Energy-Efficient UAV Communications with Interference Management: Deep Learning Framework

Fayezeh Ghavimi, Riku Jantti

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)
39 Downloads (Pure)

Abstract

In this paper, an interference-aware energy- efficient scheme for a network of coexisting aerial-terrestrial cellular users is proposed. In particular, each aerial user aims at achieving a trade-off between maximizing energy efficiency and spectral efficiency while minimizing the incurred interference on the terrestrial users along its path. To provide a solution, we first formulate the energy efficiency problem for UAVs as an optimization problem by considering different key performance indicators (KPIs) for the network, coexisting terrestrial users, and UAVs as aerial users. Then, leveraging tools from deep learning, we transform this problem into a deep queue learning problem and present a learning-powered solution that incorporates the KPIs of interest in the design of the reward function to solve energy efficiency maximization for aerial users while minimizing interference to terrestrial users. A broad set of simulations have been conducted in order to investigate how the altitude of UAVs, and the tolerable level of interference, shape the optimal energy-efficient policy in the network. Simulation results show that the proposed scheme achieves better energy and spectral efficiency for UAV and less interference to terrestrial users incurred from aerial users. The obtained results further provide insights on the benefits of leveraging intelligent energy-efficient scheme. For example, a significant increase in the energy efficiency of aerial users with respect to increases in their spectral efficiency, while a considerable decrease in incurred interference to the terrestrial users is achieved in comparison to the non-learning scheme.

Original languageEnglish
Title of host publication2020 IEEE Wireless Communications and Networking Conference Workshops, WCNCW 2020 - Proceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)9781728151786
DOIs
Publication statusPublished - Apr 2020
MoE publication typeA4 Article in a conference publication
EventIEEE Wireless Communications and Networking Conference - Seoul, Korea, Republic of
Duration: 25 May 202028 May 2020

Conference

ConferenceIEEE Wireless Communications and Networking Conference
Abbreviated titleWCNC
CountryKorea, Republic of
CitySeoul
Period25/05/202028/05/2020

Keywords

  • cellular networks
  • deep reinforcement learning
  • drone
  • Energy efficiency
  • interference management
  • machine learning
  • unmanned aerial vehicle (UAV)

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