认知无线电多信道频谱感知方法的研究
|
摘要
认知无线电技术是目前解决频谱资源稀缺问题的核心技术,频谱感知是保证该技术得以实施的前提。为了保证授权用户的正常传输,同时满足认知用户的速率要求,必须要求认知用户能进行多信道频谱感知,以便发现足够的“频谱空洞”进行频谱接入,并监测频谱活动状态的变化。本论文采用置信传播算法作为解决手段,研究了基于随机信道选择和欠采样技术的多信道频谱感知方法,以及异步OFDMA网络的频谱感知方法,对于认知无线电的频谱感知的研究做出了一定的贡献。
首先研究了基于随机信道选择的多信道频谱感知问题。阐述了本课题的系统模型,分析了检验统计量的渐近概率分布及本模型的等效二部图形式,并引入二部图的经典融合算法——置信传播算法。随后提出了三种频谱感知算法,其核心思想是认知用户随机选择部分信道进行感知,然后采用置信传播等推理算法判决出所有信道的活动状态。方法一中,仅当融合中心接收到足够多的感知信息后才开始置信推理。方法二中,当融合中心一接收到感知信息就开始推理判决,并结合“信道释放”机制,即不对那些已经判决出活动状态的信道进行重复感知,进一步降低感知延迟。方法三中,认知用户将方法一、二中的感知信息进行量化处理,并选择可靠性较高的感知结果发送给融合中心进行融合判决,降低宽带限制。
其次研究了基于欠采样技术的多信道频谱感知问题。阐述了本课题的系统模型及授权用户的时域信号模型,分析了采样速率不满足奈奎斯特采样定律时的频谱混叠方式,并分析了检验统计量的渐近分布及本模型的等效二部图表示。随后提出了一种基于欠采样技术的频谱感知算法,其基本思想是认知用户在每次感知过程中按一定规律选择采样率对整个频谱进行采样感知,并利用置信传播算法对感知信息进行推理判决。最后重点分析了感知算法中的采样率选择问题,证明了认知用户选择授权用户信道带宽的质数倍作为采样速率较好。
然后研究了异步OFDMA网络的频谱感知问题,并重点分析了存在分数倍载波频偏时的频谱感知问题。研究了存在分数倍载波频偏时的各子载波上的接收信号模型以及载波频偏对传统感知算法的影响,给出了最差情况下的虚警概率和检测概率的渐近表达式。随后简化了频域信号的接收模型,提出了一种基于置信传播的感知方法,能有效降低或抑制信道间干扰对频谱感知的影响,大大降低虚警的发生。最后对能量检测算法进行改进,提出了简化的感知方法,降低了计算复杂度,易于硬件实现。并基于ADSP-TS201,给出了简化的感知方法的DSP实现方法。
Nowadays, cognitive radio is a key technology to solve the problem of spectrum scarceness, and spectrum sensing is the precondition to achieve such technology. In order to guarantee the transmission of licensed users and satisfy the rate requirement of cognitive users simultaneously, multi-channel spectrum sensing is required. So cognitive users can discover enough spectrum holes for spectrum access and can sense the change of spectrum activities. In my paper, I have studied multi-channel spectrum sensing based on random selection of channels, multi-channel sensing based on down sampling, and spectrum sensing in asynchronous OFDMA network, which gives some significant contribution to the research on spectrum sensing.
Firstly, the problem of multi-channel spectrum sensing based on random selection of channels is studied. The system model of spectrum sensing is described, the asymptotic probability density fuction of the test statistic and the equivalent bipartite graph are analyzed, and belief propagation algorithm (BPA) is introduced, as a typical fusion algorithm in the bipartite graph. Three spectrum sensing methods are proposed then, whose basic idea is that cognitive users choose part of channels to sense randomly and infer the spectrum activities of all the channels by BPA. In method one, the fusion center starts data fusion only when enough sensing information is received. In method two, once sensing information is received, the fusion center starts data fusion and never detects the channels whose activities are already judged, which can decrease the sensing delay further. In method three, the sensing information in the above methods would be quantified, and only the reliable results would be chosen for data fusion, which can decrease the bandwidth constraint.
Next, the problem of multi-channel spectrum sensing based on down sampling is studied. The system model and the signal model of licensed users in time domain are characterized, the signal model of licensed users in frequency domain when the frequency sampling rate doesn't satisfy the theorem of Nyquist Sampling is investigated, and the asymptotic probability density function of the test statistic and the equivalent bipartite graph of our model are analyzed. Then a kind of spectrum sensing method is proposed, whose basic idea is that the cognitive user chooses the frequency sampling rate according to a certain rule to sense the whole wide-band spectrum in every sensing process and infers the spectrum activities of all the channels in terms of BPA. The problem of the selection of sampling rate is mainly analyzed. And it proves that it is better to choose the frequency sampling rate which is a multiple of the channel bandwidth of licisend users and the value of multiple is a prime.
Finally, the problem of spectrum sensing in asynchronous OFDMA network is studied, especially when fractional carrier frequency offset exists. The model of the received signal in each subcarrier and the effect to spectrum sensing induced by CFO are analyzed firstly. Then the signal is simplified, and a spectrum sensing method based BPA is presented, which can decrease the effect to spectrum sensing induced by CFO and false alarm. After that a simplified sensing method is proposed by advancing energy detection, which can reduce the complexity of computation and can be implemented on the hardware device easily. And the DSP implementation of the simplified method is introduced based on ADSP-TS201.
首先研究了基于随机信道选择的多信道频谱感知问题。阐述了本课题的系统模型,分析了检验统计量的渐近概率分布及本模型的等效二部图形式,并引入二部图的经典融合算法——置信传播算法。随后提出了三种频谱感知算法,其核心思想是认知用户随机选择部分信道进行感知,然后采用置信传播等推理算法判决出所有信道的活动状态。方法一中,仅当融合中心接收到足够多的感知信息后才开始置信推理。方法二中,当融合中心一接收到感知信息就开始推理判决,并结合“信道释放”机制,即不对那些已经判决出活动状态的信道进行重复感知,进一步降低感知延迟。方法三中,认知用户将方法一、二中的感知信息进行量化处理,并选择可靠性较高的感知结果发送给融合中心进行融合判决,降低宽带限制。
其次研究了基于欠采样技术的多信道频谱感知问题。阐述了本课题的系统模型及授权用户的时域信号模型,分析了采样速率不满足奈奎斯特采样定律时的频谱混叠方式,并分析了检验统计量的渐近分布及本模型的等效二部图表示。随后提出了一种基于欠采样技术的频谱感知算法,其基本思想是认知用户在每次感知过程中按一定规律选择采样率对整个频谱进行采样感知,并利用置信传播算法对感知信息进行推理判决。最后重点分析了感知算法中的采样率选择问题,证明了认知用户选择授权用户信道带宽的质数倍作为采样速率较好。
然后研究了异步OFDMA网络的频谱感知问题,并重点分析了存在分数倍载波频偏时的频谱感知问题。研究了存在分数倍载波频偏时的各子载波上的接收信号模型以及载波频偏对传统感知算法的影响,给出了最差情况下的虚警概率和检测概率的渐近表达式。随后简化了频域信号的接收模型,提出了一种基于置信传播的感知方法,能有效降低或抑制信道间干扰对频谱感知的影响,大大降低虚警的发生。最后对能量检测算法进行改进,提出了简化的感知方法,降低了计算复杂度,易于硬件实现。并基于ADSP-TS201,给出了简化的感知方法的DSP实现方法。
Nowadays, cognitive radio is a key technology to solve the problem of spectrum scarceness, and spectrum sensing is the precondition to achieve such technology. In order to guarantee the transmission of licensed users and satisfy the rate requirement of cognitive users simultaneously, multi-channel spectrum sensing is required. So cognitive users can discover enough spectrum holes for spectrum access and can sense the change of spectrum activities. In my paper, I have studied multi-channel spectrum sensing based on random selection of channels, multi-channel sensing based on down sampling, and spectrum sensing in asynchronous OFDMA network, which gives some significant contribution to the research on spectrum sensing.
Firstly, the problem of multi-channel spectrum sensing based on random selection of channels is studied. The system model of spectrum sensing is described, the asymptotic probability density fuction of the test statistic and the equivalent bipartite graph are analyzed, and belief propagation algorithm (BPA) is introduced, as a typical fusion algorithm in the bipartite graph. Three spectrum sensing methods are proposed then, whose basic idea is that cognitive users choose part of channels to sense randomly and infer the spectrum activities of all the channels by BPA. In method one, the fusion center starts data fusion only when enough sensing information is received. In method two, once sensing information is received, the fusion center starts data fusion and never detects the channels whose activities are already judged, which can decrease the sensing delay further. In method three, the sensing information in the above methods would be quantified, and only the reliable results would be chosen for data fusion, which can decrease the bandwidth constraint.
Next, the problem of multi-channel spectrum sensing based on down sampling is studied. The system model and the signal model of licensed users in time domain are characterized, the signal model of licensed users in frequency domain when the frequency sampling rate doesn't satisfy the theorem of Nyquist Sampling is investigated, and the asymptotic probability density function of the test statistic and the equivalent bipartite graph of our model are analyzed. Then a kind of spectrum sensing method is proposed, whose basic idea is that the cognitive user chooses the frequency sampling rate according to a certain rule to sense the whole wide-band spectrum in every sensing process and infers the spectrum activities of all the channels in terms of BPA. The problem of the selection of sampling rate is mainly analyzed. And it proves that it is better to choose the frequency sampling rate which is a multiple of the channel bandwidth of licisend users and the value of multiple is a prime.
Finally, the problem of spectrum sensing in asynchronous OFDMA network is studied, especially when fractional carrier frequency offset exists. The model of the received signal in each subcarrier and the effect to spectrum sensing induced by CFO are analyzed firstly. Then the signal is simplified, and a spectrum sensing method based BPA is presented, which can decrease the effect to spectrum sensing induced by CFO and false alarm. After that a simplified sensing method is proposed by advancing energy detection, which can reduce the complexity of computation and can be implemented on the hardware device easily. And the DSP implementation of the simplified method is introduced based on ADSP-TS201.
引文
[1] Federal Communications Commission, "Spectrum policy task force report", Technical Report (02-135), Nov. 2002.
[2] Joseph Mitola et al., "Cognitive radio: Making software radios more personal"[C], in IEEE Personal Communications, vol. 6, no.4, pp. 13-18, Aug. 1999.
[3] Joseph Mitola, "Cognitive radio: An integrated agent architecture for software defined radio", Doctor of Technology, Royal Institute Technology (KTH), Stockholm, Sweden, 2000.
[4] Simon Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications''[J], in IEEE Journal on Selected Area Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005.
[5] Ian F. Akyildiz et al., "NeXt generation /dynamic spectrum access/ cognitive radio wireless networks: A survey"[J], in Computer Networks Journal (Elsevier), vol. 50, pp. 2127-2159, Sept. 2006.
[6] Caiying Guo, Tiankui Guo et al., "Investigation on Key Techniques and Applications of Cognitive Radio", http://www.paper.edu.cn.
[7] T. A. Weiss and F. K. Jondral, "Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency"[J], in IEEE Radio Communications, vol. 42, pp. S8-14, March 2004.
[8] Danijela Cabric et al., "Spectrum Sharing Radios"[M], in IEEE Circuits and Systems Magazine, vol. 6, pp. 30-45, July 2006.
[9] Danijela Cabric et al., "Physical Layer Design Issues Unique to Cognitive Radio Systems"[C], in the IEEE International Symposium on Personal Indoor and Mobile Radio Communications, vol. 2, pp. 759-763, Sept. 2005.
[10]T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications'' [J], in IEEE Communications Surveys & Tutorials, vol. 11, issue 1, pp. 116 - 130, First Quarter 2009.
[11] D. Cabric, M. Mishra and W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios"[C], in Proceedings of 36th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 772-776, 2004.
[12]A. Sahai, N. Hoven and R. Tandra, "Some fundamental limits on cognitive radio"[C], in Proceedings of 42nd Allerton Conference on Communication, Control and Computing, 2004.
[13]R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty"[C], in IEEE Proceedings of International Conference on Wireless Networks, Communicationsand Mobile Computing, pp. 464-469, 2005.
[14]A. Sahai, N. Hoven et al., "Fundamental tradeoffs in robust spectrum sensing for opportunistic frequency reuse", Technical Report, 2006.
[15] Harry Urkowwitz, "Energy Detection of Unknown Deterministic Signals"[C], in Proceedings of the IEEE, vol. 55, no. 4, April 1967.
[16] F. F. Digham et al., "On the energy detection of unknown signals over fading channels"[C], in IEEE International Conference on Communications, vol. 5, pp. 3575-3579, May 2003.
[17]William A. Gardner, "Spectral correlation of modulated signals: Part II—digital modulation"[J], in IEEE Transactions on Communications, vol. 35, no. 6, pp. 595-601, June 1987.
[18]Ning Han, Sung Hwan Shon, Jae Hak Chung and Jae Moung Kim, "Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems"[C], in Proceedings of 8th International Conference on Advanced Communication Technology, vol. 2, pp. 1765-1770, 2006.
[19]A. Fehske, J. Gaeddert and J. H. Reed, "A New Approach to Signal Classification Using Spectral Correlation and Neural Networks"[C], in IEEE Dynamic Spectrum Access Networks, pp. 144-150, Nov. 2005.
[20] Kyouwoong Kim, Ihsan A. Akbar, et al., "Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp. 212-215, April 2007.
[21] Federal Communications Commission, "Establishment of an Interference Temperature Metric to Quantify and Manage Interference and to Expand Available Unlicensed Operation in Certain Fixed, Mobile and Satellite Frequency", Technical Report (02-135), Nov. 2002.
[22] S. M. Mishra, A. Sahai and Robert W. Brodersen, "Cooperative Sensing among Cognitive Radios"[C], in Proceedings of IEEE International Conference on Communications(ICC'06), vol. 4, June 2006.
[23] A. Ghasemi and E. S. Sousa, "Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp.131-136, Nov. 2005.
[24] E. Bisotsky, S. Kuffner and R. Peterson, "On Collaborative Detection of TV Transmissions in Support of Dynamic Spectrum Sharing"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp. 338-345, Nov. 2005.
[25]Qihang Peng, Kun Zeng, Jun Wang and Shaoqian Li, "A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context", in Proceedings of IEEE PIMRC2006, pp. 1-5, Sept. 2006.
[26]A. Taherpour, M. Nasiri-Kenari, A. Jamshidi, "Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks", [C] in Proceedings of IEEE PIMRC2007, pp. 1-6, Sept. 2007.
[27] G. Ganesan and Ye Li, "Agility Improvement Through Cooperative Diversity in Cognitive Radio"[C], in Proceedings of Global Telecommunications Conference, volume 5, pp. 2505-2509, 2005.
[28]G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio Networks"[C], in Proceedings of IEEE DYSPAN, volume 5, pp. 2505-2509, 2005.
[29] G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks" [J], in IEEE Transactions on Wireless Communications, vol. 6, pp. 2214-2222, June 2007.
[30]G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks" [J], in IEEE Trans. on Wireless Communications, vol.6, pp. 2204-2213, June 2007.
[31]J. Unnikrishnan and V. V. Veeravalli, "Cooperative Spectrum Sensing and Detection for Cognitive Radio"[C], in Global Telecommunications Conference(Globecom'07), pp. 2972-2976, Nov. 2007.
[32]Zhi Quan, Shuguang Cui and Ali H. Sayed, "Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks"[J], in IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, Feb. 2008
[33] P. Kaligineedi, M. Khabbazian and V. K. Bhargava, "Secure Cooperative Sensing Techniques for Cognitive Radio Systems"[C], in Proceedings of IEEE International Conference on Communications, pp. 3406-3410, May 2008.
[34]Ruiliang Chen, Jung-Min Park and Kaigui Bian, "Robust Distributed Spectrum Sensing in Cognitive Radio Networks", Technical report, July 2006.
[35]Hui Huang, Zhaoyang Zhang, Peng Cheng and Peiliang Qiu, "Cooperative Spectrum Sensing in Cognitive Radio System with Limited Sensing Ability"[J], accepted by Journal of Zhejiang University SCIENCE.
[36]Y. Hur, J. Park, W. Woo et al., "A Wideband Analog Multi-resolution Spectrum Snsing (MRSS) Technique for Cognitive Radio Systems"[C], in Proceedings of IEEE International Symposium on Circuits and Systems, pp. 4090-4093, May 2006.
[37] N. M. Neihart, S. Roy and D. J. Allstot, "A Parallel, Multi-Resolution Sensing Technique for Multiple Antenna Cognitive Radios"[C], in International Symposium on Circuits and Systems, May 2007.
[38]Qiwei Zhang, A. B. J. Kokkeler and G. J. M. Smit, "An Efficient Multi-resolution Spectrum Sensing Method for Cognitive Radio"[C], in Communications and Networking in China, pp. 1226-1229, Aug. 2008.
[39] Ling Luo and Sunit Roy, "A Two-stage Sensing Technique for Dynamic Spectrum Access", in Proceedings of IEEE International Conference on Communications(ICC08), pp. 4181-4185, May 2008.
[40]Jun Ma and Ye Li, "A Probability-based Spectrum Sensing Scheme for Cognitive Radio"[C], in Proceedings of IEEE International Conference on Communications, pp. 3416-3420, May 2008.
[41]Zhi Tian and G. B. Giannakis, "A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios"[C], in Proceedings of IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 1-5, June 2006.
[42]Zhi Quan, Shuguang Cui et al., " Wideband Spectrum Sensing in Cognitive Radio Networks" [C], in Proceedings of IEEE International Conference on Communications, pp. 901-906, May 2008.
[43]Zhi Quan, Shuguang Cui et al., "Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks"[J], in IEEE Transactions on Signal Processing, vol. 57, pp. 1128-1140, March 2009
[44]J. Jia, Q. Zhang and X. Shen, "HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management"[J], in IEEE Journal on Selected Areas in Communications, 2008, 26(1): 106-117.
[45]Qing Zhao, Lang Tong et al., "Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc Networks: A POMDP Framework"[J], in IEEE Journal on Selected Areas in Communications, vol. 25, no. 3, pp. 589-600, April 2007.
[46] Michael Luby, "LT Codes"[C], in Proceedings of IEEE Symposium on Foundations of Computer Science, pp. 271-280, Nov. 2002.
[47] B. V. Gendenko and A. N. Kolmogorov, "Limit Distributions for Sums of Independent Random Variables", Book, MA: Addison-Wesley, 1954.
[48] F. Kschischang, B. Frey and H. A. Loeliger, "Factor Graphs and the Sum-Product Algorithm" [J], in IEEE Transactions Information Theory, vol. 47, pp. 498-519, Feb. 2001.
[49] R. J. McEliece, D. J. C. MacKay and J.-F. Cheng, "Turbo decoding as an instance of Pearl's 'belief propagation' algorithm"[J], in IEEE Journal of Selected Areas in Communications, vol. 16, pp. 140-152, Feb. 1998.
[50] P. Rusmevichientong and B. Van Roy, "An analysis of belief propagation on the turbo decoding graph with Gaussian densities''[J], in IEEE Transactions Information Theory, vol. 47, pp. 745-765, Feb. 2001.
[51] Hans-Andrea Loeliger, "An Introduction to Factor Graphs"[M], in IEEE Signal Processing Magazine, vol. 21, pp. 28-41, Jan. 2004.
[52]T. Richardson and R. Urbanke, "The capacity of low-density parity check codes under message-passing decoding"[J], in IEEE Transactions Information Theory, vol. 47, pp. 599-618, Feb. 2001.
[53] H.A. Loeliger et al., "Signal Processing with factor grahs: examples"[C], in IEEE International Symposium on Control Communications and Signal Processing, pp. 571-574, 2004.
[54] D. Bickson et al., "Gaussian belief propagation based multiuser detection"[C], in IEEE International Symposium on Information Theory, pp. 1878-1882, July 2008.
[55] Y. Kabashima, "A CDMA Multiuser Detection Algorithm on the basis of Belief Propagation", J. Phys. A: Math. Gen., vol. 36, pp. 11111-11121, Oct. 2003.
[56]A. Tarczynski, D. Bland and T. Laakso, "Spectrum Estimation of Non-uniformly Sampled Signals"[C], in Proceedings of IEEE International Conference on Industrial Electronics, vol. 1, pp. 196-200, June 1996.
[57] L. Peretto, G. Pasini and C. Muscas, "Signal Spectrum Analysis and Period Estimation by Using Delayed Signal Sampling"[J], in IEEE Transactions on Instrumentation and Measurement, vol. 50, pp. 920-925, Aug. 2001.
[58] Liu Gao-hui, Gao Yong and Yu Ning-mei, "Research on Non-Uniform Banpass Sampling", in the Journal of China Universities of Posts and Telecommunications, Vol. 28, No. 4, pp. 66-73, Aug. 2005.
[59]Qi Yuan, T. P. Minka and R. W. Picard, "Bayesian spectrum estimation of unevenly sampled nonstationary data"[C], in Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2, pp. 1473-1476, 2002.
[60]Yan Du, "OFDM 的基本原理 ", Technical report, Shandong University.
[61]Yun Chiu, Dejan Markovic, Haiyun Tang and Ning Zhang, "OFDM Receiver Design", EE225C technical report, fall 2000.
[62]Xiaoyong Ding, "OFDM 系统载波小数频偏与子载波间干扰分析" [J], in Science and Technology Information, vol. 33, pp. 215-216, 2006.
[63] R. Lakshmish, "Hard Decision Parallel Interference Cancellation for Uplink OFDMA"[C], in Proceedings of IEEE International Conference on Communications, Circuits and Systems, vol. 2, pp. 753-756, June 2006.
[64] Bo Liu, Da Guo, Feng Jiang and Junde Song, "Analysis of ICI Power of OFDM Systems in Time Selective Fast Fading Channel"[J], in Journal of Circuits and Systems, vol. 14, no. 4, Aug. 2009.
[65] Peng Cheng, "OFDM 课程", Lession report.
[66] ADI Corporation, "ADSP-TS201 TigerSHARC~(?) Processor Hardware Reference", Dec. 2004.
[67]ADI Corporation, "ADSP-TS201 TigerSHARC(?) Processor Programming Reference", Aug. 2004.
[2] Joseph Mitola et al., "Cognitive radio: Making software radios more personal"[C], in IEEE Personal Communications, vol. 6, no.4, pp. 13-18, Aug. 1999.
[3] Joseph Mitola, "Cognitive radio: An integrated agent architecture for software defined radio", Doctor of Technology, Royal Institute Technology (KTH), Stockholm, Sweden, 2000.
[4] Simon Haykin, "Cognitive Radio: Brain-Empowered Wireless Communications''[J], in IEEE Journal on Selected Area Communications, vol. 23, no. 2, pp. 201-220, Feb. 2005.
[5] Ian F. Akyildiz et al., "NeXt generation /dynamic spectrum access/ cognitive radio wireless networks: A survey"[J], in Computer Networks Journal (Elsevier), vol. 50, pp. 2127-2159, Sept. 2006.
[6] Caiying Guo, Tiankui Guo et al., "Investigation on Key Techniques and Applications of Cognitive Radio", http://www.paper.edu.cn.
[7] T. A. Weiss and F. K. Jondral, "Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency"[J], in IEEE Radio Communications, vol. 42, pp. S8-14, March 2004.
[8] Danijela Cabric et al., "Spectrum Sharing Radios"[M], in IEEE Circuits and Systems Magazine, vol. 6, pp. 30-45, July 2006.
[9] Danijela Cabric et al., "Physical Layer Design Issues Unique to Cognitive Radio Systems"[C], in the IEEE International Symposium on Personal Indoor and Mobile Radio Communications, vol. 2, pp. 759-763, Sept. 2005.
[10]T. Yucek and H. Arslan, "A survey of spectrum sensing algorithms for cognitive radio applications'' [J], in IEEE Communications Surveys & Tutorials, vol. 11, issue 1, pp. 116 - 130, First Quarter 2009.
[11] D. Cabric, M. Mishra and W. Brodersen, "Implementation issues in spectrum sensing for cognitive radios"[C], in Proceedings of 36th Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 772-776, 2004.
[12]A. Sahai, N. Hoven and R. Tandra, "Some fundamental limits on cognitive radio"[C], in Proceedings of 42nd Allerton Conference on Communication, Control and Computing, 2004.
[13]R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty"[C], in IEEE Proceedings of International Conference on Wireless Networks, Communicationsand Mobile Computing, pp. 464-469, 2005.
[14]A. Sahai, N. Hoven et al., "Fundamental tradeoffs in robust spectrum sensing for opportunistic frequency reuse", Technical Report, 2006.
[15] Harry Urkowwitz, "Energy Detection of Unknown Deterministic Signals"[C], in Proceedings of the IEEE, vol. 55, no. 4, April 1967.
[16] F. F. Digham et al., "On the energy detection of unknown signals over fading channels"[C], in IEEE International Conference on Communications, vol. 5, pp. 3575-3579, May 2003.
[17]William A. Gardner, "Spectral correlation of modulated signals: Part II—digital modulation"[J], in IEEE Transactions on Communications, vol. 35, no. 6, pp. 595-601, June 1987.
[18]Ning Han, Sung Hwan Shon, Jae Hak Chung and Jae Moung Kim, "Spectral correlation based signal detection method for spectrum sensing in IEEE 802.22 WRAN systems"[C], in Proceedings of 8th International Conference on Advanced Communication Technology, vol. 2, pp. 1765-1770, 2006.
[19]A. Fehske, J. Gaeddert and J. H. Reed, "A New Approach to Signal Classification Using Spectral Correlation and Neural Networks"[C], in IEEE Dynamic Spectrum Access Networks, pp. 144-150, Nov. 2005.
[20] Kyouwoong Kim, Ihsan A. Akbar, et al., "Cyclostationary Approaches to Signal Detection and Classification in Cognitive Radio"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp. 212-215, April 2007.
[21] Federal Communications Commission, "Establishment of an Interference Temperature Metric to Quantify and Manage Interference and to Expand Available Unlicensed Operation in Certain Fixed, Mobile and Satellite Frequency", Technical Report (02-135), Nov. 2002.
[22] S. M. Mishra, A. Sahai and Robert W. Brodersen, "Cooperative Sensing among Cognitive Radios"[C], in Proceedings of IEEE International Conference on Communications(ICC'06), vol. 4, June 2006.
[23] A. Ghasemi and E. S. Sousa, "Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp.131-136, Nov. 2005.
[24] E. Bisotsky, S. Kuffner and R. Peterson, "On Collaborative Detection of TV Transmissions in Support of Dynamic Spectrum Sharing"[C], in Proceedings of IEEE Dynamic Spectrum Access Networks, pp. 338-345, Nov. 2005.
[25]Qihang Peng, Kun Zeng, Jun Wang and Shaoqian Li, "A Distributed Spectrum Sensing Scheme Based on Credibility and Evidence Theory in Cognitive Radio Context", in Proceedings of IEEE PIMRC2006, pp. 1-5, Sept. 2006.
[26]A. Taherpour, M. Nasiri-Kenari, A. Jamshidi, "Efficient Cooperative Spectrum Sensing in Cognitive Radio Networks", [C] in Proceedings of IEEE PIMRC2007, pp. 1-6, Sept. 2007.
[27] G. Ganesan and Ye Li, "Agility Improvement Through Cooperative Diversity in Cognitive Radio"[C], in Proceedings of Global Telecommunications Conference, volume 5, pp. 2505-2509, 2005.
[28]G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio Networks"[C], in Proceedings of IEEE DYSPAN, volume 5, pp. 2505-2509, 2005.
[29] G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio, Part I: Two User Networks" [J], in IEEE Transactions on Wireless Communications, vol. 6, pp. 2214-2222, June 2007.
[30]G. Ganesan and Ye Li, "Cooperative Spectrum Sensing in Cognitive Radio, Part II: Multiuser Networks" [J], in IEEE Trans. on Wireless Communications, vol.6, pp. 2204-2213, June 2007.
[31]J. Unnikrishnan and V. V. Veeravalli, "Cooperative Spectrum Sensing and Detection for Cognitive Radio"[C], in Global Telecommunications Conference(Globecom'07), pp. 2972-2976, Nov. 2007.
[32]Zhi Quan, Shuguang Cui and Ali H. Sayed, "Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks"[J], in IEEE Journal of Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28-40, Feb. 2008
[33] P. Kaligineedi, M. Khabbazian and V. K. Bhargava, "Secure Cooperative Sensing Techniques for Cognitive Radio Systems"[C], in Proceedings of IEEE International Conference on Communications, pp. 3406-3410, May 2008.
[34]Ruiliang Chen, Jung-Min Park and Kaigui Bian, "Robust Distributed Spectrum Sensing in Cognitive Radio Networks", Technical report, July 2006.
[35]Hui Huang, Zhaoyang Zhang, Peng Cheng and Peiliang Qiu, "Cooperative Spectrum Sensing in Cognitive Radio System with Limited Sensing Ability"[J], accepted by Journal of Zhejiang University SCIENCE.
[36]Y. Hur, J. Park, W. Woo et al., "A Wideband Analog Multi-resolution Spectrum Snsing (MRSS) Technique for Cognitive Radio Systems"[C], in Proceedings of IEEE International Symposium on Circuits and Systems, pp. 4090-4093, May 2006.
[37] N. M. Neihart, S. Roy and D. J. Allstot, "A Parallel, Multi-Resolution Sensing Technique for Multiple Antenna Cognitive Radios"[C], in International Symposium on Circuits and Systems, May 2007.
[38]Qiwei Zhang, A. B. J. Kokkeler and G. J. M. Smit, "An Efficient Multi-resolution Spectrum Sensing Method for Cognitive Radio"[C], in Communications and Networking in China, pp. 1226-1229, Aug. 2008.
[39] Ling Luo and Sunit Roy, "A Two-stage Sensing Technique for Dynamic Spectrum Access", in Proceedings of IEEE International Conference on Communications(ICC08), pp. 4181-4185, May 2008.
[40]Jun Ma and Ye Li, "A Probability-based Spectrum Sensing Scheme for Cognitive Radio"[C], in Proceedings of IEEE International Conference on Communications, pp. 3416-3420, May 2008.
[41]Zhi Tian and G. B. Giannakis, "A Wavelet Approach to Wideband Spectrum Sensing for Cognitive Radios"[C], in Proceedings of IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communications, pp. 1-5, June 2006.
[42]Zhi Quan, Shuguang Cui et al., " Wideband Spectrum Sensing in Cognitive Radio Networks" [C], in Proceedings of IEEE International Conference on Communications, pp. 901-906, May 2008.
[43]Zhi Quan, Shuguang Cui et al., "Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks"[J], in IEEE Transactions on Signal Processing, vol. 57, pp. 1128-1140, March 2009
[44]J. Jia, Q. Zhang and X. Shen, "HC-MAC: A Hardware-Constrained Cognitive MAC for Efficient Spectrum Management"[J], in IEEE Journal on Selected Areas in Communications, 2008, 26(1): 106-117.
[45]Qing Zhao, Lang Tong et al., "Decentralized Cognitive MAC for Opportunistic Spectrum Access in Ad Hoc Networks: A POMDP Framework"[J], in IEEE Journal on Selected Areas in Communications, vol. 25, no. 3, pp. 589-600, April 2007.
[46] Michael Luby, "LT Codes"[C], in Proceedings of IEEE Symposium on Foundations of Computer Science, pp. 271-280, Nov. 2002.
[47] B. V. Gendenko and A. N. Kolmogorov, "Limit Distributions for Sums of Independent Random Variables", Book, MA: Addison-Wesley, 1954.
[48] F. Kschischang, B. Frey and H. A. Loeliger, "Factor Graphs and the Sum-Product Algorithm" [J], in IEEE Transactions Information Theory, vol. 47, pp. 498-519, Feb. 2001.
[49] R. J. McEliece, D. J. C. MacKay and J.-F. Cheng, "Turbo decoding as an instance of Pearl's 'belief propagation' algorithm"[J], in IEEE Journal of Selected Areas in Communications, vol. 16, pp. 140-152, Feb. 1998.
[50] P. Rusmevichientong and B. Van Roy, "An analysis of belief propagation on the turbo decoding graph with Gaussian densities''[J], in IEEE Transactions Information Theory, vol. 47, pp. 745-765, Feb. 2001.
[51] Hans-Andrea Loeliger, "An Introduction to Factor Graphs"[M], in IEEE Signal Processing Magazine, vol. 21, pp. 28-41, Jan. 2004.
[52]T. Richardson and R. Urbanke, "The capacity of low-density parity check codes under message-passing decoding"[J], in IEEE Transactions Information Theory, vol. 47, pp. 599-618, Feb. 2001.
[53] H.A. Loeliger et al., "Signal Processing with factor grahs: examples"[C], in IEEE International Symposium on Control Communications and Signal Processing, pp. 571-574, 2004.
[54] D. Bickson et al., "Gaussian belief propagation based multiuser detection"[C], in IEEE International Symposium on Information Theory, pp. 1878-1882, July 2008.
[55] Y. Kabashima, "A CDMA Multiuser Detection Algorithm on the basis of Belief Propagation", J. Phys. A: Math. Gen., vol. 36, pp. 11111-11121, Oct. 2003.
[56]A. Tarczynski, D. Bland and T. Laakso, "Spectrum Estimation of Non-uniformly Sampled Signals"[C], in Proceedings of IEEE International Conference on Industrial Electronics, vol. 1, pp. 196-200, June 1996.
[57] L. Peretto, G. Pasini and C. Muscas, "Signal Spectrum Analysis and Period Estimation by Using Delayed Signal Sampling"[J], in IEEE Transactions on Instrumentation and Measurement, vol. 50, pp. 920-925, Aug. 2001.
[58] Liu Gao-hui, Gao Yong and Yu Ning-mei, "Research on Non-Uniform Banpass Sampling", in the Journal of China Universities of Posts and Telecommunications, Vol. 28, No. 4, pp. 66-73, Aug. 2005.
[59]Qi Yuan, T. P. Minka and R. W. Picard, "Bayesian spectrum estimation of unevenly sampled nonstationary data"[C], in Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, vol. 2, pp. 1473-1476, 2002.
[60]Yan Du, "OFDM 的基本原理 ", Technical report, Shandong University.
[61]Yun Chiu, Dejan Markovic, Haiyun Tang and Ning Zhang, "OFDM Receiver Design", EE225C technical report, fall 2000.
[62]Xiaoyong Ding, "OFDM 系统载波小数频偏与子载波间干扰分析" [J], in Science and Technology Information, vol. 33, pp. 215-216, 2006.
[63] R. Lakshmish, "Hard Decision Parallel Interference Cancellation for Uplink OFDMA"[C], in Proceedings of IEEE International Conference on Communications, Circuits and Systems, vol. 2, pp. 753-756, June 2006.
[64] Bo Liu, Da Guo, Feng Jiang and Junde Song, "Analysis of ICI Power of OFDM Systems in Time Selective Fast Fading Channel"[J], in Journal of Circuits and Systems, vol. 14, no. 4, Aug. 2009.
[65] Peng Cheng, "OFDM 课程", Lession report.
[66] ADI Corporation, "ADSP-TS201 TigerSHARC~(?) Processor Hardware Reference", Dec. 2004.
[67]ADI Corporation, "ADSP-TS201 TigerSHARC(?) Processor Programming Reference", Aug. 2004.