TY - JOUR
T1 - Airborne Integrated Access and Backhaul Systems
T2 - Learning-Aided Modeling and Optimization
AU - Tafintsev, Nikita
AU - Moltchanov, Dmitri
AU - Chiumento, Alessandro
AU - Valkama, Mikko
AU - Andreev, Sergey
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - 5G NR
KW - actor-critic
KW - deep reinforcement learning
KW - integrated access and backhaul
KW - mmWave
UR - http://www.scopus.com/inward/record.url?scp=85164383230&partnerID=8YFLogxK
U2 - 10.1109/TVT.2023.3293171
DO - 10.1109/TVT.2023.3293171
M3 - Article
AN - SCOPUS:85164383230
SN - 0018-9545
VL - 72
SP - 16553
EP - 16566
JO - IEEE transactions on vehicular technology
JF - IEEE transactions on vehicular technology
IS - 12
ER -