Three-dimensional deployment algorithm of UAV base stations in a heterogeneous network

Abstract

A few years back, terrestrial base stations were able to effectively and efficiently meet the users’ network needs. Network operators would easily deploy new base stations to enhance the performance of wireless communication during certain scenarios such as base station breakdown, network congestion, low data rates and so on. As technology has continued to advance, the demand for higher data rates has rapidly increased due to the evolvement of internet of things (IoT) and the rise in the number of mobile users. This has made the deployment of new terrestrial base stations to improve network performance a cost and time inefficient solution. On the other hand future cellular networks are expected to offload flash crowd traffic in an area (e.g., a stadium with ongoing sporting events or concert).Owing to the temporary characteristic of the traffic and the rapid increase in the number of mobile users, deployment of Unmanned Arial Vehicle base stations (UAVBSs) stands out to be the most efficient solution to the above problem. They have been used when terrestrial base stations’ capacity is suffering in some extreme cases such as congestion inside a cell or a special event. This report proposes an efficient three dimensional algorithm named as UAV location optimizing algorithm (ULOA) that optimizes the location of the UAVBS in a Heterogeneous network with two and five uniformly distributed Macro base stations (MBS) and Pico base stations (PBS) respectively to maximize the sum-rate and coverage probability of the network. In this work two models are adopted, the Air-to-Ground channel model (A2G) a well-known radio propagation model for downlink communication between the UAVBS and ground users is adopted. The independent Rayleigh fading model is also adopted for GBS to Ground-User channel. The algorithm utilizes the exhaustive search method to find the optimal location of the UAVBS. The optimal location is determined based the UAVBS location with the highest network sum-rate. The algorithm utilizes the Shannon capacity theorem to calculate the user data rate.