Ahead-Me Coverage (AMC): On Maintaining Enhanced Mobile Network Coverage for UAVs

Hamed Hellaoui, Bin Yang, Tarik Taleb, Jukka Manner

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


This paper proposes the concept of Ahead-Me Cov-erage (AMC) aiming to get the coverage of a cellular network ahead of the mobile users for maintaining enhanced Quality- of-Service (QoS) in cellular-connected unmanned aerial vehicle (UAV) networks. In such networks, each base station (BS) with an intelligent logic can automatically tilt the direction of its radio antennas based on the trajectory of UAV s. For this purpose, we first formulate AMC as an integer optimization problem for maximizing the minimum transmission rate of UAVs by jointly optimizing the angles of the different radio antenna, the resource allocation and the selection of the appropriate serving BS for the UAVs throughout their path. For this complex optimization problem, we then propose a solution based on Deep Reinforcement Learning (DRL) to solve it. Under this solution, we adopt a multi-heterogeneous agent-based approach (MHA-DRL) including two types of agents, namely the UAV agents and the BS agents. Each agent implements an Advantage Actor Critic (A2C) to learn optimal policies. Specifically, the BS agents aim to tilt their antennas to get ahead of the UAV s throughout their mobility, and the UAV agents target selecting the appropriate serving BSs along with resource allocation. Performance evaluations are presented to validate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationGLOBECOM 2022 - 2022 IEEE Global Communications Conference
Number of pages6
ISBN (Electronic)978-1-6654-3540-6
ISBN (Print)978-1-6654-3541-3
Publication statusPublished - 11 Jan 2023
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference - Rio de Janeiro, Brazil
Duration: 4 Dec 20228 Dec 2022


ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
CityRio de Janeiro


  • Performance evaluation
  • Transmitting antennas
  • Reinforcement learning
  • Quality of service
  • Interference
  • Directive antennas
  • Autonomous aerial vehicles


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