感知無線電頻譜偵測技術之繪圖處理器實現

GPU Software Implementation of Spectrum Sensing Algorithms for Cognitive Radio

李居翰(Chu-Han Lee)

指導教授 ： 陳少傑

國立臺灣大學/電機資訊學院/電子工程學研究所/碩士(2012年)

https://doi.org/10.6342/NTU.2012.10836

摘要

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。

關鍵字

感知無線電 ； 頻譜偵測 ； 繪圖處理器 ； 統一計算架構

並列摘要

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.

並列關鍵字

Cognitive Radio ； Spectrum Sensing ； GPU ； CUDA

參考文獻

[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.

[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.