A channel knowledge graph construction method for non-cooperative interference sources

Abstract

The present invention provides a channel knowledge graph construction method for a non-cooperative interference source, which belongs to the field of communications, including: measuring the received signal strength at a position according to a sampling ratio, obtaining a sampled channel spectrum, and then obtaining a base station channel spectrum estimation result, obtaining a linear power base station sampled channel spectrum and a linear power sampled channel spectrum, and then obtaining a linear power interference source channel sampling spectrum estimation result, and inputting the obtained negative power indicator matrix and the interference source channel spectrum reconstruction input into a deep learning network model to obtain a normalized interference source channel spectrum estimation result, and according to obtaining the interference source position, and then obtaining the interference source channel knowledge graph estimation result, based on and calculating the SINR of all positions, to obtain an SINR map. The present invention realizes a channel knowledge graph accurately constructed under the interference conditions of a non-cooperative interference source, and realizes accurate positioning of the interference source.

Description

Technical Field

[0001] The present invention belongs to the field of communications, and in particular relates to a method for constructing a channel knowledge graph of non-cooperative interference sources. Background Art

[0002] With the rapid development of unmanned aerial vehicle (UAV) technology, UAV-assisted communication technology, with its high flexibility, strong maneuverability, and low manufacturing cost, has effectively promoted the provision of wide-coverage, multi-functional communication services in 6G networks. However, it is worth noting that the channel environment of aerial networks differs significantly from that of terrestrial networks. Traditional transmission methods based on channel state information feedback are difficult to adapt to the complex and rapidly changing channel environment of UAV networks. Channel maps establish a mapping relationship between device location and channel state, and their effectiveness has been verified in applications such as UAV network spectrum resource management and aerial base station deployment.

[0003] Existing channel map construction methods primarily rely on accurate channel states reported by cooperative communication links. These methods rely on the actual received signal strength of the communication links sampled at different locations, and construct a complete channel map based on spatial relationships or interpolation methods. However, in real-world situations, the presence of non-cooperative interference sources can reduce measurement accuracy, leading to reduced precision in channel map construction. Therefore, constructing channel maps under interference conditions and further locating interference sources remains an open question requiring further research. Summary of the Invention

[0004] The purpose of this invention is to propose a channel knowledge graph construction method for non-cooperative interference sources, to achieve accurate construction of the channel knowledge graph under the interference conditions of the non-cooperative interference sources, and to achieve accurate positioning of the interference sources.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for constructing a channel knowledge graph of a non-cooperative interference source includes the following steps:

[0007] Step S1: According to the sampling ratio , at position Measure the received signal strength and obtain the sampling channel map ,in, , is the set of sampling locations;

[0008] Step S2: Get all locations based on environmental information Base station location , determine the communication link LoS / NLoS status , and according to Estimated location Base station received signal strength , and then get the base station channel spectrum estimation result ,in, Represents the location set of the area covered by the constructed channel knowledge graph;

[0009] Step S3: Estimation result of base station channel spectrum According to the sampling location set Sampling to obtain the base station sampling channel map , the base station sampling channel spectrum and sampling channel spectrum Converted into linear power to obtain the base station sampling channel spectrum of linear power and linear power sampling channel spectrum , and then obtain the linear power interference source channel sampling spectrum estimation result ;

[0010] Step S4: Estimation result of interference source channel sampling spectrum based on linear power Get the interference source channel sampling spectrum estimation result , negative power indicator matrix and interference source channel spectrum reconstruction input ;

[0011] Step S5: Negative power indicator matrix and interference source channel spectrum reconstruction input Input to the deep learning network model , after interpolation, the normalized interference source channel spectrum estimation result is obtained ;

[0012] Step S6: Detect the normalized interference source channel spectrum estimation result based on the peak detection algorithm The peak position in the interference source is obtained ;

[0013] Step S7: Estimation result based on interference source channel sampling spectrum And the normalized interference source channel spectrum estimation results , estimate the maximum and minimum values ​​of the interference source channel knowledge graph, and obtain the interference source channel knowledge graph estimation result ;

[0014] Step S8: Estimation results based on base station channel spectrum And interference source channel knowledge graph estimation results Calculate the SINR at all locations and get the SINR map .

[0015] Furthermore, in step S1, the position Represents the three-dimensional position coordinates in a square area, where the length, width and height are , at position The received signal strength measured at , by measuring All locations within , while the position Corresponding Set to 0 to get the sampling channel spectrum ,in, is the base station’s transmit power, It is n The transmit power of an interference source, N is the number of interference sources, Indicates the n The location of the interference source, Indicates the location of the base station The transmitter to the location The channel gain, the logarithmic calculation expression is , represent the path loss exponent and path loss intercept respectively, is the indicator variable of direct link and indirect link, represents the channel shadow fading due to unpredictable scatterers, Indicates the channel fading caused by multipath transmission.

[0016] Furthermore, in step S2, the base station receives a signal strength Expressed as , All locations within Forming the base station channel spectrum estimation result ,in, , It represents the estimated result of the base station received signal power at the location, They are estimated value.

[0017] Furthermore, the step S3 specifically includes the following steps:

[0018] Step S31: Base station channel spectrum estimation result According to the sampling location set Sampling to obtain the base station sampling channel map , Middle position The element value at The sampling results of the corresponding position in the The element value at is set to 0;

[0019] Step S32: According to the formula Base station sampling channel spectrum Linearization to obtain the linear power base station sampling channel spectrum , according to the formula Sampling channel spectrum Convert to linear power and get the sampling channel spectrum of linear power , , , where linear power conversion is only for position The corresponding received signal power is carried out, and the position The corresponding sampling result is set to 0. Represents the linear power conversion calculation process;

[0020] Step S33: According to the formula Calculate the received signal power of the interference source by calculating All locations within , while the position Corresponding Set to 0 to obtain the linear power interference source channel sampling spectrum estimation result ,in, For location The actual received signal power sampling result is For location The base station received signal power estimation result.

[0021] Furthermore, the step S4 specifically includes the following steps:

[0022] Step S41: Estimation result of linear power interference source channel sampling spectrum Logarithmization is performed to obtain the interference source channel sampling spectrum estimation result and negative power indicator matrix , where the linear power interference source channel sampling spectrum estimation result is Negative numbers and 0 values ​​in the interference source channel are not logarithmized. The corresponding position is 0, , negative power indicator matrix , corresponding to the set of sampling locations The elements in the positions other than 0 are set, and the other elements correspond to The position of is 0, corresponding to The position of is 1;

[0023] Step S42: Estimation result of interference source channel sampling spectrum The non-zero values ​​in are normalized to the maximum and minimum values ​​to obtain the interference source channel spectrum reconstruction input .

[0024] Furthermore, in step S5, the deep learning network model It is a convolutional neural network.

[0025] Furthermore, in step S6, the peak detection algorithm is a two-bit constant false alarm probability detection algorithm.

[0026] Furthermore, the step S7 specifically includes the following steps:

[0027] Step S71: Vectorized interference source channel sampling spectrum estimation result The non-zero value in is represented by ;

[0028] Step S72: normalize the interference source channel spectrum estimation result The estimation result of the interference source channel sampling spectrum is The elements of the non-zero position in the vectorization are expressed as ;

[0029] Step S73: Construct the maximum value of the interference source channel knowledge graph and minimum value The estimation optimization problem is expressed as , solving the estimation optimization problem yields ,in, Represents a vector The module length, ;

[0030] Step S74: Denormalize the normalized interference source channel spectrum estimation result , get the interference source channel knowledge graph estimation result , the specific calculation expression is ,in, for In position The element value of For Location The element value of .

[0031] Furthermore, in step S8, the position The SINR is expressed as , is the noise power.

[0032] The present invention has the following beneficial effects:

[0033] 1. The present invention first obtains the sampling channel spectrum and base station channel spectrum estimation results , and then the sampling channel maps are and base station channel spectrum estimation results Linearization to obtain the sampling channel spectrum of linear power Base station sampling channel spectrum with linear power , and then obtain the linear power interference source channel sampling spectrum estimation result and will Get the negative power indicator matrix and interference source channel spectrum reconstruction input Input deep learning network model , after interpolation, the normalized interference source channel spectrum estimation result is obtained , and then detect the normalized interference source channel spectrum estimation result based on the peak detection algorithm The peak position in the interference source is obtained , and based on the interference source channel sampling spectrum estimation results And the normalized interference source channel spectrum estimation results , estimate the maximum and minimum values ​​of the interference source channel knowledge graph, and obtain the interference source channel knowledge graph estimation result , and get the SINR map . This addresses the pain point of being unable to accurately measure the received signal power in the presence of non-cooperative interference sources. It enables the precise construction of a channel knowledge graph under interference conditions where non-cooperative interference sources exist, accurately locates the interference sources, accurately estimates the interference power, and supports the construction of a SINR map. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described in detail below with reference to the accompanying drawings.

[0035] Figure 1 Flowchart of the present invention.

[0036] Figure 2 are the network parameters of the deep learning network model of the present invention.

[0037] Figure 3 A simulation diagram showing how the error in constructing the knowledge graph of the present invention changes with the sampling ratio.

[0038] Figure 4 This is a simulation diagram of the interference source positioning error changing with the sampling ratio of the present invention.

[0039] Figure 5 This is a simulation diagram of the SINR map construction error changing with the sampling ratio of the present invention. DETAILED DESCRIPTION

[0040] This embodiment considers a downlink communication system model in an urban environment where a UAV is the receiving end, and there are non-cooperative interference nodes at unknown locations that interfere with UAV communications. Figure 1 As shown, the channel knowledge graph construction method of non-cooperative interference sources is characterized by comprising the following steps:

[0041] Step S1: According to the sampling ratio , at position Measure the received signal strength and obtain the sampling channel map ,in, , is the set of sampling locations;

[0042] Specifically, the location Represents the three-dimensional position coordinates in a square area, where the length, width and height are , at position The received signal strength measured at , by measuring All locations within , while the position Corresponding Set to 0 to get the sampling channel spectrum ,in, represents the location set of the area covered by the constructed channel knowledge graph, represents the channel spectrum coverage area at a fixed height, where is the length of the region, is the width of the region, is the base station’s transmit power, It is n The transmit power of an interference source, N is the number of interference sources, Indicates the n The location of the interference source, Indicates the location of the base station The transmitter to the location The channel gain, the logarithmic calculation expression is , represent the path loss exponent and path loss intercept respectively, Indicates the variables for Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS), k =1 represents LoS, k =0 represents NloS, It represents the channel shadow fading caused by unpredictable scatterers, and the variance is The standard Gaussian process is used to characterize Indicates the channel fading caused by multipath transmission, and the variance is is characterized by a standard Gaussian process.

[0043] In this embodiment, the base station transmit power ; Number of interference sources , the interference source transmission power ; The interference source is located at The channel parameters are defined as: , ; ; .

[0044] Step S2: Get all locations based on environmental information Base station location , determine the communication link LoS / NLoS status , and according to Estimated location Base station received signal strength , and then get the base station channel spectrum estimation result ,in, Represents the location set of the area covered by the constructed channel knowledge graph;

[0045] Base station received signal strength Expressed as , by estimating All locations within , the base station channel spectrum estimation result can be obtained ,in, , It represents the estimated result of the base station received signal power at the location, They are estimated value.

[0046] In this embodiment, the environmental information is considered to be an urban environment, where buildings are modeled as squares and the building distribution is determined by the parameters Decide, They are specifically defined as: a Indicates the proportion of area covered by buildings, b Indicates the number of buildings per square kilometer, λ The Rayleigh distribution parameter representing the building height is .

[0047] Step S3: Estimation result of base station channel spectrum According to the sampling location set Sampling to obtain the base station sampling channel map , the base station sampling channel spectrum and sampling channel spectrum Converted into linear power to obtain the base station sampling channel spectrum of linear power and linear power sampling channel spectrum , and then obtain the linear power interference source channel sampling spectrum estimation result , specifically including the following steps:

[0048] Step S31: Base station channel spectrum estimation result According to the sampling location set Sampling to obtain the base station sampling channel map , Middle position The element value at The sampling results of the corresponding position in the The element value at is set to 0;

[0049] Step S32: According to the formula Base station sampling channel spectrum Convert to linear to get the base station sampling channel spectrum of linear power , according to the formula Sampling channel spectrum Convert to linear power and get the sampling channel spectrum of linear power , , , where linear power conversion is only for position The corresponding received signal power is carried out, and the position The corresponding sampling result is set to 0. Represents the linear power conversion calculation process;

[0050] Step S33: According to the formula Calculate the received signal power of the interference source by calculating All locations within , while the position Corresponding Set to 0 to obtain the linear power interference source channel sampling spectrum estimation result ,in, For location The actual received signal power sampling result is For location The base station received signal power estimation result.

[0051] Step S31: Base station channel spectrum estimation result According to the sampling location set Sampling to obtain the base station sampling channel map ,exist Middle, location The corresponding sampling results are The corresponding element value, position The corresponding sampling result is set to 0;

[0052] Step S32: According to the formula Base station sampling channel spectrum Convert to linear to get the base station sampling channel spectrum of linear power , according to the formula Sampling channel spectrum Convert to linear power and get the sampling channel spectrum of linear power , , , where linear power conversion is only for position The corresponding received signal power is carried out, and the position The corresponding sampling result is set to 0. Represents the linear power conversion calculation process;

[0053] Step S33: According to the formula Calculate the received signal power of the interference source by calculating All locations within , while the position Corresponding Set to 0 to obtain the linear power interference source channel sampling spectrum estimation result ,in, For location The actual received signal power sampling result is For location The base station receives the signal power.

[0054] Step S4: Estimation result of interference source channel sampling spectrum based on linear power Get the interference source channel sampling spectrum estimation result , negative power indicator matrix and interference source channel spectrum reconstruction input , specifically including the following steps:

[0055] Step S41: Estimation result of linear power interference source channel sampling spectrum Logarithmization is performed to obtain the interference source channel sampling spectrum estimation result and negative power indicator matrix , where the linear power interference source channel sampling spectrum estimation result is Negative numbers and 0 values ​​in the interference source channel are not logarithmized. The elements at the corresponding positions are set to 0, and the negative power indicator matrix , corresponding to the set of sampling locations The elements in the positions other than 0 are set, and the other elements correspond to The position of is 0, corresponding to The position of is 1;

[0056] Step S42: Estimation result of interference source channel sampling spectrum The non-zero values ​​in are normalized to the maximum and minimum values ​​to obtain the interference source channel spectrum reconstruction input ;

[0057] The specific calculation expression is: ,in, Represents the interference source channel sampling spectrum estimation result The set of 0 elements in .

[0058] Step S5: Negative power indicator matrix and interference source channel spectrum reconstruction input Input to the deep learning network model , after interpolation, the normalized interference source channel spectrum estimation result is obtained ;

[0059] Specifically, the deep learning network model It is a convolutional neural network, and its network parameters are as follows Figure 2 shown.

[0060] Step S6: Detect the normalized interference source channel spectrum estimation result based on the peak detection algorithm The peak position in the interference source is obtained ;

[0061] Specifically, the peak detection algorithm is a known two-bit constant false alarm probability detection algorithm.

[0062] Step S7: Estimation result based on interference source channel sampling spectrum And the normalized interference source channel spectrum estimation results , estimate the maximum and minimum values ​​of the interference source channel knowledge graph, and obtain the interference source channel knowledge graph estimation result ;

[0063] The specific steps include:

[0064] Step S71: Vectorized interference source channel sampling spectrum estimation result The non-zero value in is represented by ;

[0065] Step S72: normalize the interference source channel spectrum estimation result The estimation result of the interference source channel sampling spectrum is The elements of the non-zero position in the vectorization are expressed as ;

[0066] Step S73: Construct the maximum value of the interference source channel knowledge graph and minimum value The estimation optimization problem is expressed as , solving the estimation optimization problem yields ,in, Represents a vector The module length, ;

[0067] Step S74: Denormalize the normalized interference source channel spectrum estimation result , get the interference source channel knowledge graph estimation result , the specific calculation expression is ,in, for In position The element value of For Location The element value of .

[0068] Step S8: Estimation results based on base station channel spectrum And interference source channel knowledge graph estimation results Calculate the SINR (Signal-to-Interference-Rate) at all locations and obtain the SINR map ;

[0069] Among them, the location The SINR is expressed as , is the noise power.

[0070] Figure 3 The horizontal axis is the sampling ratio, and the vertical axis is the estimation result of the interference source channel knowledge graph constructed The normalized mean square error with the true value shows that the error of the present invention is smaller than that of the Kriging method (Krige) and the inverse distance weighted method (IDW);

[0071] Figure 4 The horizontal axis is the sampling ratio, and the vertical axis is the estimated interference source position. The normalized mean square error with the true value shows that the error of the present invention is smaller than that of the Kriging method (Krige) and the inverse distance weighted method (IDW);

[0072] Figure 5The horizontal axis is the sampling ratio, and the vertical axis is the normalized mean square error between the reconstructed SINR map and the true value. It can be seen that compared with the Kriging method (Krige) and the inverse distance weighted method (IDW), the error of the present invention is smaller.

[0073] The above description is merely a preferred embodiment of the present invention and therefore cannot be used to limit the scope of the present invention. In other words, equivalent changes and modifications made according to the scope of the patent application and the contents of the specification should still fall within the scope of the patent of the present invention.

Claims

1. A method for constructing a channel knowledge graph for non-cooperative interference sources, characterized by: The steps include: Step S1: According to the sampling ratio , at position Measure the received signal strength and obtain the sampling channel map ,in, , is the set of sampling locations; Step S2: Get all locations based on environmental information Base station location , determine the communication link LoS / NLoS status , and according to Estimated location Base station received signal strength , and then get the base station channel spectrum estimation result ,in, Represents the location set of the area covered by the constructed channel knowledge graph; Step S3: Estimation result of base station channel spectrum According to the sampling location set Sampling to obtain the base station sampling channel map , the base station sampling channel spectrum and sampling channel spectrum Converted into linear power to obtain the base station sampling channel spectrum of linear power and linear power sampling channel spectrum , and then obtain the linear power interference source channel sampling spectrum estimation result ; Step S4: Estimation result of interference source channel sampling spectrum based on linear power Get the interference source channel sampling spectrum estimation result , negative power indicator matrix and interference source channel spectrum reconstruction input ; Step S5: Negative power indicator matrix and interference source channel spectrum reconstruction input Input to the deep learning network model , after interpolation, the normalized interference source channel spectrum estimation result is obtained ; Step S6: Detect the normalized interference source channel spectrum estimation result based on the peak detection algorithm The peak position in the interference source is obtained ; Step S7: Estimation result based on interference source channel sampling spectrum And the normalized interference source channel spectrum estimation results , estimate the maximum and minimum values ​​of the interference source channel knowledge graph, and obtain the interference source channel knowledge graph estimation result ; Step S8: Estimation results based on base station channel spectrum And interference source channel knowledge graph estimation results Calculate the SINR at all locations and get the SINR map ; The step S4 specifically includes the following steps: Step S41: Estimation result of linear power interference source channel sampling spectrum Logarithmization is performed to obtain the interference source channel sampling spectrum estimation result and negative power indicator matrix , where the linear power interference source channel sampling spectrum estimation result is Negative numbers and 0 values ​​in the interference source channel are not logarithmized. The corresponding position is 0, , negative power indicator matrix , corresponding to the set of sampling locations The elements in the positions other than 0 are set, and the other elements correspond to The position of is 0, corresponding to The position of is 1; Step S42: Estimation result of interference source channel sampling spectrum The non-zero values ​​in are normalized to the maximum and minimum values ​​to obtain the interference source channel spectrum reconstruction input .

2. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 1, characterized in that: In the step S1, the position Represents the three-dimensional position coordinates in a square area, where the length, width and height are , at position The received signal strength measured at , by measuring All locations within , while the position Corresponding Set to 0 to get the sampling channel spectrum ,in, is the base station’s transmit power, It is n The transmit power of an interference source, N is the number of interference sources, Indicates the n The location of the interference source, Indicates the location of the base station The transmitter to the location The channel gain, the logarithmic calculation expression is , represent the path loss exponent and path loss intercept respectively, is the indicator variable of direct link and indirect link, represents the channel shadow fading due to unpredictable scatterers, Indicates the channel fading caused by multipath transmission.

3. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 2, characterized in that: In step S2, the base station receives the signal strength Expressed as , All locations within Forming the base station channel spectrum estimation result ,in, , It represents the estimated result of the base station received signal power at the location, They are estimated value.

4. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 3, characterized in that: The step S3 specifically includes the following steps: Step S31: Base station channel spectrum estimation result According to the sampling location set Sampling to obtain the base station sampling channel map , Middle position The element value at The sampling results of the corresponding position in the The element value at is set to 0; Step S32: According to the formula Base station sampling channel spectrum Linearization to obtain the linear power base station sampling channel spectrum , according to the formula Sampling channel spectrum Convert to linear power and get the sampling channel spectrum of linear power , , , where linear power conversion is only for position The corresponding received signal power is carried out, and the position The corresponding sampling result is set to 0. Represents the linear power conversion calculation process; Step S33: According to the formula Calculate the received signal power of the interference source by calculating All locations within , while the position Corresponding Set to 0 to obtain the linear power interference source channel sampling spectrum estimation result ,in, For location The actual received signal power sampling result is For location The base station received signal power estimation result.

5. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 4, characterized in that: In step S5, the deep learning network model It is a convolutional neural network.

6. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 5, characterized in that: In step S6, the peak detection algorithm is a two-bit constant false alarm probability detection algorithm.

7. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 6, characterized in that: The step S7 specifically includes the following steps: Step S71: Vectorized interference source channel sampling spectrum estimation result The non-zero value in is represented by ; Step S72: normalize the interference source channel spectrum estimation result The estimation result of the interference source channel sampling spectrum is The elements of the non-zero position in the vectorization are expressed as ; Step S73: Construct the maximum value of the interference source channel knowledge graph and minimum value The estimation optimization problem is expressed as , solving the estimation optimization problem yields ,in, Represents a vector The module length, ; Step S74: Denormalize the normalized interference source channel spectrum estimation result , get the interference source channel knowledge graph estimation result , the specific calculation expression is ,in, for In position The element value of For Location The element value of .

8. The method for constructing a channel knowledge graph of a non-cooperative interference source according to claim 7, characterized in that: In step S8, the position The SINR is expressed as , is the noise power.

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