A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches (2022)

Chang, Z., Deng, H., You, L., Min, G., Garg, S., & Kaddoum, G. (2022). Trajectory Design and Resource Allocation for Multi-UAV Networks : Deep Reinforcement Learning Approaches. IEEE Transactions on Network Science and Engineering, 10(5), 2940-2951. https://doi.org/10.1109/tnse.2022.3171600

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Chang, Zheng; Deng, Hengwei; You, Li; Min, Geyong; Garg, Sahil; Kaddoum, Georges

Lehti tai sarja: IEEE Transactions on Network Science and Engineering

eISSN: 2327-4697

Julkaisuvuosi: 2022

Volyymi: 10

Lehden numero: 5

Artikkelin sivunumerot: 2940-2951

Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/tnse.2022.3171600

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/81007

Tiivistelmä

The future mobile communication system is expected to provide ubiquitous connectivity and unprecedented services over billions of devices. The unmanned aerial vehicle (UAV), which is prominent in its flexibility and low cost, emerges as a significant network entity to realize such ambitious targets. In this work, novel machine learning-based trajectory design and resource allocation schemes are presented for a multi-UAV communications system. In the considered system, the UAVs act as aerial Base Stations (BSs) and provide ubiquitous coverage. In particular, with the objective to maximize the system utility over all served users, a joint user association, power allocation and trajectory design problem is presented. To solve the problem caused by high dimensionality in state space, we first propose a machine learning-based strategic resource allocation algorithm which comprises of reinforcement learning and deep learning to design the optimal policy of all the UAVs. Then, we also present a multi-agent deep reinforcement learning scheme for distributed implementation without knowing a priori knowledge of the dynamic nature of networks. Extensive simulation studies are conducted and illustrated to evaluate the advantages of the proposed scheme.

YSO-asiasanat: langattomat verkot; miehittämättömät ilma-alukset; koneoppiminen; syväoppiminen

Vapaat asiasanat: resource management; trajectory; autonomous aerial vehicles; communication systems; reinforcement learning; wireless networks; throughput

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 1