基于Gerschgorin圆盘理论的认知无线电宽带频谱感知

doi:10.3969/j.issn.1000-436x.2014.04.001

摘要/Abstract

摘要：

提出了基于Gerschgorin圆盘理论的宽带频谱感知算法：Gerschgorin似然估计算法和Gerschgorin圆盘半径迭代算法。通过在宽带频谱感知中引入 Gerschgorin 圆盘理论，将认知无线电用户频谱观测数据中噪声圆盘空间和信号圆盘空间进行分离，并基于对主用户所占用子频段集合势的估计，实现对宽带授权频谱中多个子频段状态的监测。为了进一步提高感知性能，还提出利用宽带频谱中主用户信号占用子频段的连续性特性改善算法性能。理论推导和仿真结果表明，在信噪比较小时，Gerschgorin似然估计算法较基于信息论准则的宽带感知算法具有更稳定的检测性能；Gerschgorin圆盘半径迭代算法与传统能量检测方法相比，优势在于不依赖任何噪声功率先验信息，且在采样次数较少情况下的感知错误率较小。因此，基于Gerschgorin圆盘理论的频谱感知更适合于实际CR系统，可为宽带频谱感知提供行之有效的算法实施方案。

关键词: 认知无线电, 宽带频谱感知, ；Gerschgorin圆盘理论, Gerschgorin酉变换

Abstract:

Gerschgorin disk theorem (GDT) based wideband spectrum sensing methods, namely Gerschgorin likelihood estimate(GLE) algorithm and Gerschgorin disk radii iteration(GDRI) algorithm, were proposed for cognitive radios. By means of exploring the possibility of utilizing GDT in wideband spectrum sensing, the occupied and the idle licensed subbands was distingwished by respectively identifying the cardinalities of the corresponding subband sets. With an aim to strengthen the performance of GLE and GDRI, a subband continuity based wideband sensing algorithm was further proposed. Simulation results show that GLE sensing performance remains consistent when the SNR is low, whereas GDRI requires no a priori knowledge of the noise power and the PU signal and it overcomes the practical problem of small spectrum observation samples. With salient performance and practical feasibility, the proposed GLE and GDRI may serve as candidate wideband sensing technologies for cognitive radios.

Key words: cognitive radio, wideband spectrum sensing, Gerschgorin disk theorem, Gerschgorin unitary transform

申滨,王舒,黄琼,陈前斌. 基于Gerschgorin圆盘理论的认知无线电宽带频谱感知[J]. 通信学报, 2014, 35(4): 1-10.

Bin SHEN,Shu WANG,Qiong HUANG,Qian-bin CHEN. Gerschgorin disk theorem based spectrum sensing for wideband cognitive radio[J]. Journal on Communications, 2014, 35(4): 1-10.

图/表 9

图1

图2

图3

表1

图4

图5

图6

图7

图8

参考文献 16

[1]	HAYKIN S . Cognitive radio: brain-empowered wireless communica-tions[J]. IEEE Journal on Selected Areas in Communications, 2005,23(2):201-220.
[2]	SAHAI A , CABRIC D . Spectrum sensing: fundamental limits and practical challenges[A]. Proc IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN)[C]. Bal-timore, 2005.72-82.
[3]	SONNENSCHEIN A , FISHMAN P M . Radiometric detection of spread-spectrum signals in noise of uncertain power[J]. IEEE Transac-tions on Aerospace and Electronic Systems, 1992,28(3):654-660.
[4]	CABRIC D , TKACHENKO A , BRODERSEN R W . Experimental study of spectrum sensing based on energy detection and network cooperation[J]. ACM Int Workshop on Technology and Policy for Ac-cessing Spectrum[C]. 2006.
[5]	CABRIC D , MISHRA S M , BRODERSEN R W . Implementation issues in spectrum sensing for cognitive radios[J]. IEEE Asilomar Conf Signals, Systems and Computers, 2004,1:772-776.
[6]	BOROUJENY B F , KEMPTER R . Multicarrier communication tech-niques for spectrum sensing and communication in cognitive radios[J]. IEEE Communications Magazine, 2008,46(4):80-85.
[7]	HUR Y , PARK J , WOO W . A wideband analog multi-resolution spec-trum sensing technique for cognitive radio systems[A]. Proc IEEE Int Symposium on Circuits and Systems (ISCAS)[C]. 2006.
[8]	TIAN Z , GIANNAKIS G B . A wavelet approach to wideband spec-trum sensing for cognitive radios[J]. Proc 1st Int Conf Cognitive Radio Oriented Wireless Networks and Communications[C]. 2006.
[9]	HWANG C H , LAI G L . Spectrum sensing in wideband OFDM cogni-tive radios[J]. IEEE Transactions on Signal Processing, 2010,58(2):709-719.
[10]	QUAN Z , CUI S G . Optimal multiband joint detection for spectrum sensing in cognitive radio networks[J]. IEEE Trans on Signal Process-ing, 2009,57(3):1128-1140.
[11]	WAX M , KAILATH T . Detection of signals by information theoretic criteria[J]. IEEE Transactions on Acoustics, Speech and Signal Proc-essing, 1985,33(2):387-392.
[12]	WU H T , YANG J F . Source number estimation using transformed gerschgorin radii[J]. IEEE Trans on Signal Processing, 1995,43(6):1325-1333.
[13]	LIU S , SHEN J , ZHANG R . Information theoretic criterion based spectrum sensing for cognitive radio[J]. IET Communications, 2008,2(6):753-762.
[14]	WILKINSON J H . The Algebraic Eigenvalue Problem[M]. Oxford, UKClarendon, 1988.
[15]	SCHMIDT R O . A Signal Subspace Approach to Multiple Emitter Location and Spectral Estimation[D]. Dissertation, Stanford University, Stanford, 1981.
[16]	TANDRA R , SAHAI A . Fundamental limits on detection in low SNR under noise uncertainty[A]. Proc Wireless Com 2005[C]. Maui, HI, 2005.464-469.

算法名称	感知算法	限制条件	判决方程
ISTT	Threshold test	噪声功率已知	式(1)
AIC/MDL	COSS/likelihood test	高斯信号	式(36)和式(37)
GAIC/GMDL	COSS/GLE	高斯信号	式(44)和式(45)
GDRI	COSS/Threshold test	迭代比较	式(49)