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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳少傑(Sao-Jie Chen) | |
dc.contributor.author | Chu-Han Lee | en |
dc.contributor.author | 李居翰 | zh_TW |
dc.date.accessioned | 2021-06-07T18:05:59Z | - |
dc.date.copyright | 2012-07-27 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-07-24 | |
dc.identifier.citation | [1] Federal Communications Commission (FCC), Spectrum Policy Task Force, ET Docket no. 02-135, Nov. 15, 2002.
[2] J. Mitola III and G. Q. Maguire Jr., “Cognitive Radio: Making Software Radios more Personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, Aug. 1999. [3] J. Mitola III, “Cognitive Radio: An Integrated Agent Architecture for Software Defined Radio,” PhD Dissertation, Royal Institute of Technology (KTH), 2000. [4] S. Haykin, “Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005. [5] W. Tuttlebee, Software Defined Radio, Enabling Technologies. John Wiley & Sons, Ltd., the 1st Edition, 2002. [6] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Communications Surveys & Tutorials, vol.11, no.1, pp. 116-130, first quarter, 2009. [7] H. Tang, “Some Physical Layer Issues of Wide-band Cognitive Radio Systems,” New Frontiers in Dynamic Spectrum Access Networks, pp. 151–159, Nov. 2005. [8] nVIDIA, nVIDIA CUDA C Programming Guide ver. 4.0, May 2011. [9] E. Lindholm, J. Nickolls, S. Oberman, and J. Montrym, “nVIDIA Tesla: A Unified Graphics and Computing Architecture,” IEEE Micro, vol. 28, no. 2, pp. 39-55, May 2008. [10] nVIDIA, nVIDIA CUDA C Programming Guide ver. 4.0, May 2011 [11] nVIDIA, “nVIDIA GF100: World’s Fastest GPU Delivering Great Gaming Performance with True Geometric Realism,” Production White Paper, 2009. [12] nVIDIA, “nVIDIA’s Next Generation CUDA Compute Architecture: Fermi,” Production White Paper, 2009. [13] R. Faber, CUDA Application Design and Development, Morgan Kaufmann Publishers, Nov. 14, 2011. [14] IEEE Standard 802.11-2007, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, IEEE, Jun. 2007. [15] IEEE Standard 802.16-2004, Part 16: Air Interface for Fixed Broadband Wireless Access Systems, IEEE Computer Society and the IEEE Microwave Theory and Techniques Society, Oct. 2004. [16] D. Cabric, S. M. Mishra, and R. B. Brodersen, “Implementation Issues in Spectrum Sensing for Cognitive Radios,” 38th Annual Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 772-776, Nov. 2004. [17] K. C. Chen and R. Prasad, Cognitive Radio Networks, Wiley Publisher, Jun. 30, 2009. [18] W. A. Gardner, “Signal Interception: A Unifying Theoretical Framework for Feature Detection,” IEEE Transactions on Communications, vol. 36, no. 8, pp. 897-906, Aug. 1988. [19] S. H. Sohn, N. Han, J. M. Kim, and J. W. Kim, “OFDM Signal Sensing Method based on Cyclostationary Detection,” Cognitive Radio Oriented Wireless Networks and Communications, pp. 63-68, Aug. 2007 [20] R. S. Roberts, W. A. Brown, and H. H. Loomis Jr., “Computationally Efficient Algorithms for Cyclic Spectral Analysis,” IEEE Signal Processing Magazine, vol. 8, no. 2, pp. 38-49, Apr. 1991. [21] W. A. Gardner, Cyclostationarity in Communications and Signal Processing, IEEE Press, 1994. [22] J. Kim, S. Hyeon, and S. Choi, “Implementation of an SDR System using Graphics Processing Unit,” IEEE Communications Magazine, vol. 48, no. 3, pp. 156–162, Mar. 2010. [23] A. Akapyev and V. Krylov, “Implementation of 802.11n on 128-Core Processor,” ISCA International Conference on Parallel and Distributed Computing (and Communications) Systems, pp.56-60, Sep. 2008. [24] nVIDIA, CUFFT Library, Feb. 2011. [25] N. J. Carter, “Implementation of Cyclic Spectral Analysis Methods”, Master Thesis, Naval Postgraduate School, Dec. 1992. [26] S. R. Schnur, “Identification and Classification of OFDM Based Signals using Preamble Correlation and Cyclostationary Feature Extraction,” Master Thesis, Naval Postgraduate School, Sep. 2009. [27] nVIDIA, “CUDA Toolkit 4.0 Feature and Overview,” May, 2011 [28] nVIDIA, “The CUDA Compile Driver NVCC,” Oct. 2011 [29] J. Dedrick, K. L. Kraemer, and T. Tsai, “ACER: an IT Company Learning to Use Information Technology to Compete,” Center for Research on Information Technology and Organization, University of California, Oct. 1999. [30] F. Ge and C. W. Bostian, “A Parallel Computing Based Spectrum Sensing Approach for Signal Detection under Conditions of Low SNR and Rayleigh Multipath Fading,” the 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks, pp. 1-10, Oct. 2008. [31] IBM, Sony Computer Entertainment Inc., and Toshiba Corp., “The Design and Implementation of a First-Generation CELL Processor,” IEEE International Solid-State Circuits Conference, vol.1, pp. 184-592, Feb. 2005. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16231 | - |
dc.description.abstract | 在1999年時, Mitola 提出了感知無線電(Cognitive Radio)的理念來提升頻譜使用的效率。感知無線電是比起傳統無線電能更具有彈性及智能地使用無線通訊環境。感知無線電能偵測不同的無線電環境來調整自己的系統參數以適應不同的通訊環境。因此它必須及時且準確地得知目前環境中頻譜使用的情況。許多頻譜偵測的演算法已被提出,其中最常見的方法有三種:能量偵測 (Energy Detection)、波形偵測 (Waveform-based Detection)、週期式穩態特徵偵測 (Cyclostationary-Based Detection)。
在本論文中,我們實做了兩種頻譜偵測演算法:波形偵測和週期式穩態偵測。這兩種頻譜偵測演算法能有效從雜訊中分辨出待測訊號,但卻有較高計算複雜度。為了有效減少計算時間來提高偵測速度,我們採用繪圖處理器(Graphic Processing Unit) 透過「統一計算架構」(Compute Unified Device Architecture)來實現這些偵測方法。通過有效的CUDA平行化技術,我們可以達到比序列版偵測演算法更快的速度。最後我們將結果與其他多核心平台做比較,來證明GPU是個具有潛力來開發高複雜度演算法之平台。 | zh_TW |
dc.description.abstract | In 1999, Mitola proposed the idea of cognitive radio (CR), which is a promising technology to achieve efficient spectrum utilization. Cognitive radio is more flexible and intelligent than traditional wireless communication techniques. Cognitive radios have the ability to sense their operating environment and automatically switch between different standards. A cognitive radio system needs to sense the primary user radio spectrum fast and accurately. Various detection approaches have been proposed for spectrum sensing, such has energy detection, waveform-based sensing, and cyclostationarity-based sensing methods.
In this Thesis, we implement two kinds of spectrum sensing techniques, waveform-based detection and cyclostationary-based sensing methods. Both of these algorithms have the ability to separate the signal of interest from the noise or interference and own a high computation complexity. In order to reduce the computation time and increase the detection speed, we implemented these algorithms on an GPU (Graphic Processing Unit) platform using CUDA (Compute Unified Device Architecture) 4.0. By efficiently using the parallel processing power of CUDA, our methods showed tremendous speed-up over the sequential implementations in a multi-standards environment. In the end, we also compared our results with the results of other multi-core device to show that GPU is a promising platform to implement high-speed parallel algorithms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T18:05:59Z (GMT). No. of bitstreams: 1 ntu-101-R99943079-1.pdf: 5001386 bytes, checksum: 947647ee47d645ddc1d6c07ec7c8557c (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | ABSTRACT I
LIST OF FIGURES V LIST OF TABLES IX CHAPTER 1 INTRODUCTION 1 1.1 Cognitive Radios 1 1.2 Spectrum Sensing 3 1.3 Graphic Processing Unit and Compute Unified Device Architecture 5 1.4 Thesis Organization 7 CHAPTER 2 COMPUTE UNIFIED DEVICE ARCHITECTURE 9 2.1 CUDA Programming Models 9 2.1.1 Execution Model of CUDA 10 2.1.2 Thread Hierarchy 11 2.1.3 CUDA Device Memory Types 12 2.1.4 CUDA Compute Capability 15 2.2 CUDA Hardware Architecture 16 2.2.1 GPU Hardware Architecture 16 2.2.2 Thread Assignment and Scheduling 21 2.3 CUDA Programming Guides 22 2.3.1 C Extensions of CUDA 22 2.3.2 Programming Guides 25 CHAPTER 3 SYSTEM OVERVIEW 27 3.1 Primary Systems 27 3.1.1 Introduction of OFDM Physical Layer 27 3.1.2 IEEE 802.11-2007 Physical Layer 31 3.1.3 IEEE 802.16-2004 Physical Layer 35 3.2 Spectrum Sensing Methods 40 3.2.1 Energy Detection Method 42 3.2.2 Waveform Based Detection with Preamble Cross-Correlation 43 3.2.3 Pilot Subcarrier Spectral Cyclostationary based Detection 47 CHAPTER 4 SYSTEM IMPLEMENTATION ON GPU 55 4.1 Test Vehicle 55 4.2 Parallelizing Waveform-based Detection on GPU 57 4.2.1 Parallelizing Waveform-based Detection on GPU 58 4.2.2 Multi-Standards Parallel Waveform Detection on GPU 60 4.3 Parallelizing Cyclostationary Feature Detection on GPU 62 4.3.1 Parallelizing Channelization and Windowing 63 4.3.2 Parallelizing the First Fast Fourier Transform 65 4.3.3 Parallelizing FFT Shift and Down-Conversion 66 4.3.4 Parallelizing Column Multiplication 67 4.3.5 Parallelizing the Second FFT and SCD Magnitude Calculation 68 CHAPTER 5 EXPERIMENT RESULTS AND ANALYSIS 71 5.1 Test Platform and System Parameters 71 5.2 Results of the Parallelized Waveform-based Detection Method 73 5.3 Results of the Parallelized Cyclostationary Feature Detection Method 83 5.4 Comparison of Parallelized FAM on GPU with Other Works 92 CHAPTER 6 CONCLUSION 95 REFERENCES 97 | |
dc.language.iso | en | |
dc.title | 感知無線電頻譜偵測技術之繪圖處理器實現 | zh_TW |
dc.title | GPU Software Implementation of Spectrum Sensing Algorithms for Cognitive Radio | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 闕志達(Tzi-Dar Chiueh),游竹(Chu Yu),熊博安(Pao-Ann Hsiung) | |
dc.subject.keyword | 感知無線電,頻譜偵測,繪圖處理器,統一計算架構, | zh_TW |
dc.subject.keyword | Cognitive Radio,Spectrum Sensing,GPU,CUDA, | en |
dc.relation.page | 99 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2012-07-25 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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