CN103401622B - Joint spectrum sensing method for sensing primary user signal in the presence of cognitive user signal - Google Patents

Abstract

A joint spectrum sensing method for perceiving a primary user signal in the presence of a cognitive user signal relates to a spectrum sensing method in the field of cognitive radio technology. The invention aims to solve the problem that the existing spectrum sensing method cannot effectively detect whether the primary user signal exists or not when the cognitive user signal exists in the sensing frequency band. The method: 1. Detect the cognitive user signal through the cognitive user signal sensing module SSSB, and output its channel gain; 2. The cognitive user signal reproduction module SSR reproduces the cognitive user signal in the received signal; 3. Input The signal is subtracted from the output of the cognitive user signal reproduction module SSR to obtain the input signal of the primary user signal sensing module PSSB; 4. The primary user signal sensing module PSSB performs spectrum sensing to determine whether there is a primary user signal. The present invention is applicable to joint spectrum sensing for sensing primary user signals in the presence of cognitive user signals.

Description

A Joint Spectrum Sensing Method for Sensing Primary User Signals in the Presence of Cognitive User Signals

technical field

The invention relates to a spectrum sensing method in the field of cognitive radio technology.

Background technique

As multimedia services such as voice, data, image, and video require higher and higher transmission rates, the broadbandization of the wireless trunking system is an inevitable trend of the next-generation digital trunking system. In order to effectively improve the spectrum utilization of the wireless trunking system, it is an inevitable trend for the development of the wireless trunking system to adopt cognitive radio technology.

In the field of cognitive radio technology, spectrum sensing technology is an important part of cognitive radio. Its role is to discover the use of spectrum and detect whether there is a primary user signal in the current area. At present, the main spectrum sensing methods include energy detection, matched filter detection, cycle feature detection and covariance detection. These spectrum sensing methods have different trade-offs in sensing time, complexity and detection performance. However, the above-mentioned spectrum sensing method can only work to sense the primary user signal without recognizing the user signal. When the cognitive user signal already exists in the frequency band to be sensed, it is difficult for the above-mentioned spectrum sensing method to effectively detect the presence or absence of the primary user signal.

Contents of the invention

The present invention aims to solve the problem that the existing spectrum sensing method cannot effectively detect the presence or absence of the primary user signal when the cognitive user signal exists in the sensing frequency band, thereby providing a method for sensing the primary user signal in the presence of the cognitive user signal Joint Spectrum Sensing Approach.

A joint spectrum sensing method for perceiving primary user signals in the presence of cognitive user signals, which is implemented by the following steps:

Step 1. In the cognitive radio system, use the cognitive user signal sensing module SSSB to detect the cognitive user signal on the input signal, obtain the judgment result of whether there is a cognitive user signal and the channel gain of the cognitive user signal, and output it to Cognitive user signal reproduction module SSR;

Step 2: Use the cognitive user signal reproduction module SSR to reproduce the actual cognitive user signal according to the obtained judgment result of the cognitive user signal, the channel gain of the cognitive user signal, and the local cognitive user signal, and obtain recurring signal;

Step 3, subtracting the input signal in step 1 from the reproduced signal obtained in step 2, and inputting it to the primary subscriber signal sensing module PSSB;

Step 4: Use the primary user signal sensing module PSSB to perform primary user spectrum sensing on the subtracted signal in step 3, obtain the judgment result of whether there is a primary user signal, and complete the perception of the primary user signal in the presence of a cognitive user signal Joint Spectrum Sensing.

In step 1, the cognitive user signal detection of the input signal by using the cognitive user signal sensing module SSSB is realized by using a matched filter detection algorithm.

In step 4, the primary user spectrum sensing is performed on the subtracted signal in step 3 by using the primary user signal sensing module PSSB through an energy detection algorithm or other existing spectrum sensing algorithms.

The present invention proposes a novel joint spectrum sensing method, which can accurately and effectively detect the existence of the primary user signal when there is a cognitive user signal in the sensing frequency band.

Description of drawings

FIG. 1 is a schematic diagram of a signal flow of the spectrum sensing method of the present invention.

Detailed ways

Specific embodiments 1. This specific embodiment is described in conjunction with FIG. 1 , a joint spectrum sensing method for perceiving primary user signals in the presence of cognitive user signals, which is characterized in that it is implemented by the following steps:

Step 1. In the cognitive radio system, use the cognitive user signal sensing module SSSB to detect the cognitive user signal on the input signal, obtain the judgment result of whether there is a cognitive user signal and the channel gain of the cognitive user signal, and output it to Cognitive user signal reproduction module SSR;

Step 2: Use the cognitive user signal reproduction module SSR to reproduce the actual cognitive user signal according to the obtained judgment result of the cognitive user signal, the channel gain of the cognitive user signal, and the local cognitive user signal, and obtain recurring signal;

Step 3, subtracting the input signal in step 1 from the reproduced signal obtained in step 2, and inputting it to the primary subscriber signal sensing module PSSB;

Step 4: Use the primary user signal sensing module PSSB to perform primary user spectrum sensing on the subtracted signal in step 3, obtain the judgment result of whether there is a primary user signal, and complete the perception of the primary user signal in the presence of a cognitive user signal Joint Spectrum Sensing.

Embodiment 2. The difference between this embodiment and the joint spectrum sensing method for sensing primary user signals in the presence of cognitive user signals described in Embodiment 1 is that in step 1, cognitive user signal sensing is adopted. The cognitive user signal detection of the input signal by the module SSSB is realized by using a matched filter detection algorithm.

Specific embodiment 3. The difference between this specific embodiment and the joint spectrum sensing method for sensing the primary user signal in the presence of cognitive user signals described in specific embodiment 1 is that the primary user signal sensing module is used in step 4 The PSSB performs primary user spectrum sensing on the subtracted signal in step 3 through an energy detection algorithm.

Embodiment 4. The difference between this embodiment and the joint spectrum sensing method for sensing the primary user signal in the presence of the cognitive user signal described in Embodiment 1 is that the primary user signal sensing module is used in step 4 The primary user spectrum sensing performed by the PSSB on the subtracted signal in step 3 is realized through other existing spectrum sensing algorithms.

Principle: The signal flow of the spectrum sensing method of the present invention is shown in Figure 1: in Figure 1, y[n] is the sampling signal of the received signal through A/D, and s _su [n] is the locally known cognitive user signal , is the channel gain of the cognitive user signal, y _SSR [n] is the actual cognitive user signal reproduced locally, H _x1 /H _x0 indicates the presence/absence of the cognitive user signal, and H _1x /H _0x indicates the presence/absence master user signal.

Cognitive user signal sensing block (SSSB) is used to sense whether there is a cognitive user signal, and output the channel gain of the actual cognitive user signal . The Cognitive Subscriber Signal Reproduction Module (SSR) reproduces the actual Cognitive Subscriber Signal y _SSR [n] locally according to the output of the SSSB and subtracts it from the input signal as the input of the Primary Subscriber Signal Sensing Module (PSSB). Spectrum sensing method to sense whether there is a primary user signal.

The present invention adopts quaternary hypothetical model:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 00</span>}}\\ {\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 01</span>}}\\ {\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 10</span>}}\\ {\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 11</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

H ₀₀ indicates that there is no cognitive user signal or primary user signal, H ₀₁ indicates that there is a cognitive user signal, H ₁₀ indicates that there is a primary user signal, and H ₁₁ indicates that both cognitive user signals and primary user signals exist. The corresponding relationship between the output results of the joint spectrum sensing algorithm and the four-element hypothesis model is shown in Table 1.

Table 1

Let h _pu be the channel gain of the primary user signal, and h _su be the channel gain of the cognitive user signal.

Define y[n] as:

y[n]=h _pu s _pu [n]+h _su s _su [n]+w[n] (2)

where w[n] is additive Gaussian white noise, its expectation is 0, and its variance is σ _n ² , s _pu [n] and s _su [n] are primary user signal and cognitive user signal respectively, and their expectation is 0, and the variance is and .

Then the following conclusions can be obtained for the SSSB module:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; \&ap;</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

According to the results of SSSB, the output of SSR is:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; SSR</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}}_{\mathrm{<span\; class="notranslate">\; su</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

The input of PSSB can be obtained:

y[n]-y _SSR [n]≈h _pu s _pu [n]+w[n] (6)

So you can get:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; x</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; x</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

If PSSB adopts the energy detection algorithm, SSSB adopts the matched filter detection algorithm for analysis. Let Y _su , Y _pu , λ _su , λ _pu and N be the detection statistics of the SSSB module, the detection statistics of the PSSB module, The detection threshold of the SSSB module, the detection threshold and the detection length of the PSSB module, we can get the following conclusions:

Y _su obeys the following normal distribution:

In the formula,

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}}{\underset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; +</span>\frac{\underset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span>}{\underset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; h</span>}}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

So the output of the SSR module can be obtained:

${\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; SSR</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\mathrm{<span\; class="notranslate">\; 0</span>}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

The input to the PSSB module is:

$\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; SSR</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 0</span>}}\\ {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; w</span>}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; the\; s</span>}}_{\mathrm{<span\; class="notranslate">\; su</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; [</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; ]</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; x</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

Y _pu obeys the following square root distribution:

$\frac{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}}{{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; signal</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}\frac{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; pu</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">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; x</span>}}\\ \frac{{\mathrm{<span\; class="notranslate">\; Y</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}}{{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; pu</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">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>& {\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; x</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 14</span>}<span\; class="notranslate">\; )</span>$

Let Pf ^S be the false alarm probability of SSSB, Pd ^S be the detection probability of SSSB, Pf ^P be the false alarm probability of PSSB, and Pd ^P be the detection probability of PSSB. We obtain the detection performance of the joint spectrum sensing algorithm.

$\left\{\begin{array}{c}{\mathrm{<span\; class="notranslate">\; Pf</span>}}^{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; pu</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><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\\ {\mathrm{<span\; class="notranslate">\; PD</span>}}^{\mathrm{<span\; class="notranslate">\; P</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; pu</span>}}^{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; \&sigma;</span>}}_{\mathrm{<span\; class="notranslate">\; pu</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>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; \&Gamma;</span>}<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span>\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; )</span>$

From the above analysis, it can be seen that the detection performance of the primary user signal sensing module of the joint spectrum sensing algorithm has nothing to do with the cognitive user signal, but only with the noise and the strength of the primary user signal. Therefore, in the presence of the cognitive user signal, the algorithm can The primary user signal can be effectively and accurately detected without considering the interference of the cognitive user signal.

Claims (3)

1. A joint spectrum sensing method for perceiving primary user signals in the presence of cognitive user signals, characterized in that it is realized by the following steps: Step 1. In the cognitive radio system, use the cognitive user signal sensing module SSSB to detect the cognitive user signal on the input signal, obtain the judgment result of whether there is a cognitive user signal and the channel gain of the cognitive user signal, and output it to Cognitive user signal reproduction module SSR; Step 2: Use the cognitive user signal reproduction module SSR to reproduce the actual cognitive user signal according to the obtained judgment result of the cognitive user signal, the channel gain of the cognitive user signal, and the local cognitive user signal, and obtain recurring signal; Step 3, subtracting the input signal in step 1 from the reproduced signal obtained in step 2, and inputting the subtracted signal to the main user signal sensing module PSSB; Step 4: Use the primary user signal sensing module PSSB to perform primary user spectrum sensing on the subtracted signal in step 3, obtain the judgment result of whether there is a primary user signal, and complete the sensing of the primary user signal in the presence of a cognitive user signal Joint Spectrum Sensing. 2. A kind of joint spectrum sensing method for perceiving the primary user signal in the presence of the cognitive user signal according to claim 1, characterized in that in step 1, the cognitive user signal sensing module SSSB is used to recognize the input signal User signal detection is implemented using a matched filter detection algorithm. 3. A kind of joint spectrum sensing method of perceiving the primary user signal in the presence of the cognitive user signal according to claim 1, characterized in that in step 4, the primary user signal sensing module PSSB is used to subtract in step 3 The primary user spectrum sensing of the signal is realized through the energy detection algorithm.