A New Framework for Multi-Hop ABS-Assisted 5G-Networks With Users’ Mobility Prediction

Abstract

Unmanned Aerial Vehicles (UAVs) have gained momentum as one of the potential solutions in 5 G networks. Indeed, using UAVs as Aerial Base Stations (ABS) to assist the traditional cellular infrastructure and/or extend its coverage represents a high-potential flexible and cost-effective approach to provide on-demand communications in these new generations of networks. Yet, their effective deployment brings out many challenges, including their optimal 3D placement, coverage optimization, backhaul constraints, etc. Hence, we tackle these challenges by proposing a new framework for 3D positioning of an ABS swarm in order to extend the coverage of ground base stations (GBS). Unlike existing work that ignore ABS’s limited backhaul capacity and how it restraints the number of users that can be served, we consider a multi-hop backhaul scheme that allows ABSs to remain connected to the core network through the GBSs directly, or other deployed ABSs. Under this setup, we aim to maximize the number of served UEs, by optimizing the ABS’s placement, jointly with their association with UEs and backhauling base stations. Besides, our framework, named ASF for Adaptive Social Spider Optimization based framework, uses machine learning techniques to predict the users’ mobility and enable ABSs to adjust their position according to their spatial distribution. Simulations show that ASF framework significantly improves the cellular network’s coverage and provides access to 92% of mobile users, as compared to other benchmark schemes that only serve 79% and 58% of users.