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研究生:施宛廷
研究生(外文):Shih, Wan-Ting
論文名稱:具有情境感知技術的行動通訊系統
論文名稱(外文):Mobile Communication Systems with Situational Awareness Techniques
指導教授:蔡尚澕溫朝凱
指導教授(外文):Tsai, Shang-HoWen, Chao-Kai
口試委員:林源倍簡鳳村鍾偉和洪樂文蔡尚澕溫朝凱
口試委員(外文):Lin, Yuan-PeiChien, Feng-TsunChung, Wei-HoHong, Yao-WinTsai, Shang-HoWen, Chao-Kai
口試日期:2023-07-7
學位類別:博士
校院名稱:國立陽明交通大學
系所名稱:電控工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:中文
論文頁數:127
中文關鍵詞:整合感知和通訊情境感知波束對準天線選擇即時定位與地圖建構
外文關鍵詞:Integrated sensing and communication (ISAC)situational awarenessbeam alignmentantennal selectionsimultaneous localization and mapping (SLAM)
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第五代行動通訊(5G)已經陸續在各國商業化,隨著工業物聯網和車聯網的標準化,行動通訊已經由人聯網擴展至物聯網。預計到2030 年將進入6G 時代,與前幾代行動通訊系統相比,6G 最大的不同之處在於感知將成為其除了通訊之外的另一個基礎功能。智慧型手機是目前最普遍的個人行動通訊裝置,整合感知和通訊(Integrated Sensing and Communication,簡稱ISAC)成為一項具有前景的技術,該技術在單一裝置中將感知和通訊兩種功能整合在一起,以促進數據交換並同時實現情境感知。然而,智慧型手機的限制將對實施情境感知帶來許多挑戰。因此,本論文對情境感知在實際場景中的性能進行了評估和分析。

從頻段而言,與中/低頻段相比,毫米波有可能實現更高的目標定位、檢測和追蹤精度,而無處不在的WiFi 也為環境感知提供了更多機會。然而,在面臨易受遮擋、移動和斷開連接等情況下,毫米波需要解決通訊方面的挑戰。與毫米波的高解析度相比,使用WiFi 信號進行環境感知將面臨頻寬和天線尺寸等各種限制。為此,我們開發了多種有效的演算法來應對這些挑戰,以提高性能並減少延遲。此外,我們還透過分析性能上限來驗證我們的演算法。

在論文[1] 中,我們介紹並分析了一種利用環境訊息來輔助切換毫米波實際天線面板的方法。該方法快速選擇未被遮擋的天線面板及其對應的波束,從而避免因遮擋導致的通訊品質差或斷線。全面的分析和廣泛的模擬結果表明,我們提出的方法接近最佳解決方案的性能。即使在復雜的多路徑場景中,我們的方法也能立即識別來自其他天線的最佳波束。我們在測試平台上實際實施了該方法。實驗結果顯示,在阻塞條件下,我們提出的方法實現了高效且無縫的連接。

在論文[2] 中,我們探討了現有第五代新無線(5G new radio,簡稱5G NR)所定義的波束管理框架中,波束預測的情境感知潛力。我們提出了一種基於Cramér-Rao 下限的分析方法,用於量化六維位置的幾何信息,包括三維空間位置和以三維方式表示的方向偏差。這些信息使我們能夠預測環境中任何位置的波束對準性能,以實現連續的無線通訊。我們的分析為在阻塞條件下選擇最佳的波束和天線面板策略以保持穩定通信提供了寶貴的見解。模擬結果顯示,我們的方法的性能幾乎與基於5G NR 波束管理的現有最佳解決方案一樣出色。

最後,我們解決了由於頻寬有限和天線尺寸受限而導致的Wi-Fi 訊號感知問題。在論文[3] 中,我們提出了一種輕量級、始終在線且以用戶為中心的感知解決方案,該解決方案利用了廣泛使用的移動設備。透過模擬和實驗驗證了所提出的解決方案,證明即使在具有挑戰性的條件下也可以實現分米級定位,從而顯著提高準確性。
The fifth-generation (5G) mobile communication has been gradually commercialized in various countries. With the standardization of industrial IoT and vehicle-to-everything (V2X) communication, mobile communication has expanded from human-centric networking to the realm of the Internet of Things (IoT). It is projected that by 2030, we will enter the era of 6G, which represents a significant departure from the previous generations of mobile communication systems. In 6G, apart from communication, the perception will become another fundamental capability. Smartphones are currently the most ubiquitous personal mobile devices. The integration of sensing and communication, known as Integrated Sensing and Communication (ISAC), is a promising technology that combines these two functions within a single device. This integration facilitates data exchange and enables context-awareness. However, the limitations of smartphones pose numerous challenges in implementing context-awareness. Hence, this thesis evaluates and analyzes the performance of context-awareness in practical scenarios.

In terms of frequency bands, millimeter wave (mmWave) offers the potential for higher precision in target localization, detection, and tracking compared to mid/low-frequency bands, while ubiquitous WiFi provides more opportunities for environmental perception. However, millimeter waves face challenges in communication when dealing with issues such as blockage, movement, and disconnections. Utilizing WiFi signals for environmental perception, in contrast to the high resolution of millimeter waves, encounters various limitations such as bandwidth and antenna size. To address these challenges, we have developed multiple effective algorithms to enhance performance and reduce latency. Furthermore, we validate our algorithms by analyzing the upper limits of performance.

In [1], we introduce and analyze a method that utilizes environmental information to assist in the switching of practical antenna panels in mmWave. This method rapidly selects the unobstructed antenna panel and its corresponding beam, thereby avoiding poor communication quality or disconnection caused by the blockage. Comprehensive analysis and extensive simulations demonstrate that our proposed method approaches the performance of Oracle solutions, which can identify the best beams from other antennas instantly, even in complex multipath scenarios. We implement the method on a software-defined radio and integrate it into the fifthgeneration New Radio (5G NR) physical layer. Over-the-air experiments demonstrate that our proposed method achieves efficient and seamless connections under hand-blockage conditions.

In [2], we explore the potential of situational awareness for beam prediction in the existing 5G NR beam management framework. We propose an analytical framework based on the Cramér-Rao lower bound to quantify the geometric information about the 6-dimension (6D) position of the reflector, including the 3D position and 3D orientation deviation. This information enables us to predict beam alignment performance anywhere in the environment for uninterrupted wireless access. Our analysis provides valuable insights into selecting the optimal beam and antenna panel strategy to maintain stable communication under blocking conditions. Simulation results demonstrate that our approach performs almost as well as existing Oracle solutions based on beam management for 5G NR.

Finally, we address the sensing problem with WiFi signals due to limited bandwidth and constrained antenna size. In [3], we propose a lightweight, always-on, and user-centric sensing solution that utilizes widely available mobile devices. The proposed solution is verified by simulations and experiments, demonstrating that decimeter-level positioning accuracy can be achieved even under challenging conditions, thereby significantly improving accuracy.
摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Research Problems and Contributions . . . . . . . . . . . . . . . . . . . . . . 3
1.1.1 Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1.2 Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cramér-Rao Lower Bound . . . . . . . . 4
1.1.3 EasyAPPos: Positioning Wi-Fi Access Points by Using a Mobile Phone 5
1.2 Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.1 Beam Management in 5G NR Standard . . . . . . . . . . . . . . . . . . . . . 8
2.2 Passive Scanning in WiFi Protocol . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Antenna Configuration on Mobile . . . . . . . . . . . . . . . . . . . . . . . . 10
3 Fast Antenna and Beam Switching Method for mmWave Handsets with Hand Blockage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 Motivation and Related works . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . 15
3.3 Fast-ABS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.2 Relationship of Three-tuples between Antenna Modules . . . . . . . . 19
3.3.3 Antenna Switching and Beam Alignment . . . . . . . . . . . . . . . . 22
3.3.4 Path Parameter Estimation Algorithm . . . . . . . . . . . . . . . . . . 24
3.3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4.1 CRLB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.4.2 Selection of an Analog Beam Codebook . . . . . . . . . . . . . . . . . 30
3.4.3 Error of Virtual CSI . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.5 Simulations and Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.5.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4 Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cramér-Rao Lower Bound . . . . . . . . . . . . . . . . . . . . 45
4.1 Related Works and Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.2 System Model and Problem Statement . . . . . . . . . . . . . . . . . . . . . . 47
4.2.1 Geometric Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.2.2 Features of Channel Model . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2.3 Signal Model and Problem Statement . . . . . . . . . . . . . . . . . . 50
4.3 Beam-Foreseeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Procedure and Performance Metric . . . . . . . . . . . . . . . . . . . 52
4.3.3 Selection of Paths and Antenna Panels . . . . . . . . . . . . . . . . . . 54
4.4 Performance Metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.1 Localization Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4.2 Localization-based Angular Accuracy . . . . . . . . . . . . . . . . . . 61
4.5 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.5.1 PEB, OEB, and AEB in Sensing Mode . . . . . . . . . . . . . . . . . 65
4.5.2 Performance of Beam Foreseeing . . . . . . . . . . . . . . . . . . . . 68
4.6 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5 EasyAPPos: Positioning WiFi Access Points by Using a Mobile Phone . . . . . . 72
5.1 Challenges and Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.1 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.1.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2 System Setup for WiFi AP Positioning . . . . . . . . . . . . . . . . . . . . . . 76
5.2.1 WiFi Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2.2 Workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
5.3 Algorithms of EasyAPPos . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.3.1 Multiple CSI acquisition with different rotations . . . . . . . . . . . . 79
5.3.2 Parameter-extracting Algorithm . . . . . . . . . . . . . . . . . . . . . 80
5.3.3 SLAM-LOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3.4 Update and Identification . . . . . . . . . . . . . . . . . . . . . . . . . 87
5.4 Simulations and Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5.4.1 Scanning Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
5.4.2 Rotation Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
5.4.3 Long Trajectory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4.4 UE Positioning by IMUs . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.4.5 Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
5.5 Chapter Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Appendix A Appendix for Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . 121
Appendix B Appendix for Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . 123
Curriculum Vitae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
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