智能超表面輔助多用戶系統(tǒng)的通用低復(fù)雜度波束成形設(shè)計

陳曉; 施建鋒; 朱建月; 潘存華

doi:10.11999/JEIT240051

智能超表面輔助多用戶系統(tǒng)的通用低復(fù)雜度波束成形設(shè)計

doi: 10.11999/JEIT240051

1.
南京信息工程大學(xué)人工智能學(xué)院(未來技術(shù)學(xué)院) 南京 210044
2.
南京信息工程大學(xué)電子與信息工程學(xué)院南京 210044
3.
東南大學(xué)移動通信國家重點實驗室南京 210096

基金項目: 國家自然科學(xué)基金(62101273, 62201274)，江蘇省自然科學(xué)基金(BK20210641, BK20220439)

詳細(xì)信息

作者簡介:
陳曉：女，講師，研究方向為智能超表面、大規(guī)模MIMO系統(tǒng)、基于深度學(xué)習(xí)通信技術(shù)等

施建鋒：男，副教授，研究方向為天地一體化網(wǎng)絡(luò)、波束成形理論、基于機器學(xué)習(xí)通信技術(shù)等

朱建月：女，講師，研究方向為非正交多址接入技術(shù)、無線資源管理等

潘存華：男，教授，研究方向為無線通信傳輸設(shè)計，信道估計與定位，通感一體化，AI輔助通信等

通訊作者:
陳曉　x.chen@nuist.edu.cn

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 485
- HTML全文瀏覽量: 128
- PDF下載量: 71
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-01-24
- 修回日期: 2024-03-15
- 網(wǎng)絡(luò)出版日期: 2024-03-26
- 刊出日期: 2025-01-31

General Low-complexity Beamforming Designs for Reconfigurable Intelligent Surface-aided Multi-user Systems

1.
School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
2.
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
3.
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62101273, 62201274), The Natural Science Foundation of Jiangsu Province of China (BK20210641, BK20220439)

摘要

摘要: 針對可重構(gòu)智能超表面(RIS)輔助多用戶系統(tǒng)中基站和RIS聯(lián)合波束成形設(shè)計問題，該文提出通用低復(fù)雜度聯(lián)合波束成形設(shè)計方案。首先，分析RIS輔助多用戶系統(tǒng)以最大化和數(shù)據(jù)速率為目標(biāo)的聯(lián)合波束成形非凸優(yōu)化問題。其次，利用波束導(dǎo)向矢量近似正交性設(shè)計RIS反射矩陣，進(jìn)一步利用迫零方法設(shè)計基站發(fā)射波束成形，并對多用戶進(jìn)行功率分配優(yōu)化。最后，討論該方案適用性并對比該方案的計算復(fù)雜度相比現(xiàn)有方案降低了一個數(shù)量級。仿真結(jié)果表明，所提通用低復(fù)雜度波束成形設(shè)計可以獲得較高和數(shù)據(jù)速率，并且采用最優(yōu)功率分配可以進(jìn)一步提高和數(shù)據(jù)速率。此外，仿真結(jié)果和理論分析都表明系統(tǒng)和數(shù)據(jù)速率隨RIS位置的變化而變化，該結(jié)論為RIS位置的選擇提供參考依據(jù)。
- 智能超表面 /
- 波束成形 /
- 和數(shù)據(jù)速率 /
- 低復(fù)雜度
Abstract: Objective Reconfigurable Intelligent Surface (RIS), an innovative technology for 6G communication, can effectively reduce hardware costs and energy consumption. Most researchers examine the joint BeamForming (BF) design problem in RIS-assisted Multiple-Input Single-Output (MISO) systems or single-user Multiple-Input Multiple-Output (MIMO) systems. However, few investigate the non-convex joint BF optimization problem for RIS-assisted multi-user MISO systems. The existing joint BF design approaches for these systems primarily rely on iterative algorithms that are complex, and some methods have a limited application range. Methods To address the issue, general low-complexity joint BF designs for RIS-assisted multi-user systems are considered. The communication system consists of a Base Station (BS) with an M -antenna configuration utilizing a Uniform Rectangular Array (URA), a RIS with N reflecting elements also arranged in a URA, and K single-antenna User Equipment (UEs). It is assumed that the transmission channel between the BS and UEs experiences blocking due to fading and potential obstacles in a dynamic wireless environment. The non-convex optimization challenge of joint BF design is analyzed, with the goal of maximizing the sum data rate for RIS-aided multi-user systems. The design process involves three main steps: First, the RIS reflection matrix ${\boldsymbol{\varTheta}} $ is designed based on the perfect channel state information obtained from both the BS-RIS and RIS-UE links. This design exploits the approximate orthogonality of the beam steering vectors for all transmitters and receivers using the URA (as detailed in Lemma 1). Second, the transmit BF matrix W at the BS is derived using the zero-forcing method. Third, the power allocation at the BS for multiple users is optimized using the Water-Filling (WF) algorithm. The proposed scheme is applicable to both single-user and multi-user scenarios, accommodating Line-of-Sight (LoS) paths, Rician channels with LoS paths, as well as Non-LoS (NLoS) paths. The computational complexity of the proposed joint BF design is quantified at a total complexity of ${\mathcal{O}}(N+K^2M+K^3) $. Compared with existing schemes, the computational complexity of the proposed design is reduced by at least an order of magnitude. Results and Discussions To verify the performance of the proposed joint BF scheme, simulation tests were conducted using the MATLAB platform. Five different schemes were considered for comparison: Scheme 1: BF design and Water-Filling Power Allocation (WFPA) proposed in this paper, utilizing Continuous Phase Shift (CPS) design without accounting for the limitations of the RIS phase shifter’s accuracy. Scheme 2: Proposed Beamforming (PBF) and WFPA with 2-bit Phase Shift (2PS) design, taking phase shift accuracy limitations into consideration. Scheme 3: 1-bit Phase Shift (1PS) design under PBF and WFPA. Scheme 4: 2PS design under Random BeamForming (RBF) and WFPA. Scheme 5: Equal Power Allocation (EPA) design under PBF and CPS. Initial numerical results demonstrate that the proposed BF design can achieve a high sum data rate, which can be further enhanced by employing optimal power allocation. Furthermore, under identical simulation conditions, the LoS scenario exhibited superior sum data rate performance compared to the Rician channel scenario, with a performance advantage of approximately 6 bit/(s?Hz). This difference can be attributed to the presence of multiple paths in the Rician channel, which increases interference and decreases the signal-to-noise ratio, thereby reducing the sum data rate. Additionally, when the distance between BS and UEs is fixed, and the RIS is positioned on the straight line between the BS and the UEs, the system sum data rate initially decreases and then increases as the distance between the RIS and UEs increases due to path loss. The simulation results confirm that when the RIS is situated near the UEs (i.e., further from the BS), improved data rate performance can be achieved. This improvement arises because the path loss of the RIS-UE link is greater than that of the BS-RIS link. Therefore, optimal data rate performance is attained when the RIS is closer to the UEs. Moreover, both the simulation results and theoretical analysis indicate that the sum data rate is influenced by the RIS location, offering valuable insights for the selection of RIS positioning. Conclusions This paper proposes a general low-complexity BF design for RIS-assisted multi-user communication systems. Closed-form solutions for transmit BF, power distribution of the BS, and the reflection matrix of the RIS are provided to maximize the system’s sum data rate. Simulation results indicate that the proposed BF design achieves higher data rates than alternative schemes. Additionally, both the simulation findings and theoretical analysis demonstrate that the sum data rate varies with the RIS’s location, providing a reference criterion for optimizing RIS placement.
- Reconfigurable Intelligent Surface (RIS) /
- Beamforming /
- Sum data rate /
- Low complexity

HTML全文

圖 1 系統(tǒng)模型

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圖 2 和數(shù)據(jù)速率與反射元件數(shù)$ N $的關(guān)系，$ K = 4 $, $ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

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圖 3 LoS信道和萊斯信道和數(shù)據(jù)速率對比，$ K = 4 $,$ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

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圖 4 和數(shù)據(jù)速率與RIS和UEs間距$ {d_{{\text{RU}}}} $的關(guān)系，$ K = 4 $, $ N = 64 $

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圖 5 和數(shù)據(jù)速率與用戶數(shù)$ K $的關(guān)系，$ N = 64 $, $ {d_{{\text{BR}}}} = {d_{{\text{RU}}}} = 30{\kern 1pt} {\kern 1pt} {\text{m}} $

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1 低復(fù)雜度聯(lián)合波束成形設(shè)計算法

輸入：初始化$ \left( {{{\boldsymbol{W}}}{\text{,}}{ {\boldsymbol{\varTheta }}}{\text{,}}{ {\boldsymbol{P}}}} \right) $
步驟1　基于已知BS-RIS信道$ {{\boldsymbol{G}}} $和RIS-UEs信道$ {{{\boldsymbol{H}}}_{\text{r}}} $和引理1，根　據(jù)式(14)計算RIS反射矩陣$ {{\boldsymbol{\varTheta}} } $；
步驟2　基于ZF理論，根據(jù)式(19)計算BS發(fā)射波束成形$ {{\boldsymbol{W}}} $；
步驟3　基于WF理論，根據(jù)式(23)計算功率分配矢量$ {{\boldsymbol{P}}} $；
步驟4　輸出優(yōu)化得到的$ \left( {{{\boldsymbol{W}}}{\text{,}}{ {\boldsymbol{\varTheta}} }{\text{,}}{ {\boldsymbol{P}}}} \right) $。

下載: 導(dǎo)出CSV

表 1 波束成形方案計算復(fù)雜度對比

文獻(xiàn)	復(fù)雜度	參數(shù)
文獻(xiàn) [3]	$ \mathcal{O}\left( {{N^6}} \right) $	N：RIS反射元件數(shù)
文獻(xiàn)[18]	$ \mathcal{O}\left( {{I_{\text{o}}}\left( {{I_{\text{a}}}{M^2}{K^2} + {I_{\text{p}}}{N^2}} \right)} \right) $	M：基站發(fā)射天線數(shù)
文獻(xiàn)[16]	$ \mathcal{O}\left( {Q\left( {{M^3} + M{N^2} + N!} \right)} \right) $	K：用戶數(shù)
文獻(xiàn)[17]	$ \mathcal{O}\left( {NI\left( {K{M^2}} \right)} \right) $	$ {I_{\text{o}}} $,$ {I_{\text{a}}} $,$ {I_{\text{p}}} $,$ I $：迭代次數(shù)
本文	$ \mathcal{O}\left( {N + {K^2}M + {K^3}} \right) $	Q：預(yù)設(shè)訓(xùn)練集數(shù)目

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