Airborne Integrated Access and Backhaul Systems: Learning-Aided Modeling and Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0184278" target="_blank" >RIV/00216305:26220/26:0184278 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10175634" target="_blank" >https://ieeexplore.ieee.org/document/10175634</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TVT.2023.3293171" target="_blank" >10.1109/TVT.2023.3293171</a>
Alternative languages
Result language
angličtina
Original language name
Airborne Integrated Access and Backhaul Systems: Learning-Aided Modeling and Optimization
Original language description
The deployment of millimeter-wave (mmWave) 5G New Radio (NR) networks is hampered by the properties of the mmWave band, such as severe signal attenuation and dynamic link blockage, which together limit the cell range. To provide a cost-efficient and flexible solution for network densification, 3GPP has recently proposed integrated access and backhaul (IAB) technology. As an alternative approach to terrestrial deployments, the utilization of unmanned aerial vehicles (UAVs) as IAB-nodes may provide additional flexibility for topology configuration. The aims of this study are to (i) propose efficient optimization methods for airborne and conventional IAB systems and (ii) numerically quantify and compare their optimized performance. First, by assuming fixed locations of IAB-nodes, we formulate and solve the joint path selection and resource allocation problem as a network flow problem. Then, to better benefit from the utilization of UAVs, we relax this constraint for the airborne IAB system. To efficiently optimize the performance for this case, we propose to leverage deep reinforcement learning (DRL) method for specifying airborne IAB-node locations. Our numerical results show that the capacity gains of airborne IAB systems are notable even in non-optimized conditions but can be improved by up to 30 % under joint path selection and resource allocation and, even further, when considering aerial IAB-node locations as an additional optimization criterion.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20203 - Telecommunications
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
ISSN
0018-9545
e-ISSN
1939-9359
Volume of the periodical
72
Issue of the periodical within the volume
12
Country of publishing house
US - UNITED STATES
Number of pages
14
Pages from-to
16553-16566
UT code for WoS article
001132470500081
EID of the result in the Scopus database
2-s2.0-85164383230