TY - JOUR
T1 - Joint ABS Deployment and TBS Antenna Downtilt Optimization for Coverage Maximization
AU - Li, Huan
AU - Zhai, Daosen
AU - Zhang, Ruonan
AU - Wang, Chen
AU - Tang, Xiao
N1 - Publisher Copyright:
© 2012 IEEE.
PY - 2022/7/1
Y1 - 2022/7/1
N2 - In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.
AB - In this letter, we consider an air-and-ground cooperative network, where several aerial base stations (ABS) help terrestrial base stations (TBS) for coverage enhancement. In this network, we first quantify the space-time coverage ratio (STCR) by fully considering the antenna models and the dynamic of the ABS, and then formulate a joint ABS deployment and TBS antenna downtilt optimization problem with the objective to maximize the STCR of the concerned area. The objective function involves many control variables and judgement operations, which make the problem very complex. To solve the problem effectively, we first adopt the genetic algorithm (GA). Using the solutions of the GA as training samples, we propose a deep neural network architecture to further reduce the computational time. Simulation results indicate that the proposed GA significantly improves the coverage ratio and the deep neural network (DNN) architecture achieves orders of magnitude acceleration in computational time with acceptable performance.
KW - Cooperative communication
KW - network coverage
KW - optimization algorithm
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85128597770&partnerID=8YFLogxK
U2 - 10.1109/LWC.2022.3166625
DO - 10.1109/LWC.2022.3166625
M3 - 文章
AN - SCOPUS:85128597770
SN - 2162-2337
VL - 11
SP - 1329
EP - 1333
JO - IEEE Wireless Communications Letters
JF - IEEE Wireless Communications Letters
IS - 7
ER -