CN107196722B - Self-adaptive compressed spectrum sensing method - Google Patents

Abstract

The invention provides a self-adaptive compressed spectrum sensing method, which is characterized by comprising the following steps: by setting up observationsGenerating an observation matrix and a check matrix by the initial row and column number and the row and column number of the check matrix, and performing compression measurement on the obtained signals by using the observation matrix and the check matrix to obtain an observed value and a check value_①(ii) a Reconstructing by using the observed value to obtain an estimated value of the signal; the estimated value of the signal is compressed and measured by using the check matrix to obtain a check value_②(ii) a Measurement check value_①And a check value_②After the similarity, comparing the set tolerance threshold, if the tolerance threshold is in, carrying out spectrum sensing according to the estimated value of the signal to obtain a sensing result, if the tolerance threshold is exceeded, increasing the number of rows of the observation matrix according to the situation variable step length, and carrying out recompression measurement. The invention can self-adaptively and dynamically set the measurement parameters in the compression measurement process by fully utilizing the prior information of the wireless environment and setting a feedback mechanism, thereby realizing the high-efficiency compressed spectrum sensing while meeting the sensing requirement.

Description

An Adaptive Compressed Spectrum Sensing Method

technical field

The present invention relates to the technical field of wireless communication, in particular to an adaptive compressed spectrum sensing method.

Background technique

In the context of the explosive growth of wireless communication services, the shortage of spectrum resources has become increasingly prominent. Under the condition of limited spectrum resources, how to improve the utilization rate of spectrum resources to meet the communication needs has become a hot issue in related research today. In the traditional fixed allocation mode, the utilization rate of spectrum resources in the time domain, frequency domain, and space domain is not high, and the problem of resource idleness is prominent. By sensing the idle frequency band and using the idle frequency band to realize communication without affecting the normal communication of authorized users of spectrum resources, cognitive radio technology breaks the traditional fixed allocation mode of spectrum resources, and provides ideas and methods for improving spectrum utilization.

As the key first step of cognitive radio, spectrum sensing is the basis for the implementation of subsequent steps, and its performance directly affects the overall performance of cognitive radio network. The traditional spectrum sensing technology is limited by the Nyquist sampling law, and is out of date in the context of broadband and high-speed wireless communications.

In the existing compressed spectrum sensing algorithms, the measurement parameters in the compressed measurement process are often artificially set to a fixed value, which is not very adaptable to the wireless environment under different (frequency domain) sparse conditions, which directly affects the accuracy and reliability of the sensing results. Efficiency of the spectrum sensing process.

SUMMARY OF THE INVENTION

The invention provides an adaptive compressed spectrum sensing method. By making full use of the prior information of the wireless environment and setting the feedback mechanism, the measurement parameters in the compression measurement process can be adaptively and dynamically set, so as to achieve efficient compression that not only meets user requirements Spectrum sensing.

In order to achieve the above object, the present invention has adopted the following technical solutions:

An adaptive compressed spectrum sensing method, comprising:

S1: Set the initial value of the number of rows and columns of the observation matrix and the initial value of the number of rows and columns of the check matrix, set the first error tolerance threshold value and the second error tolerance threshold value, set the first step length value and the second step value Long value, get wireless signal;

S2: Generate an observation matrix and a check matrix by using the initial value of the number of rows and columns of the observation matrix and the initial value of the number of rows and columns of the check matrix, and use the observation matrix and the check matrix to compress and measure the wireless signal to obtain the observation matrix. value and the first check value;

S3: Perform reconstruction using the observed value to obtain an estimated value of the wireless signal;

S4: Use the check matrix to perform compression measurement on the estimated value of the wireless signal to obtain a second check value;

S5: Measure the similarity between the first check value and the second check value to obtain an evaluation basis value;

S6: Compare the evaluation basis value with the first error tolerance threshold value and the second error tolerance threshold value, if the evaluation basis value exceeds the tolerance range, compare the initial number of rows of the observation matrix in S1 Increase the value of the first step length or the second step length value, and then perform S2, if the evaluation basis value is within the tolerance range, perform spectrum sensing according to the estimated value of the wireless signal and output the sensing result;

S7: Store the output sensing result information in a data center, and update the information in the data center.

In the S1, including:

By receiving the information of the data center and setting the initial value of the row number of the observation matrix and the initial value of the row number of the check matrix according to the sensing requirements, the initial value of the column number of the observation matrix and the check matrix are set according to the requirements for the resolution of the sensing result. the initial value of the number of columns;

The first error tolerance threshold value, the second error tolerance threshold value, and the first step length value and the second step length value are set according to the width of the frequency band to be sensed, the spectral history sparse information and the sensing error requirement.

The wireless signal is a broadband signal received by the user performing spectrum sensing.

In the S5, including:

The evaluation basis value is obtained by measuring the Euclidean distance or the Minkowski distance or the cosine of the included angle between the first check value and the second check value.

In the S6, including:

If the evaluation basis value is less than the first error tolerance threshold value, it is considered that the estimated value of the wireless signal meets the accuracy requirement, spectrum sensing is performed according to the estimated value of the wireless signal, and sensing result information is output.

If the evaluation basis value is greater than the second error tolerance threshold value, it is considered that the estimated value of the wireless signal has a large deviation from the actual distance value, and the initial value of the number of rows of the observation matrix in S1 is increased by the The first step is the long value, and then S2 is executed.

If the evaluation basis value is greater than the first error tolerance threshold value and smaller than the second error tolerance threshold value, it is considered that the deviation between the estimated value of the wireless signal and the actual distance value is small, and the value in S1 The initial value of the number of rows of the observation matrix is increased by the second step value, and then S2 is performed.

The sensing result information includes communication location information, the sparseness of the sensing frequency band, and the occupied idle frequency spectrum.

In the S7, including:

The information of the data center includes the spectral sparsity in the area, as well as the frequency, time point, duration and location range used by authorized users for communication.

It can be seen from the technical solution provided by the present invention that the present invention can adaptively and dynamically set the measurement parameters in the compression measurement process by making full use of the prior information of the wireless environment and setting the feedback mechanism, so as to achieve a highly efficient solution that not only meets user needs but also Compressed Spectrum Sensing.

Additional aspects and advantages of the present invention will be set forth in part in the following description, which will be apparent from the following description, or may be learned by practice of the present invention.

Description of drawings

In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a schematic flowchart (1) of an adaptive compressed spectrum sensing method according to an embodiment of the present invention;

FIG. 2 is a schematic flowchart (2) of an adaptive compressed spectrum sensing method according to an embodiment of the present invention;

3 is a power spectral density diagram of a channel environment in an analog frequency band of an adaptive compressed spectrum sensing method provided by an embodiment of the present invention;

FIG. 4 is a comparison diagram of the change trend of the real value of the perception error and the value of the evaluation basis of the perception error of an adaptive compressed spectrum sensing method according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a sensing result of an adaptive compressed spectrum sensing method according to an embodiment of the present invention.

Detailed ways

Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but not to be construed as a limitation of the present invention.

It will be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" as used herein can include the plural forms as well, unless expressly stated otherwise. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components and/or groups thereof. It will be understood that when we refer to an element as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Furthermore, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.

In order to facilitate the understanding of the embodiments of the present invention, the following will take specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and the embodiments do not constitute limitations to the embodiments of the present invention.

Example:

FIG. 1 and FIG. 2 are schematic flowcharts of an adaptive compressed spectrum sensing method according to an embodiment of the present invention, and FIG. 2 is a more specific representation of FIG. 1 . The REM (Radio Environment Map, wireless environment map) in Fig. 1 and Fig. 2 is a digital abstraction of the complex wireless environment, and it is the data content stored in the wireless environment data center.

As shown in Figure 2 combined with Figure 1:

An adaptive compressed spectrum sensing method, comprising:

S1: Set the initial number of rows and columns of the observation matrix and the number of rows and columns of the check matrix, set the first error tolerance threshold value and the second error tolerance threshold value, set the first step length value and the second step length value, and obtain wireless Signal;

S2: Generate an observation matrix and a check matrix by using the initial number of rows and columns of the observation matrix and the number of rows and columns of the check matrix, and use the observation matrix and the check matrix to compress and measure the wireless signal respectively to obtain the observation value and the first calibration value. test value;

S3: Perform reconstruction using the observed value to obtain an estimated value of the wireless signal;

S4: Use the check matrix to perform compression measurement on the estimated value of the wireless signal to obtain a second check value;

S5: Measure the similarity between the first check value and the second check value to obtain an evaluation basis value;

S6: Compare the evaluation basis value with the first error tolerance threshold value and the second error tolerance threshold value, if the evaluation basis value exceeds the tolerance range, compare the initial number of rows of the observation matrix in S1 Increase the value of the first step length or the second step length value, and then perform S2, if the evaluation basis value is within the tolerance range, perform spectrum sensing according to the estimated value of the wireless signal and output the sensing result;

S7: Store the output sensing result information in a data center, and update the information in the data center.

FIG. 3 is an analog frequency band channel environment power spectral density diagram of an adaptive compressed spectrum sensing method provided by an embodiment of the present invention; as shown in FIG. 3 :

A 512-point discrete frequency domain signal is used to simulate the channel environment in the frequency band of 4900MHz to 5102.4MHz. Each point in the figure represents a sub-band with a bandwidth of 200KHz. In this frequency band, there are 4 signals with a bandwidth of 6MHz, the power of each signal is -83dBm, and the signal-to-noise ratio SNR=20dB;

The embodiment of the present invention provides an adaptive compressed spectrum sensing method. Spectrum sensing is performed on the above-mentioned simulated channel environment. The observation matrix uses a Gaussian random matrix, and the reconstruction algorithm uses a sparsity adaptive matching pursuit algorithm. The specific steps of the method are as follows :

Step 1: Set the initial value M ₀ of the number of rows of the observation matrix to 150, the number of rows of the check matrix M' to 40, and the error tolerance threshold based on the width of the sensing frequency band, the requirements for sensing error and the acquired prior information of the wireless environment The values th ₀ and th ₁ are 4×10 ^-12 and 1×10 ^-11 , and the approximation step values T ₁ and T ₂ are 40 and 10;

Set the number of columns N of the observation matrix and the check matrix to 512 according to the requirements of the resolution of the sensing result;

Generate a random observation matrix Φ ₀ of M ₀ ×N, and generate a check matrix Φ' of M′×N;

receive signal x;

Step 2: Compress and measure the signal x with the observation matrix Φ _i and the check matrix Φ′ respectively to obtain y _i and y ₁ ′, where x is a wideband signal received by the sensing user for spectrum sensing. This process can be performed in the observation matrix. Or completed by the Analog-to-Information Converter (AIC) under the guidance of the check matrix;

为感知用户对x的估计值；Step 3: Use _yi to reconstruct the signal

In order to perceive the user's estimated value of x;

进行压缩测量得到y₂′；Step 4: Use the parity check matrix Φ′ to

Perform compression measurement to obtain y ₂ ′;

Step 5: Find the Euclidean distance between the vectors y ₁ ′ and y ₂ ′, ie ||y ₁ ′-y ₂ ′|| ₂ ;

The Euclidean distance value is a commonly used definition of distance, which refers to the true distance between two points in m-dimensional space, or the natural length of a vector.

The calculation of Euclidean distance is to measure the similarity between y ₁ ' and y ₂ ', and it is not limited to calculating Euclidean distance. cosine.

与真实值x偏差的统计量都能选取为感知结果的评估依据；In the step 5, ||y ₁ ′-y ₂ ′|| ₂ is selected as the evaluation basis of the perception result. In practical application, everything can describe the estimated value (with a higher probability).

The statistic of deviation from the true value x can be selected as the evaluation basis of the perception result;

Step 6: Evaluate the result of signal reconstruction and recovery by ||y ₁ ′-y ₂ ′|| ₂ : if ||y ₁ ′-y ₂ ′|| ₂ <th ₀ , it is considered that the accuracy requirement is met, and according to the wireless The estimated value of the signal is subjected to spectrum sensing and the sensing result is output; if ||y ₁ ′-y ₂ ′|| ₂ >th ₁ , it is considered that there is still a large gap between the reconstruction result and the actual value, and the number of rows in the original observation matrix is M On the basis of _i , add T ₁ , perform compression measurement and reconstruction again, and return to step 2; if th ₀ <||y ₁ ′-y ₂ ′|| ₂ <th ₁ , it is considered that the deviation between the reconstruction result and the actual value is not If it is large, increase T ₂ on the basis of the original observation matrix row number, perform compression measurement and reconstruction again, and return to step 2;

In order to improve the efficiency of the adaptive process and ensure the accuracy of the output results, when increasing the number of rows of the observation matrix, an increase process of variable step value is set up. flexible thinking.

Step 7: Perform spectrum sensing according to the estimated value of the wireless signal, output the sensing result, and feed the sensing result and related information back to the local wireless environment data center.

The sensing result information includes communication location information, the sparseness of the sensing frequency band, and the occupied idle frequency spectrum.

The information stored in the data center includes the spectral sparsity in the area, as well as the frequency, time point, duration and location range used by authorized users for communication.

The sensing device can obtain or update the information of the data center through the public network or the private network.

Public networks include public wired networks and public wireless networks, such as the Internet, cellular networks;

Private networks include wireless private networks and wired private networks.

Fig. 4 is a kind of self-adaptive compressed spectrum sensing method provided by the embodiment of the present invention The real value of the perception error and the change trend of the perceptual error evaluation basis value are compared; as shown in Fig. 4:

及感知误差评估依据值||y₁-y₂||₂随观测矩阵行数变化趋势，可以看到二者的变化趋势相同。当观测矩阵行数小于350时，感知误差评估依据值||y₁′-y₂′||₂>th₁，认为重构结果与实际值还有较大差距，在原有观测矩阵行数的基础上增加40，重新进行压缩测量与重构；当观测矩阵行数为350时，感知误差评估依据值th₀<|y₁′-y₂′||₂<th₁，认为重构结果与实际值偏差不大，在原有观测矩阵行数的基础上增加10，重新进行压缩测量与重构。当观测矩阵行数为360时，感知误差评估依据值||y₁′-y₂′||₂小于误差容忍门限值th₀，认为满足精度要求，停止自适应过程，打开开关，输出信号重构结果，进行判决，输出感知结果并将频谱稀疏信息上报当地无线环境数据中心。true value of perceptual error

And the perceptual error evaluation basis value ||y ₁ -y ₂ || ₂ changes with the number of rows of the observation matrix, and it can be seen that the two have the same change trend. When the number of rows of the observation matrix is less than 350, the perceptual error evaluation is based on the value ||y ₁ ′-y ₂ ′|| ₂ >th ₁ , and it is considered that there is still a large gap between the reconstruction result and the actual value. On the basis of adding 40, the compression measurement and reconstruction are performed again; when the number of rows of the observation matrix is 350, the perceptual error evaluation is based on the value th ₀ <|y ₁ ′-y ₂ ′|| ₂ <th ₁ , and the reconstruction result is considered to be the same as The deviation of the actual value is not large, and the number of rows in the original observation matrix is increased by 10, and the compression measurement and reconstruction are performed again. When the number of rows of the observation matrix is 360, the perceptual error evaluation basis value ||y ₁ ′-y ₂ ′|| ₂ is less than the error tolerance threshold th ₀ , it is considered that the accuracy requirement is met, the adaptive process is stopped, the switch is turned on, and a signal is output Reconstruct the result, make a judgment, output the sensing result and report the spectral sparse information to the local wireless environment data center.

The information exchange method between the sensing user and the local wireless environment data center can be: using two dedicated fixed frequency bands, the uplink can use the carrier sense multiple access communication method, and the downlink can use the broadcast communication method;

Those skilled in the art should understand that the above-mentioned information interaction method between the sensing user and the local wireless environment data center is only an example, and other existing or possible future information interaction methods between the sensing user and the local wireless environment data center are applicable if applicable The embodiments of the present invention should also be included within the protection scope of the present invention, and are incorporated herein by reference.

The prior information of the wireless environment in the area broadcasted by the local wireless environment data center, the wireless environment data center divides an open wide frequency band that it is responsible for coordination into several smaller sub-bands, and broadcasts the information of each sub-band to the outside. Occupancy (ie, sparse information), enabling sensing users to select the most vacant frequency band for communication.

In order to ensure the communication needs of authorized users as much as possible, the wireless environment data center should also broadcast the communication habits of authorized users registered in the area, such as the frequency, time and duration, The location range, etc., enables the perceptual user to avoid conflict with the authorized user as much as possible in the frequency domain, time domain, and air domain. When there is a conflict between the perceived user and the authorized user, the wireless environment data center shall broadcast the instruction to limit the bandwidth to the perceived user to ensure the communication needs of the authorized user.

The sensing user selects a relatively idle sub-band for spectrum sensing. After completing the spectrum sensing, the statistical spectrum sparse information needs to be reported to the local wireless environment data center. If the sensing user decides to use the sensed idle frequency band for communication, it is also necessary to report the frequency band used for communication, the occupied time and the location at the time of communication when the communication starts and ends.

FIG. 5 is a schematic diagram of a sensing result of an adaptive compressed spectrum sensing method according to an embodiment of the present invention.

It can be seen from the sensing results shown in FIG. 5 that the embodiment of the present invention makes full use of the prior information of the wireless environment and sets the feedback mechanism, so that the measurement parameters in the compression measurement process can be adaptively and dynamically set, so as to meet the needs of users and achieve high efficiency Compressed Spectrum Sensing;

To sum up, in order to make full use of the prior information of the wireless environment to improve the efficiency of the adaptive process, the embodiments of the present invention design an information exchange mechanism between the sensing user and the local wireless environment data center. A feedback mechanism based on the evaluation of the perception results is set up, which not only controls the accuracy of the output perception results, but also indicates the direction of the adaptive process, so that the measurement parameters match the channel environment and perception requirements. necessary overhead.

Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.

The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (9)

1. an adaptive compressed spectrum sensing method, is characterized in that, comprises: S1: Set the initial value of the number of rows and columns of the observation matrix and the initial value of the number of rows and columns of the check matrix, set the first error tolerance threshold value and the second error tolerance threshold value, set the first step length value and the second step value Long value, get wireless signal; S2: Generate an observation matrix and a check matrix by using the initial value of the number of rows and columns of the observation matrix and the initial value of the number of rows and columns of the check matrix, and use the observation matrix and the check matrix to compress and measure the wireless signal to obtain the observation matrix. value and the first check value; S3: Perform reconstruction using the observed value to obtain an estimated value of the wireless signal; S4: Use the check matrix to perform compression measurement on the estimated value of the wireless signal to obtain a second check value; S5: Measure the similarity between the first check value and the second check value to obtain an evaluation basis value; S6: Compare the evaluation basis value with the first error tolerance threshold value and the second error tolerance threshold value, if the evaluation basis value exceeds the tolerance range, compare the initial number of rows of the observation matrix in S1 Increase the value of the first step length or the second step length value, and then perform S2, if the evaluation basis value is within the tolerance range, perform spectrum sensing according to the estimated value of the wireless signal and output the sensing result; S7: store the output sensing result information in a data center, and update the data center information; In the described S5, including: The evaluation basis value is obtained by measuring the Euclidean distance or the Minkowski distance or the cosine of the included angle between the first check value and the second check value. 2. The method according to claim 1, wherein, in the S1, comprising: By receiving the information of the data center and setting the initial value of the row number of the observation matrix and the initial value of the row number of the check matrix according to the sensing requirements, the initial value of the column number of the observation matrix and the check matrix are set according to the requirements for the resolution of the sensing result. The initial value of the number of columns. 3. The method according to claim 1, wherein, in the S1, comprising: The first error tolerance threshold value, the second error tolerance threshold value, and the first step length value and the second step length value are set according to the width of the frequency band to be sensed, the spectral history sparse information and the sensing error requirement. 4. The method according to claim 1, wherein, in the S1, comprising: The wireless signal is a broadband signal received by the user performing spectrum sensing. 5. The method according to claim 1, wherein, in the S6, comprising: If the evaluation basis value is less than the first error tolerance threshold value, it is considered that the estimated value of the wireless signal meets the accuracy requirement, spectrum sensing is performed according to the estimated value of the wireless signal, and sensing result information is output. 6. The method according to claim 1, wherein, in the S6, comprising: If the evaluation basis value is greater than the second error tolerance threshold value, it is considered that the estimated value of the wireless signal has a large deviation from the actual value, and the initial value of the number of rows of the observation matrix in S1 is increased by the first Step value, and then execute S2. 7. The method according to claim 1, wherein in the S6, comprising: If the evaluation basis value is greater than the first error tolerance threshold value and less than the second error tolerance threshold value, it is considered that the deviation between the estimated value of the wireless signal and the actual value is small, and the value described in S1 The initial value of the number of rows of the observation matrix is increased by the second step value, and then S2 is performed. 8. The method of claim 5, wherein The sensing result information includes communication location information, the sparseness of the sensing frequency band, and the occupied idle frequency spectrum. 9. The method according to claim 1, wherein, in the S7, comprising: The information stored in the data center includes the spectral sparsity in the area, and includes the frequency, time point, duration and location range used by authorized users for communication.

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