TY - JOUR
T1 - Machine learning-assisted adaptive modulation for optimized drone-user communication in b5g
AU - Gopi, Sudheesh Puthenveettil
AU - Magarini, Maurizio
AU - Alsamhi, Saeed Hamood
AU - Shvetsov, Alexey V.
N1 - Funding Information:
Acknowledgments: We acknowledge the support by research grant from Science596Foundation Ireland (SFI) under Grant Number SFI/16/RC/3918 (Confirm), and Marie Skłodowska-Curie597grant agreement No. 847577 co-funded by the European Regional Development Fund.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/12
Y1 - 2021/12
N2 - The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.
AB - The fundamental issue for Beyond fifth Generation (B5G) is providing a pervasive connection to heterogeneous and various devices in smart environments. Therefore, Drones play a vital role in the B5G, allowing for wireless broadcast and high-speed communications. In addition, the drone offers several advantages compared to fixed terrestrial communications, including flexible deployment, robust Line of Sight (LoS) connections, and more design degrees of freedom due to controlled mobility. Drones can provide reliable and high data rate connectivity to users irrespective of their location. However, atmospheric disturbances impact the signal quality between drones and users and degrade the system performance. Considering practical implementation, the location of drones makes the drone–user communication susceptible to several environmental disturbances. In this paper, we evaluate the performance of drone-user connectivity during atmospheric disturbances. Further, a Machine Learning (ML)-assisted algorithm is proposed to adapt to a modulation technique that offers optimal performance during atmospheric disturbances. The results show that, with the algorithm, the system switches to a lower order modulation scheme during higher rain rate and provides reliable communication with optimized data rate and error performance.
UR - http://www.scopus.com/inward/record.url?scp=85118147977&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85118147977&partnerID=8YFLogxK
U2 - 10.3390/drones5040128
DO - 10.3390/drones5040128
M3 - Article
AN - SCOPUS:85118147977
SN - 2504-446X
VL - 5
JO - Drones
JF - Drones
IS - 4
M1 - 128
ER -