混合智能反射面輔助的通信感知一體化：高能效波束成形設(shè)計

褚宏云; 楊夢瑤; 黃航; 鄭凌; 潘雪; 肖戈

doi:10.11999/JEIT230699

混合智能反射面輔助的通信感知一體化：高能效波束成形設(shè)計

doi: 10.11999/JEIT230699

褚宏云¹,
楊夢瑤^1, ,,
黃航²,
鄭凌¹,
潘雪¹,
肖戈¹

1.
西安郵電大學西安 710121
2.
南京電子設(shè)備研究所南京 210013

基金項目: 國家自然科學基金(62102314)，173計劃技術(shù)領(lǐng)域基金(2022-JCJQ-JJ-0730)，陜西省自然科學基金(2022JQ-635)

詳細信息

作者簡介:
褚宏云：女，講師，碩士生導(dǎo)師，研究方向為智能超表面使能無線通信系統(tǒng)關(guān)鍵技術(shù)

楊夢瑤：女，碩士生，研究方向為通信感知一體化

黃航：男，高級工程師，研究方向為電子對抗

鄭凌：男，講師，碩士生導(dǎo)師，研究方向為下一代網(wǎng)絡(luò)體系架構(gòu)、高性能網(wǎng)絡(luò)與交換、人工智能算法及其FPGA實現(xiàn)

潘雪：女，碩士生，研究方向為智能超表面信道估計

肖戈：男，碩士生，研究方向為智能超表面波束形成

通訊作者:
楊夢瑤　myyang1@yeah.net

中圖分類號: TN929.5
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- 文章訪問數(shù): 846
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- PDF下載量: 185
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2023-07-12
- 修回日期: 2023-11-14
- 錄用日期: 2023-11-14
- 網(wǎng)絡(luò)出版日期: 2023-11-21
- 刊出日期: 2024-06-30

Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication: Energy Efficient Beamforming Design

1.
Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2.
Nanjing Research Institute of Electronic Equipment, Nanjing 210013, China

Funds: The National Natural Science Foundation of China (62102314), The 173 Program for Technology (2022-JCJQ-JJ-0730), The Natural Science Foundation of Shaanxi Province (2022JQ-635)

摘要

摘要: 能量效率(EE)是5G+/6G無線通信的重要設(shè)計指標，而智能反射面(RIS)被普遍認為是改善EE的潛在手段。不同于被動RIS，混合RIS由有源和無源元件組成，對來波移相的同時可放大信號強度，能夠有效克服被動RIS引起的“乘性衰落”效應(yīng)。鑒于此，該文提出一種混合RIS輔助通信感知一體化(ISAC)的下行鏈路傳輸系統(tǒng)。為探究數(shù)據(jù)傳輸速率與能耗之間的內(nèi)在關(guān)聯(lián)，該文以RIS輔助ISAC網(wǎng)絡(luò)能量效率最大化為目標，在滿足基站(BS)發(fā)射功率、波束圖增益以及混合RIS功率和幅值約束的條件下，聯(lián)合優(yōu)化基站端的波束賦形和混合RIS的相移。為解決該復(fù)雜的分數(shù)規(guī)劃問題，提出基于交替優(yōu)化(AO)的算法來求解。為克服AO算法中引入輔助變量造成算法復(fù)雜度高的難題，利用耦合優(yōu)化變量的關(guān)聯(lián)，提出一種基于級聯(lián)深度學習網(wǎng)絡(luò)的求解算法。仿真結(jié)果表明，提出的混合RIS輔助ISAC方案在和速率、能效方面皆優(yōu)于現(xiàn)有方案，且算法收斂速度快。
- 通信感知一體化 /
- 能量效率最大化 /
- 混合可重構(gòu)智能超表面 /
- 聯(lián)合波束賦形 /
- 級聯(lián)深度學習
Abstract: Energy Efficiency (EE) is an important design metric for 5G+/6G wireless communications, and Reconfigurable Intelligent Surface (RIS) is widely recognized as a potential means to improve EE. Unlike passive RIS, hybrid RIS consists of both active and passive components, which can amplify the signal strength while phase-shifting the incoming wave, and can effectively overcome the “multiplicative fading” effect caused by fully passive RIS. In view of this, a hybrid RIS-assisted Integrated Sensing and Communication (ISAC) downlink transmission system is proposed in this paper. In order to investigate the intrinsic correlation between data transmission capacity and energy consumption, the paper jointly optimizes the beamforming and phase-shifting of hybrid RIS at the Base Station (BS) under the constraints of BS transmit power, beampattern gain, and hybrid RIS power and amplitude with the goal of maximizing the global EE in a multiuser network. To solve this complex fractional programming problem, an algorithm based on Alternating Optimization (AO) is proposed to solve it. To overcome the problem of high algorithm complexity caused by the introduction of auxiliary variables in the AO algorithm, a solution algorithm based on a cascaded deep learning network is proposed using the association of coupled optimization variables. Simulation results show that the proposed hybrid RIS-assisted ISAC scheme outperforms existing schemes in terms of sum rate and EE, and the algorithm converges quickly.
- Integrated Sensing and Communication (ISAC) /
- Maximizing energy efficiency /
- Hybrid reconfigurable intelligent surfaces /
- Joint beamforming /
- Cascade deep learning

HTML全文

圖 1 混合RIS輔助的ISAC系統(tǒng)模型

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圖 2 兩階段波束賦形網(wǎng)絡(luò)結(jié)構(gòu)圖

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圖 3 批量大小對損失的影響

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圖 4 不同基站發(fā)射功率下的能量效率

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圖 5 不同基站發(fā)射功率下的用戶和速率

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圖 6 不同RIS元件數(shù)目下的能量效率

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圖 7 波束圖

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算法1　算法整體流程
初始化：變量$ {b^{(0)}} $、$ {\xi ^{(0)}} $、$ {{\boldsymbol{W}}^{(0)}} $和$ {{\boldsymbol{\theta }}^{(0)}} $
迭代次數(shù)$ i = 1 $，最大迭代次數(shù)$ {I_{\max }} $
(1)While$ {f_i} - {f_{i - 1}} \ge \varepsilon $或$ i \lt {I_{\max }} $do
(2)根據(jù)式(11)更新拉格朗日對偶重組輔助變量$ {\xi ^{(i)}} $
(3)根據(jù)式(13)更新二次變換輔助變量$ {b^{(i)}} $
(4)利用CVX求解凸規(guī)劃問題$ {{\text{P}}_{{\text{2-1}}}} $，更新優(yōu)化變量$ {{\boldsymbol{W}}^{(i)}} $
(5)利用CVX求解凸規(guī)劃問題$ {{\text{P}}_{{\text{3}}}} $，更新優(yōu)化變量$ {{\boldsymbol{\varphi}} ^{(i)}} $
(6)更新能量效率$ \eta $，迭代次數(shù)$ i = i + 1 $
(7)End While

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表 1 部分仿真參數(shù)

參數(shù)名稱	符號	數(shù)值	參數(shù)名稱	符號	數(shù)值
BS發(fā)射天線數(shù)	$M$	8	用戶噪聲功率(dBm)	$\sigma _0^2$	–80
用戶數(shù)	$K$	4	RIS噪聲功率(dBm)	$\sigma _c^2$	–70
目標數(shù)	$L$	2	Rice因子	$ \rho $	10
RIS元件總數(shù)	$N$	256	最小波束圖增益(dB)	$ \varGamma $	10
RIS主動元件數(shù)	$T$	64	目標方向($ ^\circ $)	$ {\theta _1},{\theta _2} $	$ \pm 45 $
RIS最大消耗功率(dBm)	${P_0}$	10	能量放大系數(shù)	${{a,b}}$	0.8
RIS有源元件放大系數(shù)(dB)	$\alpha $	10	懲罰系數(shù)	$ {\beta _1},{\beta _2},{\beta _3} $	50

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