基于非圆信号的局部最大功效不变检验频谱感知方法

贾琼; 李兵兵

doi:10.11999/JEIT150974

基于非圆信号的局部最大功效不变检验频谱感知方法

doi: 10.11999/JEIT150974

贾琼¹,
李兵兵¹

基金项目:

国家自然科学基金(61271299)，高等学校学科创新引智计划项目(B08038)

计量
- 文章访问数: 1212
- HTML全文浏览量: 121
- PDF下载量: 1079
- 被引次数: 0
出版历程
- 收稿日期: 2015-09-06
- 修回日期: 2016-03-03
- 刊出日期: 2016-06-19

A Novel Local Most Powerful Invariant Test Spectrum Sensing Method for Non-circular Signals

JIA Qiong¹,
LI Bingbing¹

Funds:

The National Natural Science Foundation of China (61271299), 111 Project (B08038)

摘要

摘要: 频谱感知是认知无线电网络中关键的一个环节，为了保证主用户不受干扰，要求感知算法必须具有较高的检测效率和检测精度。该文主要研究MIMO场景下的频谱感知问题，利用非圆信号的特性，提出一种基于局部最大功效不变检验(LMPIT)的频谱感知方法。根据渐近分布理论，推导了所述方法的理论检测门限。最后，采用蒙特卡洛仿真方法，分别分析了不同信道环境下该方法的检测性能，并与相关的感知算法进行对比。结果表明：在相同的环境下，文中提出的方法相比其他方法检测性能更高，且所需的采样点数更小，能够实现快速且精确的检测。
- 认知无线电 /
- 频谱感知 /
- 非圆信号 /
- 局部最大功效不变检验
Abstract: Spectrum sensing is a key technology in the cognitive radio network, in order to protect the primary user, the sensing algorithms must have a high detection efficiency and detection accuracy. This paper mainly focuses on the spectrum sensing in MIMO environment. Considering that the non-circular signal is usually used in the communication system, a novel spectrum sensing method is proposed for non-circular signals based on the Locally Most Powerful Invariant Test (LMPIT). The theoretical threshold is derived according to the asymptotic distribution theorem. Finally, the detection performance comparisons with other methods in various channels are simulated respectively. The results show that the proposed method outperforms other algorithms and only need small sample numbers, thus having higher sensing accuracy and efficiency.
- Cognitive radio /
- Spectrum sensing /
- Non-circular signal /
- Locally Most Powerful Invariant Test (LMPIT)

HTML全文

参考文献(20)

AXELL E, LEUS G, LARSSON E G, et al. Spectrum sensing for cognitive radio: State-of-the-art and recent advances[J]. IEEE Signal Processing Magazine, 2012, 29(3): 101-116.

YUCEK T and ARSLAN H. A survey of spectrum sensing algorithms for cognitive radio applications[J]. IEEE Communications Surveys Tutorials, 2009, 11(1): 116-130.

ZHU Y, LIU J, FENG Z, et al. Sensing performance of efficient cyclostationary detector with multiple antennas in multipath fading and lognormal shadowing environments[J]. Journal of Communications and Networks, 2014, 16(2): 162-171.

SUTTON P D,ZGL B, and DOYLE L E. Cyclostationary signatures for LTE Advanced and beyond[J]. Physical Communication, 2014, 10: 179-189.

LIU Y, ZHONG Z, WANG G, et al. Cyclostationary detection based spectrum sensing for cognitive radio networks[J]. Journal of Communications, 2015, 10(1): 74-79.

ATAPATTU S, TELLAMBURA C, and JIANG H. Energy Detection for Spectrum Sensing in Cognitive Radio[M]. Berlin: Springer, 2014: 11-26.

LI B, SUN M, LI X, et al. Energy detection based spectrum sensing for cognitive radios over time-frequency doubly selective fading channels[J]. IEEE Transactions on Signal Processing, 2015, 63(2): 402-417.

GOKCEOGLU A, DIKMESE S, VALKAMA M, et al. Energy detection under iq imbalance with single-and multi- channel direct-conversion receiver: analysis and mitigation[J]. IEEE Journal on Selected Areas in Communications, 2014, 32(3): 411-424.

ZENG Y and LIANG Y C. Maximum-minimum eigenvalue detection for cognitive radio[C]. Proceedings of IEEE 18th International Symposium on Personal Indoor, Mobile Radio Communication, Athens, 2007: 1-5.

LIM T J, ZHANG R, LIANG Y C, et al. GLRT- based spectrum sensing for cognitive radio[C]. Proceedings of IEEE Global Telecommunications Conference (GLOBECOM), New Orleans, LO, 2008: 1-5.

RAMREZ D, VAZQUEZ-VILAR G, LPEZ-VALCARCE R, et al. Detection of rank-signals in cognitive radio networks with uncalibrated multiple antennas[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3764-3774.

HUANG L, SO H C, and QIAN C. Volume-based method for spectrum sensing[J]. Digital Signal Processing, 2014, 28: 48-56.

HUANG L, XIAO Y H, and ZHANG Q T. Robust spectrum sensing for noncircular signal in multiantenna cognitive receivers[J]. IEEE Transactions on Signal Processing, 2015, 63(2): 498-511.

SCHREIER P J and SCHARF L L. Statistical Signal Processing of Complex-valued Data: the Theory of Improper and Noncircular Signals[M]. New York: Cambridge University Press, 2010: 65-66.

RAMIREZ D, VIA J, SANTAMARIA I, et al. Locally most powerful invariant tests for correlation and sphericity of

gaussian vectors[J]. IEEE Transactions on Information Theory, 2013, 59(4): 2128-2141.

BOX G E P, HUNTER J S, and HUNTER W G. Statistics for Experimenters: Design, Innovation, and Discovery[M]. New York, John Willey, 2005: 46-48.

贾琼, 李兵兵. 基于局部方差的MIMO频谱感知算法研究[J].电子与信息学报, 2015, 37(7): 1525-1530. doi: 10.11999/ JEIT141540.

JIA Qiong and LI Bingbing. MIMO Spectrum sensing method based on the local variance[J]. Journal of Electronics Information Technology, 2015, 37(7): 1525-1530. doi: 10.11999/JEIT141540.

CHIANI M, WIN M Z, and ZANELLA A. On the capacity of spatially correlated MIMO Rayleigh-fading channels[J]. IEEE Transactions on Information Theory, 2003, 49(10): 2363-2371.

施引文献

资源附件(0)

访问统计