基于压缩感知的认知无线电频谱检测技术及其研究
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摘要
众所周知,如今无线电频谱资源在全球范围内都很稀缺。为了提高频谱的利用率,1999年学者J.Mitola提出了认知无线电这个概念。在认知无线电中,认知用户可以使用频谱检测技术在授权信道寻找空闲信道进行通信。可见,频谱检测技术是整个认知无线电系统中保证成功的关键技术之一。此外,当我们对宽带信道进行频谱检测时,使用传统的奈奎斯特采样定律会产生海量的采样数据,而现在的硬件水平很难满足这一信号的快速处理需求。幸运的是,近年出现的压缩感知理论给这一困境带来了转机:一方面,压缩感知理论允许稀疏信号以低于奈奎斯特采样定理的速率进行采样,大大减轻了硬件的处理压力;另一方面,无线信号在频域上天然的稀疏特性又符合压缩感知理论的前提之一——原始信号的稀疏性。因此基于压缩感知的认知无线电频谱检测技术是一个可行且很有价值的研究方向。
本文在第一章中介绍了本课题的研究背景,介绍了认知无线电的相关理论,特别是对频谱检测技术做了详细的阐述。在第二章中本文详细的论述了压缩感知理论和其当前的研究成果。在第三章中本文研究了压缩感知重构算法之一的最小l_1范数法,结合迭代加权和约束条件l_1范数化,提出了自己的最小l_1范数法的改进算法,并引入频谱检测,用仿真验证其在频谱检测中的效果。在第四、五章中本文研究了贝叶斯压缩感知的理论和引入优化高斯随机观测矩阵贝叶斯压缩感知理论,并将两种算法分别引入频谱检测技术进行仿真研究;最后以能量检测法和使用能量判决的BCS频谱检测方法为代表分析了传统频谱检测法与压缩重构频谱检测法的优劣。
As we all know, the wireless spectrum resource is very scarce globally. In order to improve the utilization of the wireless spectrum, the conception of Cognitive Radio (CR) was proposed by J. Mitola in 1999. In CR theory, a cognitive user can first search an unoccupied authorized channel, and then use it for transmitting signals. So obviously, the spectrum detection is one of the key technologies in CR. And as we also know, when it comes to detection for the wideband spectrum, the samplings should be two times of the bandwidth according to the Nyquist’s Theorem, which made the hardware hard to burden. Luckily, the appealing of the Compressed Sensing (CS) theory changes the situation. On one hand, the CS theory permits the hardware to deal with the signal far below the Nyquist’s Sampling Rate, which can release the pressure on the hardware’s processing ability. On the other hand, the radio signals are sparse in the frequency domain, which satisfied one of the preconditions to apply the CS theory on. Thus, the research on the CS-based spectrum sensing technology is available and quite meaningful.
The first chapter of the paper introduces the background of our research and the fundamental knowledge of CR. The second chapter of the paper introduces the CS theory detailedly. The third chapter of the paper focuses on the l_1 norm minimum algorithm and its improved algorithms. We propose a new improved algorithm, which combines the iterative weighted l_1 norm algorithm with the constraints of l_1 norm. The simulations show that the improved algorithm works well in the spectrum detection. In the forth and fifth chapter of the paper, our research is focused on the Bayesian CS (BCS) algorithm and an optimized BCS (OBCS) algorithm. Then we simulate them in the spectrum detection scene. At last we take energy detection and BCS spectrum detection which judged by energy as the examples to compare the traditional spectrum detection and the compressed sensing spectrum detection.
本文在第一章中介绍了本课题的研究背景,介绍了认知无线电的相关理论,特别是对频谱检测技术做了详细的阐述。在第二章中本文详细的论述了压缩感知理论和其当前的研究成果。在第三章中本文研究了压缩感知重构算法之一的最小l_1范数法,结合迭代加权和约束条件l_1范数化,提出了自己的最小l_1范数法的改进算法,并引入频谱检测,用仿真验证其在频谱检测中的效果。在第四、五章中本文研究了贝叶斯压缩感知的理论和引入优化高斯随机观测矩阵贝叶斯压缩感知理论,并将两种算法分别引入频谱检测技术进行仿真研究;最后以能量检测法和使用能量判决的BCS频谱检测方法为代表分析了传统频谱检测法与压缩重构频谱检测法的优劣。
As we all know, the wireless spectrum resource is very scarce globally. In order to improve the utilization of the wireless spectrum, the conception of Cognitive Radio (CR) was proposed by J. Mitola in 1999. In CR theory, a cognitive user can first search an unoccupied authorized channel, and then use it for transmitting signals. So obviously, the spectrum detection is one of the key technologies in CR. And as we also know, when it comes to detection for the wideband spectrum, the samplings should be two times of the bandwidth according to the Nyquist’s Theorem, which made the hardware hard to burden. Luckily, the appealing of the Compressed Sensing (CS) theory changes the situation. On one hand, the CS theory permits the hardware to deal with the signal far below the Nyquist’s Sampling Rate, which can release the pressure on the hardware’s processing ability. On the other hand, the radio signals are sparse in the frequency domain, which satisfied one of the preconditions to apply the CS theory on. Thus, the research on the CS-based spectrum sensing technology is available and quite meaningful.
The first chapter of the paper introduces the background of our research and the fundamental knowledge of CR. The second chapter of the paper introduces the CS theory detailedly. The third chapter of the paper focuses on the l_1 norm minimum algorithm and its improved algorithms. We propose a new improved algorithm, which combines the iterative weighted l_1 norm algorithm with the constraints of l_1 norm. The simulations show that the improved algorithm works well in the spectrum detection. In the forth and fifth chapter of the paper, our research is focused on the Bayesian CS (BCS) algorithm and an optimized BCS (OBCS) algorithm. Then we simulate them in the spectrum detection scene. At last we take energy detection and BCS spectrum detection which judged by energy as the examples to compare the traditional spectrum detection and the compressed sensing spectrum detection.
引文
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[21]闫琦,杨家玮等,认知无线电中基于截断序贯检测的频谱感知技术[J],电子与信息学报,2011,33(7):1532-1536。Yan Qi, Yang Jia-wei, etc,“Truncated Sequential Detection for Spectrum Sensing in Cognitive Radio”, Journal of Electronics & Information Technology, 2011, 33(7): 1532-1536.
[22] Wald A.“Sequential Analysis”, New York: Dover Publications, 2004, pp. 1-224.
[23]赵春晖,马爽等,基于分形盒维数的频谱感知技术研究[J],电子与信息学报,2011,33(2):475-478。Zhao Chun-hui, Ma shuang, etc,“Spectrum Sensing in Cognitive Radios Based on Fractal Box Dimension”, Journal of Electronics & Information Technology, 2011, 33(2): 475-478.
[24]王颖喜,卢光跃,基于最大最小特征值之差的频谱感知技术研究[J],电子与信息学报,2010,32(11):2571-2575。Wang Ying-xi, Lu Guang-yue,“DMM Based Spectrum Sensing Method for Cognitive Radio Systems”, Journal of Electronics & Information Technology, 2010, 32(11): 2571-2575.
[25] Park J., Hur Y., etc,“Implementation Essues of a Wideband Multi-Resolution Spectrum Sensing(MRSS) Technique for Cognitive Radio(CR) Systems”, Proceedings of the 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications(CROWNCOM’06), Jun. 8-10, 2006, Mykonos Island, Greece. Piscataway, NJ, USA, IEEE, 2006:5p.
[26]钟国辉等,基于循环平稳特性的双循环频率频谱感知技术[J],华中科技大学学报,2010,38(7):78-81。Zhong Guohui, et al,“Cyclostationary Spectrum based Dual Cycle Frequencies Sensing Technique”, J. Huazhong Univ. of Sci. & Tech., 2010, 38(7): 78-81.
[27] E. Candes,“Compressive Sampling”, Proceedings of the International Congress of Mathematicians. Madrid, Spain, 2006, 3: 1433-1452.
[28] E. Candes, J. Romberg, et al,“Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency information”, IEEE Trans. on Information Theory, 2006, 52(2): 489-509.
[29] E. Candes, J. Romberg,“Quantitative Robust Uncertainty Principles and Optimally Sparse Decompositions”, Foundations of Comput. Math, 2006, 6(2): 227-254.
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