支持無線采能及簇間負載均衡的無人機輔助數(shù)據(jù)調(diào)度及軌跡優(yōu)化算法

柴蓉; 李沛欣; 梁承超; 陳前斌

doi:10.11999/JEIT240048

支持無線采能及簇間負載均衡的無人機輔助數(shù)據(jù)調(diào)度及軌跡優(yōu)化算法

doi: 10.11999/JEIT240048

重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065

基金項目: 國家自然科學(xué)基金(62271097)

詳細信息

作者簡介:
柴蓉：女，教授，研究方向為空天地一體化網(wǎng)絡(luò)架構(gòu)及關(guān)鍵技術(shù)、無線資源管理及移動性管理技術(shù)等

李沛欣：女，碩士生，研究方向為無線通信、無線資源管理等

梁承超：男，教授，研究方向為衛(wèi)星通信系統(tǒng)架構(gòu)及關(guān)鍵技術(shù)、無線資源管理等

陳前斌：男，教授，研究方向為無線通信關(guān)鍵技術(shù)、無線資源管理等

通訊作者:
梁承超　liangcc@cqupt.edu.cn

中圖分類號: TN926.2
計量
- 文章訪問數(shù): 277
- HTML全文瀏覽量: 74
- PDF下載量: 35
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-24
- 修回日期: 2024-08-27
- 網(wǎng)絡(luò)出版日期: 2024-09-01
- 刊出日期: 2024-10-30

Wireless Energy Harvest and Inter-Cluster Load Balancing-Enabled UAV-Assisted Data Scheduling and Trajectory Optimization Algorithms

School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China(62271097)

摘要

摘要: 該文研究了無人機(UAV)輔助無線傳感器網(wǎng)絡(luò)的數(shù)據(jù)收集問題。首先提出基于均值漂移算法的傳感器節(jié)點(SN)初始分簇策略，進而以簇間負載均衡為目標，設(shè)計SN切換算法?；谒贸纱夭呗?，將UAV數(shù)據(jù)收集及軌跡規(guī)劃問題建模為系統(tǒng)能耗最小化問題。由于該問題是一個非凸問題，難以直接求解，將其分為兩個子問題，即數(shù)據(jù)調(diào)度子問題及UAV軌跡規(guī)劃子問題。針對數(shù)據(jù)調(diào)度子問題，提出一種基于多時隙庫恩-蒙克雷斯算法的時頻資源調(diào)度策略。針對UAV軌跡規(guī)劃子問題，將其建模為馬爾可夫決策過程，并提出一種基于深度Q網(wǎng)絡(luò)的UAV軌跡規(guī)劃算法。仿真結(jié)果驗證了所提算法的有效性。
- 無人機 /
- 數(shù)據(jù)收集 /
- 軌跡優(yōu)化 /
- 馬爾可夫決策過程
Abstract: Data collection problem in an Unmanned Aerial Vehicle (UAV)-assisted wireless sensor network is addressed. Firstly, an initial Sensor Node (SN) clustering strategy is proposed based on the mean drift algorithm, then an SN switching algorithm is designed to achieve load balancing between clusters. Based on the obtained clustering strategy, the UAV data collection and trajectory planning problem is formulated as a system energy consumption minimization problem. Since the formulated problem is a non-convex problem and is difficult to solve directly, it is decoupled into two subproblems, namely data scheduling subproblem and UAV trajectory planning subproblem. To tackle the data scheduling subproblem, a multi-slot Kuhn-Munkres algorithm-based time-frequency resource scheduling strategy is proposed. To solve the UAV trajectory planning subproblem, the problem is modeled as a Markov decision-making process, and a deep Q-network-based algorithm is proposed. Simulation results verify the effectiveness of the proposed algorithm.
- Unmanned aerial vehicle /
- Data collection /
- Trajectory planning /
- Markov decision process

HTML全文

圖 1 系統(tǒng)模型圖

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圖 2 各簇數(shù)據(jù)量比較圖

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圖 3 UAV總懸停時隙與節(jié)點數(shù)量關(guān)系圖

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圖 4 系統(tǒng)能耗與SN發(fā)射功率關(guān)系圖

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圖 5 累計獎勵與迭代次數(shù)關(guān)系圖

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表 1 仿真參數(shù)設(shè)置

仿真參數(shù)	數(shù)值
SN數(shù)據(jù)量$ {\varphi _k} $	[0, 1024] MB
載波頻率C_f	[1, 3] GHz
節(jié)點可用帶寬B	1 MHz
SN發(fā)射功率p^c	0.1 W
UAV飛行高度H	70 m
UAV飛行速度v	10 m/s
UAV平均轉(zhuǎn)子誘導(dǎo)速度v₀	4.03 m/s
空氣密度ρ	1.225 km/m³
轉(zhuǎn)子盤面積S_r	0.503 m²

下載: 導(dǎo)出CSV

參考文獻(15)

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