Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

Shibata, Yohei; Takabatake, Wataru; Hoshino, Kenji; Nagate, Atsushi; Ohtsuki, Tomoaki

doi:10.1109/access.2022.3186003

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…The specific range of each parameter is outlined in Table I. In our approach, we assume a step size of 2 degrees, similar to the study referenced in [4].…”

Section: A Proposed Methodsmentioning

confidence: 99%

“…Regarding the antenna pattern of the HAPS system investigated in this study, we refer to the work presented in [4], which accounts for modifications made to ITU-R recommendations [12]. The antenna gain for a given angle, denoted as ψ, can be expressed as follows:…”

Section: A Antenna Pattern and Channel Modelmentioning

confidence: 99%

“…In order to ensure a certain level of communication quality for users, we establish specific coverage conditions. These conditions, outlined in [4], are utilized to determine whether a user is within the coverage area of the HAPS system. The coverage criteria are follows:…”

Section: B Coverage Conditionsmentioning

confidence: 99%

“…The radius of a HAPS service area extends from several dozen kilometers to 100 km, requiring a multicell configuration with single-cell frequency reuse to enhance communication capacity. 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].…”

mentioning

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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“…The specific range of each parameter is outlined in Table I. In our approach, we assume a step size of 2 degrees, similar to the study referenced in [4].…”

Section: A Proposed Methodsmentioning

confidence: 99%

Section: A Antenna Pattern and Channel Modelmentioning

confidence: 99%

Section: B Coverage Conditionsmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

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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“…We adopt the International Telecommunications Union (ITU) path loss model between the users and the ABSs. The path loss model between HAPS m ∈ M and user k ∈ K includes the free space path loss (FSPL) model which can be expressed as [18] L m,k (t) = 32.44 + 20 log 10 f HAPS + 20 log 10 d m,k (t) [dB],…”

Section: Radio Propagation and Signal Qualitymentioning

confidence: 99%

HAPS-UAV-Enabled Heterogeneous Networks: A Deep Reinforcement Learning Approach

Arani¹,

Hu²,

Zhu³

2023

Preprint

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The integrated use of non-terrestrial network (NTN) entities such as the high-altitude platform station (HAPS) and low-altitude platform station (LAPS) has become essential elements in the space-air-ground integrated networks (SAGINs). However, the complexity, mobility, and heterogeneity of NTN entities and resources present various challenges from system design to deployment. This paper proposes a novel approach to designing a heterogeneous network consisting of HAPSs and unmanned aerial vehicles (UAVs) being LAPS entities. Our approach involves jointly optimizing the three-dimensional trajectory and channel allocation for aerial base stations, with a focus on ensuring fairness and the provision of quality of service (QoS) to ground users. Furthermore, we consider the load on base stations and incorporate this information into the optimization problem. The proposed approach utilizes a combination of deep reinforcement learning and fixed-point iteration techniques to determine the UAV locations and channel allocation strategies. Simulation results reveal that our proposed deep learning-based approach significantly outperforms learningbased and conventional benchmark models.

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