智能反射面輔助無線網(wǎng)絡(luò)性能及最優(yōu)位置分析

束鋒; 賴斯豪; 劉川; 高煒; 董榕恩; 王艷

doi:10.11999/JEIT240488

智能反射面輔助無線網(wǎng)絡(luò)性能及最優(yōu)位置分析

doi: 10.11999/JEIT240488 cstr: 32379.14.JEIT240488

束鋒^{1, 2},
賴斯豪¹,
劉川³,
高煒¹,
董榕恩^1, ,,
王艷¹

1.
海南大學(xué)信息與通信工程學(xué)院 ?？? 570228
2.
南京理工大學(xué)電子工程與光電技術(shù)學(xué)院南京 210094
3.
國網(wǎng)智能電網(wǎng)研究院有限公司北京 102209

基金項(xiàng)目: 國家重點(diǎn)研發(fā)計(jì)劃(2023YFF0612900)，國家自然科學(xué)基金(U22A2002, 62071234)，海南省科技專項(xiàng)基金(ZDKJ2021022)，海南大學(xué)科研啟動(dòng)項(xiàng)目(KYQD(ZR)-21008)，海南大學(xué)信息技術(shù)協(xié)同創(chuàng)新中心項(xiàng)目(XTCX2022XXC07)

詳細(xì)信息

作者簡(jiǎn)介:
束鋒：男，博士生導(dǎo)師，研究方向?yàn)橹悄軣o線通信、信息安全、大規(guī)模MIMO測(cè)向等

賴斯豪：男，碩士生，研究方向?yàn)镮RS輔助的無線通信

劉川：男，博士，研究方向?yàn)槟茉磾?shù)字化、電力通信網(wǎng)路

高煒：男，博士后，網(wǎng)絡(luò)架構(gòu)、無線網(wǎng)絡(luò)接入和無線電資源分配

董榕恩：女，博士，研究方向?yàn)榉较蛘{(diào)制、IRS輔助的無線網(wǎng)絡(luò)

王艷：女，博士生，研究方向?yàn)镮RS輔助的通信系統(tǒng)

通訊作者:
董榕恩　dre2000@163.com

中圖分類號(hào): TN92
計(jì)量
- 文章訪問數(shù): 353
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-06-16
- 修回日期: 2024-09-24
- 網(wǎng)絡(luò)出版日期: 2024-09-28
- 刊出日期: 2025-02-28

Performance and Optimal Placement Analysis of Intelligent Reflecting Surface-assisted Wireless Networks

SHU Feng^{1, 2},
LAI Sihao¹,
LIU Chuan³,
GAO Wei¹,
DONG Rongen^{1
, ,},
WANG Yan¹

1.
School of Information and Communication Engineering, Hainan University, Haikou 570228, China
2.
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
3.
State Grid Smart Grid Research Institute Co., Ltd., Beijing 102209, China

Funds: The National Key Research and Development Program of China (2023YFF0612900), The National Natural Science Foundation of China (U22A2002, 62071234), Hainan Province Science and Technology Special Fund (ZDKJ2021022), The Scientific Research Fund Project of Hainan University (KYQD(ZR)-21008), The Collaborative Innovation Center of Information Technology, Hainan University (XTCX2022XXC07)

摘要

摘要: 當(dāng)基站(BS)和用戶的位置固定，基站到智能反射面(IRS)與IRS到用戶的距離和一定時(shí)，該文在視距信道和瑞利信道下基于最大化系統(tǒng)可達(dá)速率準(zhǔn)則對(duì)無源和有源IRS的最優(yōu)放置位置進(jìn)行分析。首先，運(yùn)用相位對(duì)齊和大數(shù)定律推導(dǎo)了無源和有源IRS輔助無線網(wǎng)絡(luò)可達(dá)速率的閉合表達(dá)式；然后，分析了基站到IRS的路徑損耗指數(shù)${\beta _1}$和IRS到用戶的路徑損耗指數(shù)${\beta _2}$對(duì)IRS最優(yōu)部署位置的影響，即當(dāng)${\beta _{\text{1}}} \gt {\beta _{\text{2}}}$時(shí)，無源IRS的最優(yōu)部署位置始終靠近基站，隨著${\beta _1}$和${\beta _2}$的差距逐漸增大，有源IRS的最優(yōu)部署位置逐漸靠近基站；當(dāng)${\beta _1} \lt {\beta _2}$時(shí)，則得到相反的結(jié)論。仿真結(jié)果表明：當(dāng)${\beta _1} = {\beta _2}$且無源IRS到基站和到用戶的距離相等時(shí)，系統(tǒng)的可達(dá)速率性能最差。當(dāng)固定有源IRS處的噪聲功率且增加用戶處的噪聲功率時(shí)，IRS的最優(yōu)部署位置始終靠近用戶；當(dāng)固定后者增大前者時(shí)，IRS的最優(yōu)部署位置逐漸靠近基站。
- 智能反射面 /
- 大數(shù)定律 /
- 智能反射面最優(yōu)位置 /
- 可達(dá)速率
Abstract: Objective: Previous studies have extensively examined the performance of Intelligent Reflecting Surface (IRS)-assisted wireless communications by varying the location of the IRS. However, relocating the IRS alters the sum of the distances between the IRS and the base station, as well as the distances to users, leading to discrepancies in reflective channel transmission distances, which introduces a degree of unfairness. Additionally, the assumption that the path loss indices for the base station-to-IRS and IRS-to-user channels are equal is overly idealistic. In practical scenarios, the user’s height is typically much lower than that of the base station, and the IRS may be positioned closer to either the base station or the user. This disparity results in significantly different path loss indices for the two channels. Consequently, this paper focuses on identifying the optimal deployment location of the IRS while keeping the total distance fixed. The IRS is modeled to move along an ellipsoid or ellipsoidal plane defined by the base station and the user as focal points. The analysis provides insights into the optimal deployment of the IRS while taking into account a broader range of application scenarios, specifically addressing different path loss indices for the base station-to-IRS and IRS-to-user channels given a predetermined sum of the transmitting powers. Methods: Utilizing concepts of phase alignment and the law of large numbers, closed-form expressions for the reachability rate of both passive and active IRS-assisted wireless networks are initially derived for two scenarios: the line-of-sight channel and the Rayleigh channel. Following this, the study analyzes how the path loss exponents from the base station to the IRS and from the IRS to the user impact the optimal deployment location of the IRS. Results and Discussions: The reachability rate of a passive IRS-assisted wireless network, considering IRS locations under both line-of-sight and Rayleigh channels, is illustrated. It is evident that the optimal deployment location of the IRS is nearest to either the base station or the user when β₁=β₂. When β₁>β₂, the optimal deployment location of the IRS is obtained solely at the base station, while the least effective deployment location shifts progressively closer to the user. Conversely, a contrasting result is obtained when β₁<β₂. The above results verify the correctness of the theoretical derivation in Section 3.1.3. The reachability rate of an active IRS-assisted wireless network as a function of IRS location under line-of-sight and Rayleigh channels is depicted. The figure indicates that when β₁=β₂, the system’s reachability rate under the line-of-sight channel exceeds that of the Rayleigh channel, with the optimal deployment location of the active IRS positioned in proximity to the user. When β₁>β₂ (fixed β₂, increasing β₁), the optimal deployment location of the active IRS progressively approaches the base station. And when β₁<β₂, the optimal deployment location shifts closer to the user. The optimal deployment location of the IRS for IRS-assisted wireless networks operating under a Rayleigh channel, reflecting variations in the path loss index β, is portrayed. Notably, for passive IRS systems, regardless of the path loss index variations, the optimal deployment locations across three different cases yield consistent conclusions with those derived. For the active IRS, when β₁=β₂=β₁, the optimal deployment location gradually distances itself from the user ultimately approaching the IRS location at m (directly above the midpoint of the line connecting the base station and user). Conversely, when β₁>β₂, the optimal deployment position of the IRS increasingly aligns with the base station along an elliptical trajectory; conversely, when β₁<β₂, it shifts towards the user. The optimal deployment location of the active IRS under both line-of-sight and Rayleigh channels as a function of Igressively approaches the base station. And wRS reflected power P_I is displayed. The analysis indicates that in both channel conditions, as the IRS reflected power increases, the optimal deployment location for the active IRS progressively moves closer to the base station along an elliptical trajectory as P_I gradually increases. And at β₁=β₂ and P_I=P_B, the optimal deployment location of the active IRS maintains an equal distance from both the base station and the user. The system’s reachability rate in relation to the distance r from the base station to the active IRS, accounting for different user noise $\sigma_{\mathrm{U}}^2 $ and amplified noise $\sigma_{\mathrm{I}}^2 $ of the active IRS, is presented. When fixing $\sigma_{\mathrm{I}}^2 $ and gradually increasing $\sigma_{\mathrm{U}}^2 $, the optimal deployment location of the active IRS is situated closer to the user. Conversely, when fixing $\sigma_{\mathrm{U}}^2 $ and gradually increasing $\sigma_{\mathrm{U}}^2 $, the optimal deployment location gradually approaches the base station. Additionally, irrespective of increased noise levels, the system’s reachability rate demonstrates a tendency to decline. Conclusions: This paper examines the maximization of system reachable rates by varying the deployment locations of passive and active IRSs in line-of-sight and Rayleigh channel transmission scenarios. In the analysis, fixed positions are assumed for both the base station and the user, with the sum of the base station-to-IRS and IRS-to-user distances kept constant. Phase alignment and the law of large numbers are employed to derive a closed-form expression for the reachable rate. Theoretical analysis and simulation results provide several key insights: When β₁<β₂, the optimal deployment locations for both passive and active IRS are close to the user, the least favorable deployment locations for passive IRS move progressively closer to the base station as the difference between β₁ and β₂ increases. When β₁=β₂, the optimal deployment location for the active IRS remains near the user, while the passive IRS can be effectively placed near either the base station or the user. When β₁>β₂, the optimal deployment location of the passive IRS remains close to the base station. As the difference between β₁ and β₂ ncreases, the optimal deployment location of the active IRS gradually shifts closer to the base station. Additionally, as the amplified noise of the active IRS increases, its optimal deployment location moves closer to the base station. Conversely, when the noise at the user increases, the optimal deployment location of the active IRS is always closer to the user.
- Intelligent Reflecting Surface (IRS) /
- Law of large numbers /
- Optimal placement of IRS /
- Achievable rate

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圖 1 系統(tǒng)模型圖

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圖 2 系統(tǒng)可達(dá)速率與IRS部署位置的關(guān)系曲線圖

下載: 全尺寸圖片幻燈片

圖 3 IRS最優(yōu)部署位置與路徑損耗曲線圖

下載: 全尺寸圖片幻燈片

圖 4 有源IRS的最優(yōu)部署位置隨反射功率${P_{\text{I}}}$變化曲線圖

下載: 全尺寸圖片幻燈片

圖 5 在不同$\sigma _{\text{I}}^2$和$\sigma _{\text{U}}^2$情形下系統(tǒng)可達(dá)速率隨基站到有源IRS的距離$r$變化的關(guān)系曲線圖。

下載: 全尺寸圖片幻燈片

表 1 路徑損耗指數(shù)設(shè)置表

	無源IRS		有源IRS
	${\beta _1}$	${\beta _2}$	${\beta _1}$	${\beta _2}$
${\beta _1} \gt {\beta _2}$	3.0	2.2	3.5	2.5
${\beta _1} = {\beta _2}$	2.2	2.2	2.5	2.5
${\beta _1} \lt {\beta _2}$	2.2	3.0	2.5	3.5

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