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WO2014190573A1 - Procédé et dispositif de détection de spectre pour un essai d'ajustement sur la base de valeurs propres normalisées - Google Patents

Procédé et dispositif de détection de spectre pour un essai d'ajustement sur la base de valeurs propres normalisées Download PDF

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Publication number
WO2014190573A1
WO2014190573A1 PCT/CN2013/077584 CN2013077584W WO2014190573A1 WO 2014190573 A1 WO2014190573 A1 WO 2014190573A1 CN 2013077584 W CN2013077584 W CN 2013077584W WO 2014190573 A1 WO2014190573 A1 WO 2014190573A1
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WIPO (PCT)
Prior art keywords
normalized
eigenvalues
eigenvalue
goodness
signal
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PCT/CN2013/077584
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English (en)
Chinese (zh)
Inventor
王东明
刘瑞勋
吴雨霏
王向阳
金石
唐文锐
黄禹淇
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Southeast University
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Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/021—Estimation of channel covariance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/0238—Channel estimation using blind estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L27/00—Modulated-carrier systems
    • H04L27/0006—Assessment of spectral gaps suitable for allocating digitally modulated signals, e.g. for carrier allocation in cognitive radio
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/14—Spectrum sharing arrangements between different networks

Definitions

  • the present invention relates to the field of wireless communication technologies, and in particular, to a spectrum sensing method and apparatus that do not need to transmit any feature information of a signal.
  • spectrum detection In cognitive radio systems, how to discern whether there is an authorized user's signal on the spectrum is the first problem to be solved. This problem is called spectrum detection or spectrum sensing.
  • spectrum detection methods include: energy detection, matching filter detection, and cyclostation detection.
  • the energy detection complexity is low, but due to the uncertainty of noise, its performance deteriorates severely.
  • the matched filtering performance is excellent, but the characteristics of the known transmitted signal are required.
  • the performance of the cyclostationary detection is also excellent, but the complexity is high, and it is limited in practical applications.
  • the covariance matrix of the received signal it can be judged whether the signal is noise or not in the frequency band, and the theoretical basis of its operation is as follows.
  • the conventional technical scheme proposes a spectrum sensing using the eigenvalues of the covariance matrix of the received signal, and the decision variable can be composed of the eigenvalues of the covariance matrix.
  • a method of constructing a decision variable that is, the ratio of the largest and smallest eigenvalues, is given.
  • the conventional technical solution also proposes to use the ratio of the geometric mean and the arithmetic mean of the feature values as the decision variable.
  • the advantage of these methods is that there is no need to send any a priori information of the signal, nor any statistical characteristics of the noise. However, these methods do not take advantage of the distribution characteristics of the eigenvalues. Summary of the invention
  • the present invention proposes a spectrum detection method and apparatus based on the normalized goodness test of the normalized eigenvalues.
  • the spectrum sensing method based on the goodness-of-fit test of the normalized eigenvalue includes the following steps: (1) receiving a wireless signal on a frequency band to be perceived; (2) Sampling and filtering the received signal, calculating the covariance matrix of the signal, denoted as R, and its dimension is LXL;
  • the cumulative distribution function F ( ⁇ , based on the normalized eigenvalue of the noise, calculates the cumulative probability of the normalized eigenvalue: F ⁇ ) ⁇ ) ⁇ - ⁇ L) .
  • the cumulative distribution function of the eigenvalues is obtained by theoretical calculation, or by simulation ( ⁇ is made into a tabular form, and the table looks for the cumulative probability of normalized eigenvalues.
  • the decision variable is tested by Anderson-Darlin.
  • the method is applicable to spectrum sensing of single antenna and multi-antenna systems.
  • the method is applicable to multi-node collaborative sensing.
  • a spectrum sensing device based on a goodness-of-fit test of normalized eigenvalues comprising: a wireless signal sampling and filtering module, a covariance matrix calculation module, an eigenvalue decomposition module, a normalized calculation module of eigenvalues, and a normalized feature a cumulative probability calculation module, a decision variable calculation module, and a decision module;
  • the wireless signal sampling and filtering module is configured to obtain a wireless signal of the perceived frequency band
  • the covariance matrix calculation module is configured to calculate a covariance matrix of the signal to be perceived
  • the eigenvalue decomposition module is configured to calculate eigenvalue decomposition of a covariance matrix of the signal to be perceived; the normalization calculation module of the eigenvalue divides the eigenvalue by the sum of all eigenvalues to obtain a normalized feature Value
  • the cumulative probability calculation module of the normalized feature value calculates a cumulative probability corresponding to each normalized feature value; the decision variable calculation module calculates a test amount according to a cumulative probability of the normalized feature value; -Darling goodness of fit test method;
  • the decision module includes a comparator for comparing the decision variable with a threshold.
  • the present invention adopts the above technical solution, and has the following beneficial effects: In the calculation stage of the decision variable , the method only needs simple addition and comparison operations, and the complexity is very low compared to the spectrum sensing based on the eigenvalue in the background art, and the noise is relatively low. The uncertainty is not sensitive. Furthermore, the invention is also applicable to spectral sensing of multi-antenna systems.
  • FIG. 1 is a flowchart of a spectrum sensing method according to an embodiment of the present invention
  • FIG. 2 is a block diagram of a spectrum sensing device according to an embodiment of the present invention.
  • FIG. 3 is a block diagram of a decision variable calculation module when using the Anderson-Darling goodness-of-fit test method according to an embodiment of the present invention
  • FIG. 4 is a schematic diagram showing performance comparison between the embodiment of the present invention and the background art when the sampling point is 64 for the wireless microphone signal;
  • FIG. 5 is a schematic diagram showing performance comparison between the embodiment of the present invention and the background technology when the sampling point is 128 for the wireless microphone signal;
  • FIG. 6 is a schematic diagram showing performance comparison between the embodiment of the present invention and the background art when the sampling point is 32 for the multi-point cooperative spectrum sensing model;
  • FIG. 7 is a schematic diagram showing performance comparison between the embodiment of the present invention and the background art for a multi-point cooperative spectrum sensing model with a sampling point of 64.
  • the spectrum detection method based on the normalized goodness test of the normalized eigenvalues, as described in FIG. 1, includes the following steps:
  • the spectrum sensing device receives the wireless signal on the frequency band to be perceived
  • Scenario 1 The covariance matrix of the signal can be estimated by the sliding average method. Suppose the received signal sequence is expressed as
  • N Indicates the number of oversampling points of the signal (or for a multi-antenna receiving system, N can be modeled as the number of receiving antennas.)
  • N can be modeled as the number of receiving antennas.
  • Scenario 2 Cooperative spectrum sensing model, assuming a flat fading channel, and the number of cooperative nodes is L. Then, the received signal at the moment " can be expressed as,
  • Equation 5 XP ⁇ 2 H n s n +w n [Equation 5] where is the received signal vector of > ⁇ 1, is the transmitted signal vector of xl, is the standard Rayleigh fading channel matrix, P is the LxL receiving correlation matrix, ⁇ is independent The same distribution of Gaussian white noise, the variance is. Then, the covariance matrix of the received signal can be calculated by the following formula.
  • R is a matrix of LxL.
  • the spectrum sensing device covariance matrix performs eigenvalue decomposition, and the eigenvalues are expressed as small, large, ⁇ ,..., ⁇ .
  • the spectrum sensing device calculates the normalized eigenvalue, that is, the eigenvalue divided by the sum of all eigenvalues.
  • the result is expressed as follows. We will perform the goodness-of-fit test of the normalized eigenvalue.
  • the spectrum sensing device calculates the cumulative probability of the normalized feature value according to the cumulative distribution function F( ⁇ ) of the normalized feature value of the noise: ⁇ 6 ⁇ , ⁇ 6 ⁇ , ⁇ , ⁇ 6 ⁇ ;
  • a key step in performing the goodness-of-fit test is to calculate the cumulative probability of the data to be tested.
  • the cumulative probability calculation of the normalized trait value can be calculated by the theoretical formula, or the cumulative probability of the normalized eigenvalue can be obtained by simulation and stored as a table for query.
  • the cumulative distribution function into a table and store it in memory to reduce the computational complexity.
  • the cumulative probability of normalized eigenvalues is obtained by looking up the table. 6)
  • the spectrum sensing device calculates the decision variable ⁇ according to the goodness of fit test. When the threshold is greater than a preset threshold, the spectrum sensing device determines that there is an authorization signal in the spectrum. When the threshold is less than a preset threshold, the spectrum sensing device determines that there is no authorization signal, that is, the spectrum is idle. .
  • the cumulative distribution function of the normalized feature values is realized by looking up the table. Compared with the conventional eigenvalue spectrum detecting method, the complexity only increases the calculation of [Formula 6], and thus the complexity is not high.
  • the spectrum sensing device receives the wireless signal on the frequency band to be perceived, samples and filters the received signal, calculates a covariance matrix R of the signal, calculates a eigenvalue decomposition of the covariance matrix, and obtains the sorted eigenvalue. And then calculating the normalized feature value, that is, the sum of the feature values divided by all the feature values. Finally, the decision variable T is calculated according to the goodness of fit test, and when the threshold is greater than a preset threshold, the spectrum sensing device determines There is an authorization signal on the spectrum. When ⁇ is less than a preset threshold, the spectrum sensing device determines that there is no authorization signal, that is, the spectrum is idle.
  • the spectrum sensing device of the present invention comprises: a covariance matrix calculation module, a sorted eigenvalue decomposition module, a normalized eigenvalue calculation module, a goodness-of-fitness test decision variable calculation module, and a decision module.
  • the wireless signal sampling and filtering module is configured to obtain a signal of the perceived frequency band
  • the covariance matrix calculation module is configured to calculate a covariance matrix of the signal to be perceived
  • the ordered eigenvalue decomposition module is configured to perform eigenvalue decomposition on the covariance matrix
  • the feature values are sorted from small to large
  • the normalized feature value calculation module divides the feature value by the sum of all the feature values.
  • Figure 3 shows the decision variable calculation module when using the Anderson-Darling goodness-of-fit test method, which includes the cumulative probability calculation module of the normalized eigenvalues and the calculation module of the Anderson-Darling test decision variable T, where the calculation of ⁇ As shown in [Formula 6].
  • FIG. 1 and FIG. 7 The performance comparison between the method of the present invention and the background art is given in FIG. 1 and FIG. 7 by the actual simulation.
  • the patented technology and the maximum and minimum eigenvalue ratio detection are insensitive to noise uncertainty. That is, the presence or absence of noise uncertainty does not affect their performance.
  • the figure shows the performance of the patented technology and background art in the presence of noise uncertainty.
  • the noise uncertainty coefficient is 0.5 dB.
  • Figure 4 and Figure 5 show the signal model of scene 1 as an example.
  • the transmitted signal is a wireless microphone signal.
  • the number of sampling points is 64 points and 128 points, and the sliding factor is 16.
  • the oversampling factor is 1. It can be seen from Fig. 4 that the patented method is slightly worse than the energy detection under noiseless uncertainty, but is significantly better than the energy detection, the maximum and minimum eigenvalue ratio detection and the geometric mean of the eigenvalues in the presence of noise uncertainty. Arithmetic mean ratio detection. As the number of samples increases to 128 points, it can be seen from Figure 5 that this patented method is superior to all other considered techniques when the probability of detection is greater than 0.9, including energy detection with precisely known noise variance.
  • FIG. 6 and 7 are examples of the signal model of scene 2, and signal generation is given by [Equation 5].
  • the element receiving the correlation matrix P is generated by the following method:
  • the first row element of the P matrix is equal to " I , where" is equal to 0.9.
  • the number L of cooperative antennas is 16.
  • the geometric mean and arithmetic mean ratio are detected.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Monitoring And Testing Of Transmission In General (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

La présente invention concerne le domaine technique des communications radio et, en particulier, un procédé de détection de spectre et un dispositif de détection de spectre pour un essai d'ajustement sur la base de valeurs propres normalisées. Le procédé comprend les étapes suivantes : le dispositif de détection de spectre reçoit un signal sur une bande de fréquences autorisée, calcule une matrice de covariance du signal reçu après avoir échantillonné et filtré ledit signal, effectue une décomposition en valeurs propres de la matrice de covariance, classe les valeurs propres en ordre ascendant, puis divise les valeurs propres par la somme de toutes les valeurs propres pour acquérir les valeurs propres normalisées, effectue finalement l'essai d'ajustement des valeurs propres normalisées et détermine, sur la base du résultat de l'essai, si un signal est présent ou non. Le procédé et le dispositif selon la présente invention présentent les avantages de supprimer le besoin de caractéristiques de signal autorisées et d'être insensibles à l'égard de l'incertitude due au bruit tout en offrant une excellente efficacité.
PCT/CN2013/077584 2013-05-27 2013-06-20 Procédé et dispositif de détection de spectre pour un essai d'ajustement sur la base de valeurs propres normalisées Ceased WO2014190573A1 (fr)

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CN108462544A (zh) * 2018-03-27 2018-08-28 广东工业大学 一种频谱感知方法及装置
CN108900268A (zh) * 2018-09-12 2018-11-27 宁波大学 利用小特征值估计噪声功率的最大特征值频谱感知方法

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CN103795481B (zh) * 2014-01-28 2017-02-22 南京邮电大学 一种基于自由概率理论的协作频谱感知方法
CN103986626B (zh) * 2014-05-30 2017-06-13 电子科技大学 基于端到端实测数据统计的路径特征刻画模拟方法及装置
CN104320209B (zh) * 2014-10-14 2016-04-20 宁波大学 一种基于拟合优度检验的频谱感知方法
CN105187143B (zh) * 2015-09-30 2017-10-20 西安邮电大学 一种基于二项分布的快速频谱感知方法和装置
CN108322277B (zh) * 2018-04-04 2021-01-12 宁波大学 一种基于协方差矩阵反特征值的频谱感知方法
CN108900267B (zh) * 2018-07-17 2021-10-15 浙江万胜智能科技股份有限公司 基于特征值的单边右尾拟合优度检验频谱感知方法及装置
CN109286937B (zh) * 2018-09-12 2023-03-24 宁波大学 利用小特征值估计噪声功率的协方差矩阵频谱感知方法
CN112181982B (zh) * 2020-09-23 2021-10-12 况客科技（北京）有限公司 数据选取方法、电子设备和介质

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CN108900268A (zh) * 2018-09-12 2018-11-27 宁波大学 利用小特征值估计噪声功率的最大特征值频谱感知方法

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