Dynamic Antenna Control for HAPS Using Fuzzy Q-Learning in Multi-Cell Configuration

Wada, Kenshiro; Yang, Siyuan; Bouazizi, Mondher; Ohtsuki, Tomoaki; Shibata, Yohei; Takabatake, Wataru; Hoshino, Kenji; Nagate, Atsushi

doi:10.1109/icc45855.2022.9838271

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…In our study, we proposed cell optimization methods employing genetic algorithms (GAs) to enhance spectral efficiency (SE), considering the utilization of phased array antennas [3,4]. Additionally, previous studies have put forward various methods for controlling multicell configurations [5][6][7][8]. The methods in [5][6][7] rely on reinforcement learning (RL), where the method in [5] combines the evolution algorithm with RL, the method in [6] employs the mean field game theory with RL during the training phase, and the method in [7] employs fuzzy Q-learning.…”

mentioning

confidence: 99%

“…Additionally, previous studies have put forward various methods for controlling multicell configurations [5][6][7][8]. The methods in [5][6][7] rely on reinforcement learning (RL), where the method in [5] combines the evolution algorithm with RL, the method in [6] employs the mean field game theory with RL during the training phase, and the method in [7] employs fuzzy Q-learning. K-means clustering aided particle swarm optimization (PSO) algorithm is proposed in [8] to help PSO to converge quickly by redefining the search range for each parameter by utilizing K-means clustering.…”

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confidence: 99%

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HAPS Cell Design Method for Coverage Extension Considering Coexistence on Terrestrial Mobile Networks

Shibata,

Takabatake,

Hoshino

et al. 2024

IEEE Access

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High-altitude platform stations (HAPSs) that provide direct communication services to smartphones on the ground are garnering significant attention as innovative mobile communication platforms suitable for ultrawide coverage areas. The deployment of a multi-cell configuration is essential for enhancing communication capacity over large geographical regions. However, designing and optimizing multi-cell configurations proves challenging due to the numerous antenna parameters involved, such as beamwidth and beam direction. HAPSs also hold promise in serving as reliable networks during disasters, as they can quickly restore coverage when terrestrial base stations (BSs) are in failure, allowing users to maintain connectivity via HAPSs using the same frequency band as terrestrial networks. Despite this advantage, HAPSs can potentially cause interference in terrestrial networks during such scenarios, necessitating careful considerations to avoid disrupting existing terrestrial networks. However, the optimization of cell configurations in the coexistence of HAPSs and terrestrial networks remains largely unexplored. To address this gap, this paper proposes a design scheme that leverages a genetic algorithm (GA) to optimize antenna parameters for each cell, thereby maximizing both user coverage and area coverage while ensuring coexistence with terrestrial networks. We optimize antenna parameters to increase user coverage and area coverage as a multi-objective optimization problem, resulting in a Pareto front that illustrates the trade-off choices. Additionally, the proposed method protects terrestrial networks by setting the initial beam direction of each cell to avoid interference with terrestrial BSs. Consequently, the GA can initiate optimization with candidate combinations that adhere to the interference constraints of terrestrial networks, thus expediting the optimization process and yielding improved coverage. Simulation results demonstrate that the proposed scheme outperforms conventional schemes which solely control beam direction, leading to superior coverage. While the results show trade-off between user coverage and area coverage, it is also clarified that considering both types of coverage show the potential to achieve significant improvements in both aspects simultaneously. Moreover, the results highlight the efficacy of the initial beam direction setting in enhancing coverage rates and significantly reducing the number of combinations required for the GA to converge.INDEX TERMS Coverage Optimization, Genetic Algorithm, HAPS, Non-Uniform User Distribution, Coexistence between HAPS and terrestrial networks. I. INTRODUCTIONA high-altitude platform station (HAPS) holds immense potential for revolutionizing mobile communication services [1,2]. HAPS mobile communication relies on unmanned aerial vehicles (UAVs) such as balloons, airships, and other aircraft, operating in the stratosphere at an altitude of 20 km, where wind speeds are relatively low. Compared to communication satellites like stationary satellites (3...

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