可重構(gòu)智能表面輔助的V2I通信系統(tǒng)聯(lián)合波束賦形算法

仲偉志; 何藝; 段洪濤; 萬詩晴; 范振雄; 朱秋明; 林志鵬

doi:10.11999/JEIT231324

可重構(gòu)智能表面輔助的V2I通信系統(tǒng)聯(lián)合波束賦形算法

doi: 10.11999/JEIT231324 cstr: 32379.14.JEIT231324

1.
南京航空航天大學電磁頻譜空間認知動態(tài)系統(tǒng)工業(yè)與信息化部重點實驗室南京 211106
2.
國家無線電監(jiān)測中心北京 100144

基金項目: 江蘇省重點研發(fā)計劃(產(chǎn)業(yè)前瞻與關(guān)鍵核心技術(shù))(BE2022067, BE2022067-1, BE2022067-3)，國家自然科學基金(62271250)，南京航空航天大學研究生科研與實踐創(chuàng)新計劃(xcxjh20231507)

詳細信息

作者簡介:
仲偉志：女，副教授，研究方向為5G中的毫米波通信、波束成形、波束跟蹤技術(shù)和無人機軌跡規(guī)劃

何藝：女，碩士生，研究方向為車載毫米波通信，可重構(gòu)智能表面聯(lián)合波束賦形

段洪濤：男，正高級工程師，研究方向為無人機通信與反制，頻譜管理，短波及超短波監(jiān)測等

萬詩晴：女，碩士生，研究方向為無人機通信，可重構(gòu)智能表面聯(lián)合波束賦形

范振雄：男，高級工程師，研究方向為無人機通信與反制，超短波干擾定位及查找，短波測向等

朱秋明：男，教授，研究方向為電磁信號傳播機理、無線信道測量建模應(yīng)用、電磁頻譜語義孿生以及通信裝備智能化測試等

林志鵬：男，副研究員，研究方向為高維信道參數(shù)估計、大規(guī)模陣列信號處理、無人機通信、頻譜信號感知及重構(gòu)等

通訊作者:
仲偉志　zhongwz@nuaa.edu.cn

中圖分類號: TN929.5
計量
- 文章訪問數(shù): 432
- HTML全文瀏覽量: 211
- PDF下載量: 102
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2023-11-30
- 修回日期: 2024-06-13
- 網(wǎng)絡(luò)出版日期: 2024-06-21
- 刊出日期: 2024-08-30

Joint Beamforming Algorithm for Reconfigurable Intelligent Surface-aided V2I Communication System

1.
Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2.
State Radio Monitoring Center, Beijing 100144, China

Funds: The Key Technologies R&D Program of Jiangsu (Prospective and Key Technologies for Industry) (BE2022067, BE2022067-1, BE2022067-3), The National Natural Science Foundation of China (62271250), The Postgraduate Research and Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (xcxjh20231507)

摘要

摘要: 為解決基于信道先驗知識的聯(lián)合波束賦形方法受限于多變的車輛與交通基礎(chǔ)設(shè)施(V2I)通信場景且信道估計開銷過大等問題，該文結(jié)合環(huán)境態(tài)勢感知，提出一種基于無線傳播鏈路預(yù)測的聯(lián)合波束賦形方法。該方法首先利用射線追蹤模擬器構(gòu)建了可重構(gòu)智能表面(RIS)輔助的V2I毫米波通信系統(tǒng)模型，通過改變環(huán)境態(tài)勢以獲取多樣的無線傳播鏈路數(shù)據(jù)來構(gòu)建數(shù)據(jù)集。其次，使用該數(shù)據(jù)集訓練基于機器學習的無線傳播鏈路預(yù)測模型。最后，在最大發(fā)射功率約束條件下，構(gòu)建了聯(lián)合波束賦形問題模型，并基于預(yù)測結(jié)果采用交替迭代優(yōu)化方法(AIOA)優(yōu)化基站波束賦形矩陣和RIS相移矩陣，以實現(xiàn)同步通信車輛用戶最小信干噪比(SINR)的最大化。仿真結(jié)果驗證了該方法的有效性，通過引入非信道先驗知識驅(qū)動，降低了信道探測開銷，提高了該方法在V2I場景中的可行性。
- V2I通信 /
- 可重構(gòu)智能表面 /
- 聯(lián)合波束賦形 /
- 環(huán)境態(tài)勢感知 /
- 信道預(yù)測
Abstract: In order to address the limitations of the joint beamforming method based on channel prior knowledge, which is constrained by multivariate Vehicle-to-Infrastructure (V2I) communication scenes and suffers from large overhead caused by channel estimation, a wireless propagation link prediction-based joint beamforming method assisted by environmental situation awareness is proposed in this paper. Firstly, a model of Reconfigurable Intelligent Surface (RIS) assisted mmWave communication system for V2I networks is established using a ray tracer. To build a dataset, diverse data of wireless propagation links is obtained by changing the environmental situation. Then, this dataset is used to train a machine learning-based wireless propagation link prediction model. Finally, the joint beamforming problem under the constraint of maximum transmission power is modeled. Additionally, based on the prediction outcome, the beamforming matrix of base station and the phase shift matrix of RIS are optimized using Alternating Iterative Optimization Algorithm (AIOA) to maximize the minimum Signal to Interference plus Noise Ratio (SINR) among synchronous communication vehicle users. Simulation results validate the effectiveness of the proposed method. Introducing non-channel prior knowledge driven reduces channel detection overhead and improves feasibility in applying the proposed method to V2I scenes.
- Vehicle-to-Infrastructure(V2I) communication /
- Reconfigurable Intelligent Surface(RIS) /
- Joint beamforming /
- Environmental situation awareness /
- Channel prediction

HTML全文

圖 1 RIS輔助的V2I多用戶毫米波通信系統(tǒng)示意圖

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圖 2 環(huán)境態(tài)勢感知的實現(xiàn)方法示意圖

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圖 3 坐標系示意圖

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圖 4 場景模型以及無線傳播鏈路可視化結(jié)果

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圖 5 基于無線傳播鏈路預(yù)測的聯(lián)合波束賦形方法流程圖

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圖 6 交替迭代優(yōu)化算法流程圖

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圖 7 不同迭代停止精度$ {\beta _2} $下的AIOA性能比較

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圖 8 不同維度RIS的AIOA性能比較

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圖 9 基于不同編碼方式預(yù)測結(jié)果的AIOA性能比較

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圖 10 不同聯(lián)合波束賦形方法的$ {\text{E}}[\min ({\text{SIN}}{{\text{R}}_k})] $性能比較

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圖 11 基于真實信道和預(yù)測信道的AIOA總頻帶利用率比較

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圖 12 定位誤差的影響

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圖 13 不同車輛用戶數(shù)對聯(lián)合波束賦形性能的影響

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1 二分搜索算法

初始化參數(shù)：$ {\beta _1} $, $ P $, $ \delta $, $ {\boldsymbol{\varTheta}} $,$ {\lambda _{\text{u}}} $, $ {\lambda _{\text{l}}} = 0 $
(1) While $ ({\lambda _{\text{u}}} - {\lambda _{\text{l}}}) \ge {\beta _1} $ do
(2) 　$ t = ({\lambda _{\text{u}}}{\text{ + }}{\lambda _{\text{l}}})/2 $
(3) 　利用半定松弛方法解決問題(P2a)
(4) 　if 當前的$ t $和$ {\boldsymbol{\varTheta}} $可以使得問題(P2a)狀態(tài)為可行 then
(5) 　　$ {\lambda _{\text{u}}} = \delta t,{\text{ }}{\lambda _{\text{l}}} = t $
(6) 　else
(7) 　　$ {\lambda _{\text{u}}} = t $
(8) 　end if
(9) end while
輸出$ t;{\text{ }}{\boldsymbol{X}}_k^{{\text{opt}}},\forall k = \{ 1,2, \cdots ,K\} $

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表 1 通用仿真參數(shù)

參數(shù)	值	參數(shù)	值
M	9	單車道尺寸/(長, 寬)(m)	(100, 5)
N	{16, 36}	BS坐標/(水平位置, 高度)(m)	(50, 3)
L	2	RIS坐標/(水平位置, 高度)(m)	(50, 8)
K	2	小型車輛尺寸/(長, 寬, 高)(m)	(4, 2, 1.5)
$ \sigma _k^2 $(dB)	–110	大型車輛尺寸/(長, 寬, 高)(m)	(9, 2.5, 2.5)
$ \kappa $(dB)	4	信號中心頻率(GHz)	28

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表 2 不同編碼方式對應(yīng)的環(huán)境態(tài)勢特征下預(yù)測模型在測試集上的MSE

	T_1,1	T_2,1	T_3,1	T_4,1	T_5,1	T_1,2	T_2,2	T_3,2	T_4,2	T_5,2
TVOM	3.07E–07	4.50E–07	2.82E–04	1.71E–02	1.44E–06	7.07E–04	1.24E–03	5.75E–03	2.18E–02	3.25E–03
NOCM	1.86E–07	1.96E–07	9.45E–07	3.96E–05	1.21E–06	3.66E–04	8.60E–04	2.73E–04	4.62E–04	1.55E–03
OLCM,N_v=2	1.22E–07	8.66E–09	7.14E–08	4.04E–07	3.92E–07	2.66E–04	7.75E–04	4.59E–04	5.11E–05	2.13E–03

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