TY - GEN
T1 - Emergent Communication in Multi-Agent Reinforcement Learning for Flying Base Stations
AU - Naoumi, Salmane
AU - Alami, Reda
AU - Hacid, Hakim
AU - Almazrouei, Ebtesam
AU - Debbah, Merouane
AU - Bennis, Mehdi
AU - Chafii, Marwa
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In order to increase network capacity and coverage, flying base stations (FBSs) can be deployed in a variety of scenarios, such as in extremely crowded gatherings or for emergency communication and network access in areas without terrestrial network coverage. Due to their inherent low cost, ease of deployment and high mobility, unmanned aerial vehicles (UAVs) deployed as FBSs can provide cost-effective, fast and reliable network access services to remote ground users. To maximize network capacity, FBSs need to coordinate and exchange information about their observations to optimize their positions, under limited energy and bandwidth resources. In this paper, we investigate the problem of optimizing the positions of FBSs using the framework of emerging communications in multi-agent reinforcement learning (EC-MARL) and we evaluate two EC-MARL architectures, namely Multi-Agent Graph-Attention Communication and Teaming (MAGIC) and Targeted Multi-Agent Communication (TarMAC). We show that coordination between FBSs through learning a communication protocol increases the total achievable rate and coverage of ground users, compared to baselines without communication. Moreover, we consider challenging environments with a large number of FBSs and demonstrate the efficiency of the proposed method in terms of speed of convergence and robustness to the movement of users.
AB - In order to increase network capacity and coverage, flying base stations (FBSs) can be deployed in a variety of scenarios, such as in extremely crowded gatherings or for emergency communication and network access in areas without terrestrial network coverage. Due to their inherent low cost, ease of deployment and high mobility, unmanned aerial vehicles (UAVs) deployed as FBSs can provide cost-effective, fast and reliable network access services to remote ground users. To maximize network capacity, FBSs need to coordinate and exchange information about their observations to optimize their positions, under limited energy and bandwidth resources. In this paper, we investigate the problem of optimizing the positions of FBSs using the framework of emerging communications in multi-agent reinforcement learning (EC-MARL) and we evaluate two EC-MARL architectures, namely Multi-Agent Graph-Attention Communication and Teaming (MAGIC) and Targeted Multi-Agent Communication (TarMAC). We show that coordination between FBSs through learning a communication protocol increases the total achievable rate and coverage of ground users, compared to baselines without communication. Moreover, we consider challenging environments with a large number of FBSs and demonstrate the efficiency of the proposed method in terms of speed of convergence and robustness to the movement of users.
KW - Emergent communication
KW - Flying base station
KW - Multi-agent reinforcement learning
KW - UAV
UR - http://www.scopus.com/inward/record.url?scp=85175149268&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85175149268&partnerID=8YFLogxK
U2 - 10.1109/MeditCom58224.2023.10266608
DO - 10.1109/MeditCom58224.2023.10266608
M3 - Conference contribution
AN - SCOPUS:85175149268
T3 - 2023 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023
SP - 133
EP - 138
BT - 2023 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023
Y2 - 4 September 2023 through 7 September 2023
ER -