智能反射面輔助的無(wú)人機(jī)無(wú)線攜能通信網(wǎng)絡(luò)吞吐量最大化算法研究

劉志新; 趙松晗; 楊毅; 袁亞洲

doi:10.11999/JEIT220195

智能反射面輔助的無(wú)人機(jī)無(wú)線攜能通信網(wǎng)絡(luò)吞吐量最大化算法研究

doi: 10.11999/JEIT220195

燕山大學(xué)電氣工程學(xué)院秦皇島 066004

基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61873223)

詳細(xì)信息

作者簡(jiǎn)介:
劉志新：男，1976年生，教授，研究方向?yàn)闊o(wú)線通信網(wǎng)絡(luò)資源優(yōu)化分配與協(xié)同控制

趙松晗：男，1996年生，博士生，研究方向?yàn)橹悄芊瓷涿嫱ㄐ畔到y(tǒng)

楊毅：女，1983年生，助理研究員，研究方向?yàn)闊o(wú)線資源優(yōu)化管理

袁亞洲：男，1985年生，副教授，研究方向?yàn)槲锫?lián)網(wǎng)技術(shù)與應(yīng)用

通訊作者:
劉志新　lzxauto@ysu.edu.cn

中圖分類號(hào): TN92
計(jì)量
- 文章訪問(wèn)數(shù): 1449
- HTML全文瀏覽量: 960
- PDF下載量: 295
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2022-02-28
- 修回日期: 2022-04-05
- 網(wǎng)絡(luò)出版日期: 2022-04-15
- 刊出日期: 2022-07-25

Throughput Maximization Algorithm for Intelligent Reflecting Surface-aided Unmanned Aerial Vehicle Communication Networks with Wireless Energy Transfer

School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

Funds: The National Natural Science Foundation of China (61873223)

摘要

摘要: 為了解決城市場(chǎng)景中無(wú)人機(jī)(UAV)與地面終端設(shè)備(GUs)間易受到障礙物阻擋的問(wèn)題，該文提出一種基于智能反射面(IRS)輔助的UAV供能通信網(wǎng)絡(luò)吞吐量最大化算法。首先，在滿足能量因果、IRS相移、UAV移動(dòng)性等約束條件下，建立了一個(gè)聯(lián)合IRS相移設(shè)計(jì)、GU無(wú)線資源分配、UAV飛行軌跡設(shè)計(jì)的多變量耦合優(yōu)化模型。其次，通過(guò)快坐標(biāo)下降法(BCD)將原非凸問(wèn)題轉(zhuǎn)換為3個(gè)易于處理的子問(wèn)題，并通過(guò)三角不等式、引入松弛變量、連續(xù)凸近似(SCA)等方法，對(duì)子問(wèn)題進(jìn)行轉(zhuǎn)化求解。仿真結(jié)果表明，該文所提算法具有較好的收斂性，同時(shí)可有效提高系統(tǒng)總吞吐量。
- 無(wú)人機(jī)通信網(wǎng)絡(luò) /
- 智能反射面 /
- 無(wú)線資源分配
Abstract: In order to mitigate the adverse effect of blockages between the Unmanned Aerial Vehicle (UAV) and Ground Users (GUs), a throughput maximization algorithm for an Intelligent Reflecting Surface (IRS)-aided UAV communication network is proposed. First, considering the constraints of the energy causality, the IRS phase-shift, the UAV mobility, etc, a multi-variable coupling optimization problem is proposed with jointly optimizing the phase-shift of the IRS, the resource allocation of GUs, and the UAV trajectory. Second, the original non-convex problem is decomposed into three simpler sub-problems via the Block Coordinate Descent (BCD), which are tackled by the triangle inequality, introducing the slack variables and Successive Convex Approximation (SCA). Numerical results show that the proposed algorithm has a desirable convergence, as well as improves effectively the system sum-throughput.
- Unmanned Aerial Vehicle (UAV) communications /
- Intelligent Reflecting Surface (IRS) /
- Wireless resource allocation

HTML全文

圖 1 IRS輔助的UAV供能通信網(wǎng)絡(luò)

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圖 2 時(shí)隙分配

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圖 3 不同參數(shù)下UAV軌跡

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圖 4 算法收斂性驗(yàn)證

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圖 5 SCA逼近程度驗(yàn)證圖

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圖 6 總吞吐量與IRS元件數(shù)量的關(guān)系

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表 1 基于BCD的資源分配算法

初始化系統(tǒng)參數(shù)：$ {{\boldsymbol{\varTheta }}^{(0)}} $, $ {{\boldsymbol{P}}^{(0)}} $, $ {\boldsymbol{t}}_{\text{E}}^{(0)} $, $ {\boldsymbol{t}}_m^{(0)} $, $ {{\boldsymbol{q}}^{(0)}} $, $ {\psi ^{(0)}} $；設(shè)置最大迭代次數(shù)$ {L_{{\text{max}}}} $；設(shè)置收斂精度$ \varepsilon \gt 0 $；迭代次數(shù)$ l = 0 $；
(1) Repeat
(2) 設(shè)置迭代次數(shù)$ l = l + 1 $；
(3) 根據(jù)給定的$ {{\boldsymbol{P}}^{(l - 1)}} $, $ {\boldsymbol{t}}_{\text{E}}^{(l - 1)} $, $ {\boldsymbol{t}}_m^{(l - 1)} $, $ {{\boldsymbol{q}}^{(l - 1)}} $，通過(guò)式(11)更新$ {{\boldsymbol{\varTheta }}^{(l)}} $；
(4) 根據(jù)給定的$ {{\boldsymbol{\varTheta }}^{(l)}} $和$ {{\boldsymbol{q}}^{(l - 1)}} $，通過(guò)求解問(wèn)題式(13)得到$ {{\boldsymbol{P}}^{(l)}} $, $ {\boldsymbol{t}}_{\text{E}}^{(l)} $和$ {\boldsymbol{t}}_m^{(l)} $；
(5) 根據(jù)給定的$ {{\boldsymbol{P}}^{(l)}} $, $ {\boldsymbol{t}}_{\text{E}}^{(l)} $, $ {\boldsymbol{t}}_m^{(l)} $和$ {{\boldsymbol{\varTheta }}^{(l)}} $，通過(guò)求解問(wèn)題式(20)得到$ {{\boldsymbol{q}}^{(l)}} $；
(6) Until $ \|{\psi ^{(l)}} - {\psi ^{(l - 1)}}\| \le \varepsilon $或者$ l \ge {L_{{\text{max}}}} $；
(7) 結(jié)束并輸出結(jié)果。

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參考文獻(xiàn)(15)

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