TY - JOUR
T1 - Optimal Deployment and Operation of Robotic Aerial 6G Small Cells with Grasping End Effectors
AU - Liao, Yuan
AU - Friderikos, Vasilis
PY - 2023/3/27
Y1 - 2023/3/27
N2 - Airborne base stations (ABSs) have drawn a significant interest since they can enhance network capacity and coverage thanks to their dominant line-of-sight links as well as flexible deployment. However, the system performance of ABS-assisted networks is severely confined by the limited endurance of the on-board battery. To overcome this energy issue, we are exploring the robotic airborne base station (RABS) with energy neutral grasping end-effectors able to autonomously perch at tall urban landforms. This paper studies the optimal deployment (fly to another grasping location or remain in the same one) and operation (active or sleep mode at an epoch) of RABS based on the spatio-temporal distribution of underlying traffic load, which problem is formulated as an integer linear programming (ILP) aiming to maximize the volume of served traffic load under the on-board energy constraints. To tackle the curse of dimensionality in this ILP formulation, we first investigate a special case with a single RABS in the system and develop a Lagrangian heuristic algorithm to solve it by exploiting the totally unimodularity structure. A polynomial-time method is then proposed to decompose the multi-RABS problem into several single-RABS cases based on the Hungarian algorithm. In terms of aggregated traffic that can be supported, numerical results reveal that a single RABS outperforms four (4) fixed micro cells when the serving duration ranges from 10 to 24 hours, allowing in that sense efficient network densification. The traffic loaded in a RABS is 3.2 times higher than a fixed small cell when the traffic distribution is highly heterogeneous. Finally, the efficiency and performance of the proposed algorithms are also detailed.
AB - Airborne base stations (ABSs) have drawn a significant interest since they can enhance network capacity and coverage thanks to their dominant line-of-sight links as well as flexible deployment. However, the system performance of ABS-assisted networks is severely confined by the limited endurance of the on-board battery. To overcome this energy issue, we are exploring the robotic airborne base station (RABS) with energy neutral grasping end-effectors able to autonomously perch at tall urban landforms. This paper studies the optimal deployment (fly to another grasping location or remain in the same one) and operation (active or sleep mode at an epoch) of RABS based on the spatio-temporal distribution of underlying traffic load, which problem is formulated as an integer linear programming (ILP) aiming to maximize the volume of served traffic load under the on-board energy constraints. To tackle the curse of dimensionality in this ILP formulation, we first investigate a special case with a single RABS in the system and develop a Lagrangian heuristic algorithm to solve it by exploiting the totally unimodularity structure. A polynomial-time method is then proposed to decompose the multi-RABS problem into several single-RABS cases based on the Hungarian algorithm. In terms of aggregated traffic that can be supported, numerical results reveal that a single RABS outperforms four (4) fixed micro cells when the serving duration ranges from 10 to 24 hours, allowing in that sense efficient network densification. The traffic loaded in a RABS is 3.2 times higher than a fixed small cell when the traffic distribution is highly heterogeneous. Finally, the efficiency and performance of the proposed algorithms are also detailed.
M3 - Article
SN - 0018-9545
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
ER -