Abstract
Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.
Highlights
We present an alternate artificial intelligence (AI) solution, which is based on a double deep Q learning algorithm (DDQN) and uses a single AI agent to model all macro base stations (MBSs) and UAV base stations (UABSs) in the Unmanned aerial vehicles (UAVs) heterogeneous network (HetNet)
Intel reinforcement learning (RL) Coach is a python framework, and it implements many state-of-the-art algorithms. These algorithms can be used through a set of application programming interfaces (APIs)
We studied how 3GPP LTE further enhanced inter-cell interference coordination (FeICIC) parameters can be tuned in a UAV-HetNet to maximize the mean and median spectral efficiency of a cellular network
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. An unmanned aerial vehicle (UAV) heterogeneous network (HetNet) consists of conventional stationary ground macro base stations (MBSs), supplemented by mobile. UAV base stations (UABSs) and cells on wheels [1]. The agility of UAVs coupled with their ability to carry radios and communicate wirelessly has led their adoption in various applications to address network congestion and in public safety communications as a temporary substitute for damaged communication infrastructure. In the aftermath of hurricane Maria in 2017, ground base stations were destroyed and AT&T used UAVs to temporarily restore wireless voice, text, data, and multimedia services [2]
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