CN102148650B - Detecting method for energy detector based on weighting and combining of detection rate and false alarm rate - Google Patents

Abstract

The invention relates to a detection method of an energy detector based on the weighted combination of detection rate and false alarm rate. The hypothesis H ₀ that there is no wireless spectrum signal x(t) and the hypothesis H ₁ that there is a line spectrum signal x(t) detect whether there is a signal x(t) in the received signal y(t), including the following steps : 1) Set the detection rate weighting factor and the false alarm rate weighting factor; 2) The detection rate P _d and the false alarm rate P _fa are weighted and combined to form the objective function P(γ); 3) Set P(γ) as the objective optimization function , the decision threshold γ of energy detection is obtained by the objective optimization function; 4) the energy E of the received signal y(t) is calculated by the squarer and the integrator within 0≤t≤T time; 5 the decision device is based on the decision threshold The difference between γ and the energy E of the signal y(t) is used to determine whether the signal x(t) exists. The advantages are: the judgment threshold of the energy detector can be flexibly adjusted according to the different requirements of the system for detection rate and false alarm rate, the dynamic range of the judgment threshold is large, and the accuracy of signal perception and detection is high; the system structure is simple, the detection time is short, and it can be applied to Spectrum detection of any signal.

Description

Detection method of energy detector based on weighted combination of detection rate and false alarm rate

technical field

The invention relates to wireless spectrum perception and detection technology in wireless communication, more specifically to a detection method of an energy detector based on the weighted combination of detection rate and false alarm rate in a wireless channel environment.

Background technique

At present, the contradiction between the ever-increasing spectrum demand and the limited spectrum resources is becoming more and more prominent, which seriously restricts the development of wireless communication services. However, from the perspective of actual wireless spectrum operation, the allocated (authorized) wireless spectrum is idle to a considerable extent in time and space. According to the measurement data report on the wireless spectrum, the spectrum utilization rate of most wireless frequency bands is only 10%. %about. How to effectively solve the contradiction between the scarcity of spectrum resources and the low utilization rate of spectrum has become a key technology in wireless communication. Cognitive-enabled radio (CR) is recognized as an effective technical means to efficiently utilize wireless spectrum.

The core of CR technology is to detect "spectrum holes" through dynamic spectrum sensing, reasonably occupy temporarily available frequency bands, and adaptively and dynamically change its own signal transmission power, transmission frequency, modulation mode and other transmission parameters according to the perception information to avoid the current communication. master user (authorized user). It requires the secondary user (CR user) to sense the surrounding wireless environment and change its own transmission parameters to ensure that no interference is generated to the existing primary user. It is this feature of rapidly changing its own transmission parameters that makes CR technology considered the "next revolution" in future communications. Today, when spectrum compatibility and interoperability are becoming more and more difficult, CR technology with both physical layer (PHY) and network layer (MAC) sensing functions is placed with high expectations.

Correct perception and detection of the surrounding wireless environment is the premise of CR work. Common spectrum detectors include matched filter detectors, energy detectors, cyclostationary feature detectors, wavelet detectors, and covariance detectors. Among them, the energy detector is simple to implement and does not require prior knowledge of the signal. It only needs to measure the total energy of the received signal in the observation space in the frequency domain or time domain to determine whether there is an authorized user. It is the most widely used one at present. Spectrum detection method.

Existing energy detectors are designed based on the constant false alarm rate criterion or constant detection rate criterion. The dynamic range of the judgment threshold of this energy detector is very small, even in the environment of low signal-to-noise ratio, there is no optional judgment threshold, which cannot meet the requirements of the wireless cognitive system for the detection rate and false alarm rate of the wireless spectrum at the same time, which affects the wireless spectrum. Perception and detection performance.

Contents of the invention

The object of the present invention is to overcome the deficiency of its detection method of the above-mentioned existing energy detector and design a kind of detection method based on the detection rate and the false alarm rate weighted combination of the energy detector, to solve the judgment threshold dynamic range in the spectrum detection method is small to realize fast, accurate and effective signal spectrum sensing and detection in wireless environment.

Above-mentioned purpose is achieved through following technical scheme:

Two assumptions are made on the wireless spectrum signals existing in the surrounding environment: H ₀ without wireless spectrum signal x(t) and H ₁ with wireless spectrum signal x(t)

H ₀ : y(t)=n(t)

H ₁ : y(t)=x(t)+n(t)

n(t) represents additive Gaussian noise, x(t) represents authorized wireless spectrum signals, and signal y(t) received within T time period, 0≤t≤T. Detecting whether there is signal x(t) in signal y(t), is characterized in that comprising the following steps:

1) Set the weighting factor. According to the requirements of the cognitive system for the spectrum detection rate _Pd and the false alarm rate _Pfa , set the detection rate weighting factor α and the false alarm rate weighting factor β, 0≤α≤1, 0≤β≤1 ;

2) Combining algorithm, according to the system transmission rate requirements and channel noise size and the above weighting factors, the detection rate _Pd and the false alarm rate _Pfa are weighted and combined to form the objective function P(γ);

3) Calculate the decision threshold, set the target optimization function of P(γ), and obtain the decision threshold γ of energy detection by the target optimization function;

4) Energy calculation, the squarer and the integrator calculate the energy E of the received signal y(t) within 0≤t≤T time;

5) Judgment, the decision device performs a difference comparison according to the decision threshold γ and the energy E of the signal y(t), such as E-γ>0, there is a signal x(t) in y(t), then as E-γ<0, there is no signal x(t) in y(t).

The further design of the above method is that the weighted combination algorithm includes the linear weighted combination algorithm P(γ)=αP _d +β(1-P _fa ), P(γ)=α(1-P _d )+βP _fa and non- Linear Weighted Merging Algorithm

以为目标优化函数，合并算法P(γ)＝α(1-P_d)+βP_fa以为目标优化函数，得到最佳判决门限。The further design of the above method is that the combination algorithm P(γ)=αP _d +β(1-P _fa ) and

by is the objective optimization function, the combined algorithm P(γ)=α(1-P _d )+βP _fa is given by Optimize the function for the objective and get the best decision threshold.

The further design of the above method is that the optimal decision threshold using the linear weighted merging algorithm is

$\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; NP</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}}\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; [</span>\frac{\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&alpha;\&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ]</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; )</span>}}$

in, is the noise power, P _x is the authorized signal power, and N is the number of detection sampling points.

A further design of the above method is that the calculation of the energy of the received signal y(t) can be performed in either the time domain or the frequency domain.

The method of the invention lies in that the detection rate and the false alarm rate weighted combination processing algorithm is adopted in the judgment threshold setting of the energy detector. Through the weighted combination of the spectrum detection rate and false alarm rate required by the system, the judgment threshold of the energy detector is accurately calculated, and the accurate sensing and detection of the wireless spectrum is realized. Specifically, the weighting factor is set according to the requirements of the cognitive system on the detection rate and false alarm rate, and a reasonable combination algorithm is selected to optimize the weighted target of the detection rate and false alarm rate to calculate the corresponding judgment threshold, so that the decision device can accurately perceive useful signals Whether it appears or not solves the problem that the decision threshold in the energy detector is difficult to preset. As a result, such beneficial effects can be produced:

(1) Set the weighting factor according to the different requirements of the system for detection rate and false alarm rate, and flexibly adjust the judgment threshold of the energy detector to adapt to the dynamic requirements of different user systems for spectrum detection performance, and improve the accuracy of signal perception and detection;

(2) Through the combined processing of spectrum detection rate and false alarm rate, the preset of effective and accurate energy detector decision threshold is realized;

(3) The system structure is simple, the operation complexity is low, the detection time is short, and it can be applied to the spectrum detection of any signal.

Description of drawings

Fig. 1 is a structural block diagram of the present invention.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

Wireless spectrum signals that exist in the surrounding environment. Two assumptions are given: H ₀ —there is only additive Gaussian noise n(t) in the surrounding environment; H ₁ —there are allocated (licensed) wireless spectrum signals x(t) and additive Gaussian noise n(t) in the surrounding environment t), namely

H ₀ : y(t)=n(t)

H ₁ : y(t)=x(t)+n(t)

Let the received signal be y(t), 0≤t≤T. Now estimate and detect whether there is a signal x(t) in the received signal y(t), the detection method of the implemented energy detector is shown in the block diagram in Figure 1, and the specific process is as follows:

比较合适。此加权合并算法的计算过程，可以采用现场可编程门阵列(FPGA)实现。然后，对加权合并后的目标函数进行优化，以加权合并的P(γ)的

或

为目标优化函数。其中，合并算法P(γ)＝αP_d+β(1-P_fa)和

以

为目标优化函数，合并算法P(γ)＝α(1-P_d)+βP_fa以

为目标优化函数，得到最佳判决门限为：First, according to the requirements of the system for detection rate P _d and false alarm rate P _fa , set the appropriate detection rate weighting factor α and false alarm rate weighting factor β. The value ranges of α and β are: 0≤α≤1, 0≤ β≤1. The detection rate P _d and the false alarm rate P _fa are a pair of contradictions. If the system has high requirements on the detection rate, a larger detection rate weighting factor α can be appropriately selected, and a smaller false alarm rate weighting factor β can be appropriately selected. Conversely, if the system has high requirements on the false alarm rate, a larger weighting factor β for the false alarm rate can be appropriately selected, and a smaller weighting factor α for the detection rate can be appropriately selected. Then select the appropriate weighting algorithm according to the system transmission rate requirements and the channel noise size to carry out weighted and combined processing on the detection rate and false alarm rate required by the system. For example, when the signal-to-noise ratio is between -20dB and 0dB, it is more appropriate to choose the linear weighted combination algorithm P(γ)=αP _d +β(1-P _fa ) or P(γ)=α(1-P _d )+βP _fa , when the signal-to-noise ratio is between 0dB and 20dB, the non-linear weighted combination algorithm is selected

More appropriate. The calculation process of the weighted merging algorithm can be realized by using a field programmable gate array (FPGA). Then, the weighted combined objective function is optimized to weight the combined P(γ)

or

Optimize a function for an objective. Among them, the combination algorithm P(γ)=αP _d +β(1-P _fa ) and

by

is the objective optimization function, the combined algorithm P(γ)=α(1-P _d )+βP _fa is given by

Optimizing the function for the objective, the best decision threshold is obtained as:

$\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\sqrt{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; N</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}\mathrm{<span\; class="notranslate">\; ln</span>}<span\; class="notranslate">\; [</span>\frac{\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; \&alpha;\&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; ]</span>}}{\frac{\mathrm{<span\; class="notranslate">\; 2</span>}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>}}$

为噪声功率，

为授权信号功率，N为检测采样点数。并以此值预设为能量检测器的判决门限。In the formula,

is the noise power,

is the authorized signal power, and N is the number of detection sampling points. And this value is preset as the decision threshold of the energy detector.

where the power of the noise n(t) It can be measured through a dedicated channel that only sends pilot signals, and the power P _x of the signal x(t) can be obtained from the total power P _y of the received signal y(t):

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; the\; y</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}$

The number of detection sampling points N depends on the length of detection time T allowed by the system. If the system allows a short detection time T, then there are fewer sampling points, and the value of N will be small; on the contrary, if the system allows a long detection time T, and more sampling points, then the value of N will be large, and the detection accuracy will be high.

Then, the received signal y(t) is squared with a squarer first, and then the result of the squared calculation is sent to the integrator for integral calculation, so as to obtain the energy of the received signal y(t) within 0≤t≤T, that is

$\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>{<span\; class="notranslate">\; \&Integral;</span>}_{\mathrm{<span\; class="notranslate">\; 0</span>}}^{\mathrm{<span\; class="notranslate">\; T</span>}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; dt</span>}<span\; class="notranslate">\; .</span>$

Finally, the decider compares the difference between the above-mentioned decision threshold γ and the energy E of the above-mentioned signal y(t), if E-γ>0, there is a signal x(t) in y(t), then the detection During the time 0≤t≤T, the authorized wireless signal x(t) is in use in this frequency band, and this frequency band cannot be used for communication any more. If E-γ<0, and there is no signal x(t) in y(t), then within the detection time 0≤t≤T, there is no authorized wireless signal in this frequency band, and this frequency band can be used for communication.

Claims (5)

1. The detection method of the energy detector based on the weighted combination of the detection rate and the false alarm rate performs two assumptions on the wireless spectrum signals existing in the surrounding environment: absence of H of the radio-spectrum signal x (t)₀And H in the presence of a radio-spectrum signal x (t)₁

H₀：y(t)＝n(t)

H₁：y(t)＝x(t)+n(t)

n (T) additive gaussian noise, x (T) authorized radio spectrum signals, signals y (T) received during a time period T, 0 ≦ T, and detecting the presence of signals x (T) in signals y (T), comprising the steps of:

1) setting a weighting factor, and detecting the frequency spectrum detection rate P according to the cognitive system_dAnd false alarm rate P_faSetting a detection rate weighting factor alpha and a false alarm rate weighting factor beta, wherein alpha is more than or equal to 0 and less than or equal to 1, and beta is more than or equal to 0 and less than or equal to 1;

2) the merging algorithm is used for detecting the detection rate P according to the system transmission rate requirement, the channel noise and the weighting factor_dAnd false alarm rate P_faCarrying out weighted combination to form an objective function P (gamma), wherein the weighted combination algorithm P (gamma) comprises a linear weighted combination algorithm P (gamma) ═ alpha P_d+β(1-P_fa)、P(γ)＝α(1-P_d)+βP_faAnd nonlinear weighting

3) Calculating a decision threshold, setting a target optimization function of P (gamma), and obtaining the decision threshold gamma of energy detection by the target optimization function;

4) energy calculation, namely calculating the energy E of the received signal y (T) within the time of T being more than or equal to 0 and less than or equal to T by a squarer and an integrator;

5) and judging, the decider compares the difference value according to the judgment threshold gamma and the energy E of the signal y (t), if E-gamma is more than 0, the signal x (t) exists in y (t), otherwise, if E-gamma is less than 0, the signal x (t) does not exist in y (t).

2. The method of claim 2, wherein the energy meter is weighted by a combination of detection rate and false alarm rate, and is implemented by a combination algorithm P (γ)

Or

And obtaining an optimal decision threshold for the target optimization function.

3. The method of claim 3 based onThe method for detecting the energy device with weighted combination of measurement rate and false alarm rate is characterized in that the combination algorithm P (gamma) ═ alpha P_d+β(1-P_fa) And

to be provided with

For the objective optimization function, the merging algorithm P (γ) ═ α (1-P)_d)+βP_faTo be provided withAnd obtaining an optimal decision threshold for the target optimization function.

4. The method of claim 4 wherein the optimal decision threshold for the linear weighted combination algorithm is

Wherein,as noise power, P_xAnd N is the number of detection sampling points for authorizing signal power.

5. The detection method of claim 5 wherein the energy of the received signal y (t) is calculated in either the time domain or the frequency domain.

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