumes almost 90% less transmission energy compared to the case of TD only while serving the same amount of traffic in such a dynamic environment.