Genetic Algorithm Based Location Deployment Optimization of Small Cell with Marine Application

Chen, Canbin; Gao, Zhibin; Huang, Lianfen; Wen, Bin

doi:10.1109/ccitsa.2015.32

Cited by 2 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The complexity of the GA in each iteration is O(GN), where G is the number of individuals [26]. An individual is an entity that is characterized by a set of Genes (i.e., variables).…”

Section: Computational Complexity Analysismentioning

confidence: 99%

Controlling Interference Structure and Transmit Power of Aerial Small Cells by Hybrid Affinity Propagation Clustering and Reinforcement Learning

Cheng¹,

Jialing²,

Wang³

2021

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

This article presents a learning-based interference management mechanism for multiple unmanned aerial vehicles mounted small cells (ASCs), called HAPPIER, standing for hybrid affinity propagation clustering (APC) and reinforcement learning (RL) power control. The proposed HAPPIER interference management mechanism consists of two main algorithms: APC and RL. First, from the macroscopic viewpoint, the APC explores the interference structure of multiple ASCs and then changes the most serious interfering ASCs into sleeping mode. As such, we can shift the complicated interference structure into the one with fewer interfering sources and thus speed up the learning process of interference management. Secondly, from the microscopic viewpoint, based on the interference structure suggested by HAPPIER, the RL is applied to adjust the transmission power of active ASCs to optimize the total throughput further. HAPPIER can achieve the optimal trade-off between system throughput and complexity. From our numerical results, subject to the same complexity constraint, our proposed HAPPIER outperforms all the existing approaches and can achieve 93% of the system throughput of the exhaustive searching algorithm.

show abstract

“…The complexity of the GA in each iteration is O(GN), where G is the number of individuals [26]. An individual is an entity that is characterized by a set of Genes (i.e., variables).…”

Section: Computational Complexity Analysismentioning

confidence: 99%