一種面向多任務(wù)的無人機(jī)輔助的通信網(wǎng)絡(luò)資源分配與軌跡優(yōu)化研究

裴二榮; 婁宇涵; 李永剛; 黎偉

doi:10.11999/JEIT230974

一種面向多任務(wù)的無人機(jī)輔助的通信網(wǎng)絡(luò)資源分配與軌跡優(yōu)化研究

doi: 10.11999/JEIT230974

1.
重慶郵電大學(xué)通信與信息工程學(xué)院重慶 400065
2.
重慶金美通信有限公司重慶 400035

基金項目: 國家自然科學(xué)基金(62071077)，重慶成渝科技創(chuàng)新項目(KJCXZD2020026)

詳細(xì)信息

作者簡介:
裴二榮：男，教授，研究方向為無線移動通信

婁宇涵：男，碩士生，研究方向為無人機(jī)通信、移動邊緣計算

李永剛：男，副教授，研究方向為無線移動通信

黎偉：男，博士，研究方向為無線移動通信

通訊作者:
婁宇涵　1162961114@qq.com

中圖分類號: TN929.5
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- 文章訪問數(shù): 825
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2023-09-06
- 修回日期: 2024-01-25
- 網(wǎng)絡(luò)出版日期: 2024-02-27
- 刊出日期: 2024-07-29

Research on Resource Allocation and Trajectory Optimization of a Multitask Unmanned Aerial Vehicles Assisted Communication Network

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Jinmei Communication Co., Ltd., Chongqing 400035, China

Funds: The National Natural Science Foundation of China (62071077), Chongqing Chengyu Science and Technology Innovation Project (KJCXZD2020026)

摘要

摘要: 裝載各種有效荷載的無人機(jī)(UAV)能夠?qū)崿F(xiàn)傳感、通信和計算等多任務(wù)，因而常被部署到數(shù)據(jù)采集(DA)和輔助計算等領(lǐng)域。但是到目前為止，絕大多數(shù)研究僅專注于單一功能的無人機(jī)輔助的通信網(wǎng)絡(luò)資源分配與軌跡優(yōu)化，對于面向多任務(wù)的資源分配和軌跡優(yōu)化問題還未解決。為此，該文提出一種綜合考慮無人機(jī)數(shù)據(jù)采集、數(shù)據(jù)廣播以及計算任務(wù)卸載的無人機(jī)輔助的通信網(wǎng)絡(luò)資源優(yōu)化的分配策略，旨在通過聯(lián)合優(yōu)化傳輸占空比、用戶發(fā)射功率與無人機(jī)軌跡，在滿足目標(biāo)位置采集數(shù)據(jù)實時廣播的前提下，最大化用戶卸載量。為了解決多變量耦合優(yōu)化問題，提出了基于塊坐標(biāo)下降(BCD)和連續(xù)凸逼近(SCA)的高效迭代優(yōu)化算法，將耦合優(yōu)化問題分解為3個子問題進(jìn)行迭代優(yōu)化。最后，大量仿真結(jié)果表明，該算法在公平性和總卸載計算量方面都優(yōu)于其他測試方案。
- 無人機(jī)通信 /
- 移動邊緣計算 /
- 數(shù)據(jù)采集 /
- 凸優(yōu)化
Abstract: Unmanned Aerial Vehicles (UAV) loaded with various payloads can achieve multiple tasks such as sensing, communication, and computing, and are often deployed in fields such as Data Acquisition (DA) and auxiliary computing. However, so far, the vast majority of research has only focused on single function drone resource allocation and trajectory optimization, and the problem of multi task oriented drone resource allocation and trajectory optimization has not been solved yet. Therefore, an allocation strategy for optimizing drone communication network resources is proposed that comprehensively considers drone data acquisition, data broadcasting, and computing task offloading. The aim is to maximize user offloading by jointly optimizing transmission duty cycle, user transmission power, and drone trajectory, while meeting the real-time broadcast of target location data collection. In order to solve the problem of multivariable coupled optimization, an efficient iterative optimization algorithm based on Block Coordinate Descent (BCD) and Successive Convex Approximate (SCA) is proposed. The coupled optimization problem is decomposed into three sub problems for iterative optimization. Finally, a large number of simulation results show that the algorithm outperforms other testing schemes in terms of fairness and total offloading computation.
- Unmanned Aerial Vehicle (UAV) communication /
- Mobile Edge Computing (MEC) /
- Data acquisition /
- Convex optimization

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圖 1 系統(tǒng)模型

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圖 2 無人機(jī)時隙分配策略

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圖 3 6種方案在每個時隙的總卸載吞吐量

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圖 4 6種方案的收斂性

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圖 5 無人機(jī)采集數(shù)據(jù)下行吞吐量

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圖 6 無人機(jī)飛行高度H對系統(tǒng)性能的影響

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圖 7 用戶傳輸能量對系統(tǒng)性能的影響

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圖 8 不同的$ {D_k} $和$ {d_{{\text{set}}}} $對系統(tǒng)性能的影響

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1 面向多任務(wù)的無人機(jī)軌跡和資源迭代優(yōu)化算法

(1) 初始化最大誤差 $ \varepsilon $ 、總量吐量迭代值obj、最大迭代次數(shù) $ \alpha $ 與　 $ {{{D}}^i}\left( n \right) = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $
(2) $ {\bf{while}} $ $ i \lt \alpha $ $ {\bf{do}} $
(3) 　 $ i = i + 1, $
(4) 　給定 $ \left\{ {{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ，求解占空比子問題P1，得到最優(yōu)情況　　　 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ;
(5) 　給定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i} \right\} $ ，求解功率子問題P2，得到最優(yōu)情況　　　 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;
(6) 　給定 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ，求解軌跡子問題P3.1，得到最優(yōu)情　　　況 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ ;
(7) 　將 $ \left\{ {{\boldsymbol{A}}_n^{i + 1},{\boldsymbol{Q}}_n^{i + 1},{\boldsymbol{P}}_{m,n}^{i + 1}} \right\} $ 帶入目標(biāo)函數(shù)中計算當(dāng)前總吞吐　　　量迭代值objnew;
(8) 　 $ {\bf{if }}{\text{ abs}}\left( {{\text{objnew}} - {\text{obj}}} \right) \le \varepsilon {\text{ }}{\bf{then}} $ break
(9) 　else $ {\text{obj}} = {\text{objnew}} $ ;
(10) end while
(11) 輸出最優(yōu)參數(shù)值 $ \left\{ {{\boldsymbol{A}}_n^,{\boldsymbol{Q}}_n^,{\boldsymbol{P}}_{m,n}^} \right\} = \left\{ {{\boldsymbol{A}}_n^i,{\boldsymbol{Q}}_n^i,{\boldsymbol{P}}_{m,n}^i} \right\} $ ，　　　計算獲得當(dāng)前最大的總吞吐量為 $ {\text{ob}}{{\text{j}}^} = {\text{objnew}} $ ;

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表 1 關(guān)鍵的仿真參數(shù)

參數(shù)	取值	參數(shù)	取值
飛行高度$ H $	100 m	最大傳輸能量$ {E_{\max }} $	0.4 J
飛行時隙$ N $	60	最小傳輸速率$ {R_{\min }} $	4×10⁴ bit/s
飛行周期$ T $	60 s	最大平均功率$ {P_{\max }} $	500 mW
信道帶寬$ B $	1 MHz	目標(biāo)區(qū)域半徑$ {d_{{\text{set}}}} $	10 m
最大速度$ {V_{\max }} $	20 m/s	功率譜密度$ {N_0} $	–90 dBm/Hz
信道功率增益$ {\beta _0} $	–60 dB	目標(biāo)位置數(shù)據(jù)量$ {D_k} $	7×10⁶ bit

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參考文獻(xiàn)(18)

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