Tunnel edge computing terminal layout method based on channel map construction

Abstract

The invention relates to the technical field of wireless information transmission, and discloses a tunnel edge computing terminal layout method based on channel map construction. Comprising the steps of constructing a channel impulse response model, generating a self-adaptive dynamic noise threshold value, extracting an effective multipath component, calculating sparse channel data, mapping the channel data, complementing a channel data matrix, displaying a channel map, optimizing layout and the like. According to the tunnel edge computing terminal layout method based on channel map construction, a channel model is constructed based on electromagnetic wave transmission characteristics of a tunnel scene, and a complete and accurate three-dimensional channel map of the tunnel scene is constructed through accurate and real-time extraction of various sparse channel data in the tunnel; according to the method, channel data such as propagation path information, receiving power, path loss, root-mean-square delay extension and channel capacity are obtained, effective layout optimization of a tunnel edge computing terminal is achieved, effective extraction of various channel data of a tunnel scene is achieved, and complete construction of a channel map of a tunnel three-dimensional scene is achieved.

Description

Technical Field

[0001] The present invention relates to the field of wireless information transmission technology, and in particular to a tunnel edge computing terminal layout method based on channel map construction. Background Art

[0002] Highway tunnels are important transportation facilities that connect both sides of geographical barriers. They play an important role in ensuring smooth and convenient traffic and promoting regional economic development. With the completion and opening of special sections such as extra-long tunnels and tunnel groups, tunnel construction, management and maintenance are facing many new problems. The rapid development of emerging technologies such as the Internet of Things, new generation mobile communications, and edge computing will help promote the intelligent and unmanned development of tunnel construction, management and maintenance, which is of great significance for further reducing costs and increasing efficiency, and improving the safety and stability of tunnel operation and management.

[0003] It is difficult to achieve effective and reliable data sharing and linkage between various devices such as data acquisition sensors and control systems with a large number of complex layouts in the tunnel. The redundant data resources are also very complicated for subsequent analysis and processing. The tunnel edge computing terminal is compatible with various transmission protocols and standards, and can effectively and reliably connect various devices in the tunnel and collect, process and analyze data in real time. Some tunnel equipment uses wireless communication technologies such as 4G or 5G for data transmission, which has the advantages of more flexibility, convenience and low cost. However, due to the special closed structure of the tunnel, the transmission characteristics of electromagnetic waves are quite different from those of traditional ground. How to effectively layout edge computing terminals and achieve reliable and stable access to various wireless transmission devices in the tunnel has become a key issue that needs to be solved urgently.

[0004] Traditional edge computing terminal layout methods usually use ultra-low latency transmission of devices as the main constraint to solve the optimal layout quantity and location. Although this can meet the needs of edge computing terminal layout optimization to a certain extent and improve the operation and management efficiency of various devices, due to the special wireless communication environment of the tunnel, this has a significant impact on the reliable and stable connection of wireless transmission equipment. Summary of the invention

[0005] To solve the above problems, the invention proposes a tunnel edge computing terminal layout method based on channel map construction; this method constructs a channel model based on the electromagnetic wave transmission characteristics of the tunnel scene, and constructs a complete and accurate three-dimensional channel map of the tunnel scene through accurate and real-time extraction of various sparse channel data in the tunnel, thereby effectively and conveniently realizing the layout optimization of tunnel edge computing terminals.

[0006] The technical solution adopted by the present invention is: A tunnel edge computing terminal layout method based on a channel map comprises the following steps: Step 1: Construct a channel impulse response model: Based on the wireless transmission characteristics of electromagnetic waves in the tunnel, the user constructs a channel impulse response model in the tunnel scenario. ; The influence of direct and indirect paths is described by channel impulse response model; Step 2, generating an adaptive dynamic noise threshold: the user generates an adaptive dynamic noise threshold according to the channel impulse response of the wireless channel in the tunnel scenario; Step 3: Extracting effective multipath components: Based on adaptive dynamic noise threshold , extracting effective multipath components from the channel impulse response of the tunnel wireless channel; Step 4: Calculate sparse channel data: The user is based on the effective multipath component in the channel impulse response Calculate sparse channel data in tunnel scenarios; Step 5, channel data mapping: the user maps the acquired sparse channel data in the tunnel scenario to the channel map model; Step 6: Complete the channel data matrix: The user completes the sparse channel data matrix in the tunnel scenario. Complete the channel map in the tunnel scenario. Step 7, channel map display and layout optimization: Based on the constructed channel map, intuitively display the electromagnetic wave transmission characteristics in the tunnel scenario, including propagation path power, propagation path delay, received power, path loss, root mean square delay spread and channel capacity; use the channel map to optimize the layout of the tunnel edge computing terminal.

[0007] Furthermore, in step 1, the channel impulse response model Specifically, it is the superposition of the direct path component of electromagnetic wave propagation in the tunnel scene and the indirect path component reflected by the tunnel top, ground and side walls; As shown below:

[0008] In the formula, Representatives from The wireless transmission device in the tunnel is The channel impulse response of a tunnel edge computing terminal depends on time t and delay ;in, and Respectively represent The wireless transmission equipment in the tunnel and the Tunnel edge computing terminals, , ; represents the power gain of the direct path; represents the channel fading coefficient of the direct path; and is the impulse function, represents the delay of the direct path, Indicates The delay of the indirect path; represents the number of indirect paths in the wireless channel of the tunnel scenario, ; Indicates The power gain of the indirect path; Indicates The channel fading coefficient of the indirect path.

[0009] Further, in step 2, the adaptive dynamic noise threshold generation process includes the following steps: Step 2.1, set parameters: the user sets the sliding window size to , the number of protected cells is , the false alarm probability is ; False alarm probability , and its calculation formula is as follows: ; In the formula, is a counting variable, which ranges from 0 to change; represents the threshold factor; Step 2.2, Calculate power estimates: Calculate power estimates for lagged cells , and calculate the power estimate of the look-ahead cell ; The calculation formula is as follows: ; In the formula, The index representing the time or space position currently being processed, where ; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; Step 2.3, Calculate background noise power: Calculate an estimate of the background noise power ; The calculation formula is as follows: ; In the formula, Indicates finding the minimum value; Step 2.4, calculate the threshold factor: based on the false alarm probability The expression for further calculating the threshold factor ; The calculation formula is as follows: ; Step 2.5, calculate the adaptive dynamic noise threshold: Calculate the adaptive dynamic noise threshold ; The calculation formula is as follows: .

[0010] Further, in step 3, the effective multipath component extraction process in the channel impulse response includes the following steps: Step 3.1, obtaining peak data in the channel impulse response: obtaining peak data in the channel impulse response according to the following formula; ; In the formula, Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; A variable representing the peak index, with a value range of: ; Represents the number of peaks in the channel impulse response; represents the first peak value; Step 3.2, determine and extract the effective multipath component: According to the following formula, the user is based on the adaptive dynamic noise threshold Determine the peak values ​​of the channel impulse response in the tunnel scene and extract the effective multipath components ; ; In the formula, Indicates the first The path amplitude of the effective propagation path; Indicates the first The delay of an effective propagation path; Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; represents the adaptive dynamic noise threshold; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0011] Furthermore, in step 3, the peak value must satisfy the following conditions: ; In the formula, represents the first The previous sampling point of the peak value; represents the first The next sampling point after the peak value.

[0012] Further, in step 4, the sparse channel data calculation in the tunnel scenario includes the following steps: Step 4.1, calculation of path power of effective propagation path: Calculate the path power of the tunnel scenario according to the following formula: Path power of effective propagation paths ; ; In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; It is The path delay The channel impulse response under ; Step 4.2, signal receiving power calculation: According to the following formula, sum the effective propagation path power at each delay and calculate the signal receiving power at the tunnel edge computing terminal ; ; In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; It is The path delay The channel impulse response under ; Step 4.3, path loss calculation: Calculate the path loss in the tunnel scenario according to the following formula: ; ; In the formula, Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The power of each path; Step 4.4, RMS delay spread calculation: Calculate the RMS delay spread in the tunnel scenario according to the following formula: ; ; In the formula, Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The delay of each path; represents the power-weighted average delay in the channel impulse response; Step 4.5, channel capacity calculation: Calculate the channel capacity in the tunnel scenario according to the following formula: ; ; In the formula, It means to find the determinant operation; represents the identity matrix; represents the signal-to-noise ratio; represents the conjugate transpose operation; represents the channel matrix of the constructed tunnel scenario channel model, ; Step 4.6, define the sparse channel dataset: user-defined sparse channel dataset in tunnel scenario , sparse channel dataset in tunnel scenario The specific expression is as follows: ; In the formula, Indicates The power of each path; Indicates The delay of each path; Calculate the signal received power at the terminal for the tunnel edge; is the path loss in the tunnel scenario; is the RMS delay spread in the tunnel scenario; is the channel capacity in the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0013] Furthermore, in step 4, the power-weighted average delay in the channel impulse response is The calculation process is as follows: .

[0014] Further, in step 5, the mapping process of sparse channel data in the tunnel scenario includes the following steps: Step 5.1, divide the 3D cube area: the user divides the tunnel 3D scene into indivual A three-dimensional cube area composed of sub-cubes; assuming that the channel data value collected by each sub-cube is the channel data value in the entire sub-cube area; Step 5.2, construct the channel data matrix: construct a three-dimensional channel map model of the tunnel scene, specifically represented as the channel data matrix between any two sub-cubes in the three-dimensional area of ​​the tunnel , where the matrix elements As shown below: ; In the formula, Indicates the first indivual subcube and indivual subcube When the sparse channel data between When , the two sub-cubes overlap, and there is no channel data; Step 5.3, Mapping sparse channel data: Map the sparse channel data collected in the tunnel 3D cube area to the channel data matrix On the top, obtain the sparse channel data matrix in the tunnel 3D scene ; Step 5.4, define channel dataset: user defines channel dataset in tunnel channel map model , the specific expression is as follows: ; In the formula, Indicates the first The subcube and Channel data sets between sub-cubes; Indicates The subcube and The impulse response of the channel between the sub-cubes Path power in the effective propagation paths; Indicates The subcube and The impulse response of the channel between the sub-cubes The path delay in the effective propagation paths; Indicates The subcube and The received power between sub-cubes; Indicates The subcube and Path loss between sub-cubes; Indicates The subcube and The RMS delay spread between subcubes; Indicates The subcube and The channel capacity between sub-cubes; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0015] Further, in step 6, the tunnel sparse channel data matrix completion process includes the following steps: Step 6.1, estimate the channel data in the uncollected sub-cube: the channel data in the uncollected sub-cube in the tunnel 3D cube area It is specifically expressed as the weighted average of the channel data in all the acquisition sub-cubes in the three-dimensional cube area, which is specifically expressed as follows: ; In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents weight; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the number of acquisition sub-cubes in the tunnel scene; Step 6.2, calculate the Euclidean distance between each acquisition sub-cube: The user calculates the Euclidean distance between each acquisition sub-cube in the tunnel scene. The calculation formula is as follows: ; In the formula, Indicates the tunnel scenario. The collection sub-cube and The Euclidean distance between the collected sub-cubes; indivual The position coordinates of the collection sub-cube are ;No. indivual The position coordinates of the collection sub-cube are ; Step 6.3, calculate semi-variance: According to the following formula, the user calculates the semi-variance between each acquisition sub-cube; ; In the formula, Indicates the tunnel scenario. The collection sub-cube and The semi-variance between the collected sub-cubes; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the first indivual Collect sub-cubes The true value of the channel data; Step 6.4, fitting the semivariance function: the user fits the semivariance function based on the Euclidean distance and semivariance between each acquisition sub-cube in the tunnel scene ; As shown below: ; In the formula, represents the Euclidean distance between any two sub-cubes in the tunnel scene; Step 6.5, calculate the semivariance between the uncollected sub-cube and the collected sub-cube: the user calculates the semivariance function according to the fitted tunnel scene Calculate uncollected subcubes Semivariance with all collected subcubes ;in, Indicates the tunnel scenario. indivual Collect sub-cubes; Step 6.6, calculate the optimal weight: The user calculates the optimal weight according to the following formula : ; In the formula, represents the Lagrange multiplier; Step 6.7, calculate the estimated value of the channel data: Calculate the estimated value of the channel data in the uncollected sub-cube in the tunnel three-dimensional cube area according to the following formula: ; ; In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents the optimal weight; Indicates the first indivual Collect sub-cubes The true value of the channel data.

[0016] The beneficial effects of the present invention are: 1. The tunnel edge computing terminal layout method based on channel map construction can accurately and completely construct the channel map of the three-dimensional electromagnetic scene of the tunnel, obtain channel data such as propagation path information, received power, path loss, root mean square delay spread and channel capacity, and realize the effective layout optimization of tunnel edge computing terminals.

[0017] 2. The adaptive dynamic noise threshold generation process of the tunnel edge computing terminal layout method based on the channel map can generate an adaptive dynamic noise threshold for the channel impulse response of the wireless channel in the tunnel scene, thereby realizing the effective extraction of multiple channel data in the tunnel scene.

[0018] 3. The process of completing the sparse channel data of the tunnel scene in the tunnel edge computing terminal layout method based on the channel map can accurately complete the sparse channel data and realize the complete construction of the three-dimensional tunnel scene channel map. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of a tunnel edge computing terminal layout method based on a channel map; Figure 2 is a flow chart of the process of generating an adaptive noise threshold in the present invention; Figure 3 It is a flow chart of the sparse channel data completion process in the tunnel scenario of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the accompanying drawings of the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] This embodiment provides a tunnel edge computing terminal layout method based on channel map construction, such as Figure 1 As shown, the tunnel edge computing terminal layout method based on the channel map includes the following steps: Step 1: Construct a channel impulse response model: Based on the wireless transmission characteristics of electromagnetic waves in tunnels, users build a channel impulse response model in tunnel scenarios ; The influence of direct path and indirect path is described by channel impulse response model.

[0022] Channel impulse response model Specifically, it is the superposition of the direct path component of electromagnetic wave propagation in the tunnel scene and the indirect path component reflected by the tunnel top, ground and side walls, as shown in the following formula: ; In the formula, Representatives from The wireless transmission device in the tunnel is The channel impulse response of a tunnel edge computing terminal depends on time t and delay ;in, and Respectively represent The wireless transmission equipment in the tunnel and the Tunnel edge computing terminals, , ; represents the power gain of the direct path; represents the channel fading coefficient of the direct path; and is the impulse function, represents the delay of the direct path, Indicates The delay of the indirect path; represents the number of indirect paths in the wireless channel of the tunnel scenario, ; Indicates The power gain of the indirect path; Indicates The channel fading coefficient of the indirect path.

[0023] The channel impulse response model combines the influence of the direct path and multiple indirect paths in the tunnel environment, and describes the propagation characteristics of electromagnetic waves by using power gain and channel fading coefficient. The channel impulse response model can more accurately reflect the propagation of wireless signals in the tunnel, providing a basis for further communication system design and performance analysis.

[0024] Step 2, adaptive dynamic noise threshold generation: like Figure 2 As shown, the user generates an adaptive dynamic noise threshold based on the channel impulse response of the wireless channel in the tunnel scenario.

[0025] The adaptive dynamic noise threshold generation process includes the following steps: Step 2.1, set parameters: The user sets the sliding window size to , the number of protected cells is , the false alarm probability is .

[0026] False alarm probability , and its calculation formula is as follows: ; In the formula, is a counting variable, which ranges from 0 to change; Represents the threshold factor.

[0027] Step 2.2, calculate the power estimate: Calculate power estimates for lagged cells , and calculate the power estimate of the look-ahead cell ; The calculation formula is as follows:

[0028] In the formula, The index representing the time or space position currently being processed, where ; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay .

[0029] Step 2.3, calculate the background noise power: Calculate an estimate of the background noise power ; The calculation formula is as follows:

[0030] In the formula, Indicates finding the minimum value; Step 2.4, calculate the threshold factor: Based on the false alarm probability The expression for further calculating the threshold factor ; The calculation formula is as follows: ; Step 2.5, calculate the adaptive dynamic noise threshold: Calculate adaptive dynamic noise threshold ; The calculation formula is as follows:

[0031] The above steps describe in detail how to generate an adaptive dynamic noise threshold that can be used for noise management in a wireless channel.

[0032] Step 3, extract the effective multipath components: Based on adaptive dynamic noise threshold , extract the effective multipath components in the channel impulse response of the tunnel wireless channel.

[0033] The effective multipath component extraction process in the channel impulse response includes the following steps: Step 3.1, obtain the peak data in the channel impulse response: The peak data in the channel impulse response is obtained according to the following formula; ; In the formula, Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; A variable representing the peak index, with a value range of: ; Represents the number of peaks in the channel impulse response; represents the first A peak value.

[0034] Furthermore, the peak value must satisfy the following conditions:

[0035] In the formula, represents the first The previous sampling point of the peak value; represents the first The next sampling point after the peak value; Step 3.2, determine and extract the effective multipath components: According to the following formula, the user is based on the adaptive dynamic noise threshold Determine the peak values ​​of the channel impulse response in the tunnel scene and extract the effective multipath components ;

[0036] In the formula, Indicates the first The path amplitude of the effective propagation path; Indicates the first The delay of an effective propagation path; Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; represents the adaptive dynamic noise threshold; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0037] Through the above steps, the effective multipath components in the channel impulse response of the tunnel wireless channel can be effectively extracted, providing basic data for the subsequent optimization and analysis of the wireless communication system.

[0038] Step 4, calculate sparse channel data: The effective multipath components of the user based on the channel impulse response Compute sparse channel data in tunnel scenarios.

[0039] The sparse channel data calculation in the tunnel scenario includes the following steps: Step 4.1, path power calculation of effective propagation path: According to the following formula, calculate the first Path power of effective propagation paths ; ; In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; It is The path delay The channel impulse response under .

[0040] Step 4.2, signal receiving power calculation: According to the following formula, sum the effective propagation path power at each delay and calculate the signal receiving power at the tunnel edge computing terminal ; ; In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; It is The path delay The channel impulse response under .

[0041] Step 4.3, path loss calculation: Calculate the path loss in the tunnel scenario according to the following formula: ; ; In the formula, Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The power of the path.

[0042] Step 4.4, RMS delay spread calculation: According to the following formula, the RMS delay spread in the tunnel scenario is calculated: ; ; In the formula, Indicates the number of effective propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The delay of each path; It represents the power-weighted average delay in the channel impulse response.

[0043] Furthermore, the power-weighted average delay in the channel impulse response is The calculation process is as follows:

[0044] Step 4.5, channel capacity calculation: According to the following formula, the channel capacity in the tunnel scenario is calculated ; ; In the formula, It means to find the determinant operation; represents the identity matrix; represents the signal-to-noise ratio; represents the conjugate transpose operation; represents the channel matrix of the constructed tunnel scenario channel model, .

[0045] Step 4.6, define the sparse channel dataset: Sparse channel dataset in user-defined tunnel scenario , sparse channel dataset in tunnel scenario The specific expression is as follows: ; In the formula, Indicates The power of each path; Indicates The delay of each path; Calculate the signal received power at the terminal for the tunnel edge; is the path loss in the tunnel scenario; is the RMS delay spread in the tunnel scenario; is the channel capacity in the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0046] The above calculation process of sparse channel data in the tunnel scenario covers path power, received power, path loss, RMS delay spread, channel capacity and the final data set definition.

[0047] Step 5, channel data mapping: The user maps the acquired sparse channel data in the tunnel scenario in this example to the channel map model.

[0048] The mapping process of sparse channel data in the tunnel scenario includes the following steps: Step 5.1, divide the three-dimensional cube area: The user divides the tunnel 3D scene into indivual A three-dimensional cube area composed of sub-cubes; it is assumed that the channel data value collected by each sub-cube is the channel data value in the entire sub-cube area.

[0049] Step 5.2, construct the channel data matrix: Construct a 3D channel map model of the tunnel scene, which is specifically represented as a channel data matrix between any two sub-cubes in the 3D area of ​​the tunnel. , where the matrix elements As shown below: ; In the formula, Indicates the first indivual subcube and indivual subcube When the sparse channel data between When , the two sub-cubes overlap and there is no channel data.

[0050] Step 5.3, map sparse channel data: Map the sparse channel data collected in the three-dimensional cube area of ​​the tunnel to the channel data matrix On the top, obtain the sparse channel data matrix in the tunnel 3D scene .

[0051] Step 5.4, define the channel data set: The user defines the channel data set in the tunnel channel map model in this example , the specific expression is as follows:

[0052] In the formula, Indicates the first The subcube and Channel data sets between sub-cubes; Indicates The subcube and The impulse response of the channel between the sub-cubes Path power in the effective propagation paths; Indicates The subcube and The impulse response of the channel between the sub-cubes The path delay in the effective propagation paths; Indicates The subcube and The received power between sub-cubes; Indicates The subcube and Path loss between sub-cubes; Indicates The subcube and The RMS delay spread between subcubes; Indicates The subcube and The channel capacity between sub-cubes; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

[0053] The above steps provide a detailed methodology for mapping sparse channel data in tunnel scenarios, and channel performance can be effectively analyzed and optimized through structured data.

[0054] Step 6, complete the channel data matrix: like Figure 3 As shown, the user has a sparse channel data matrix in the tunnel scenario in this example Complete the channel map in the tunnel scenario.

[0055] The tunnel sparse channel data matrix completion process includes the following steps: Step 6.1, estimate the channel data in the uncollected sub-cube: Channel data in the sub-cubes are not collected in the tunnel 3D cube area It is specifically expressed as the weighted average of the channel data in all the acquisition sub-cubes in the three-dimensional cube area, which is specifically expressed as follows: ; In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents weight; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the number of collection sub-cubes in the tunnel scene.

[0056] Step 6.2, calculate the Euclidean distance between each collected sub-cube: The user calculates the Euclidean distance between each collection sub-cube in the tunnel scene of this example. The calculation formula is as follows:

[0057] In the formula, Indicates the tunnel scenario. The collection sub-cube and The Euclidean distance between the collected sub-cubes; indivual The position coordinates of the collection sub-cube are ;No. indivual The position coordinates of the collection sub-cube are .

[0058] Step 6.3, calculate the semivariance: According to the following formula, the user calculates the semi-variance between each acquisition sub-cube;

[0059] In the formula, Indicates the tunnel scenario. The collection sub-cube and The semi-variance between the collected sub-cubes; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the first indivual Collect sub-cubes The true value of the channel data.

[0060] Step 6.4, fitting the semivariogram function: The user fits the semivariance function based on the Euclidean distance and semivariance between the collected sub-cubes in the tunnel scene in this example. ; As shown below:

[0061] In the formula, Represents the Euclidean distance between any two sub-cubes in the tunnel scene.

[0062] Step 6.5, calculate the semi-variance between the uncollected sub-cube and the collected sub-cube: The user uses the semivariogram function of the tunnel scenario fitted in this example Calculate uncollected subcubes Semivariance with all collected subcubes ;in, Indicates the tunnel scenario. indivual Collect sub-cubes.

[0063] Step 6.6, calculate the optimal weight: The user calculates the optimal weight according to the following formula :

[0064] In the formula, the left side of the matrix represents the product of the semivariance matrix and the weight vector, where each element reflects the relationship between different sub-cubes and their contribution to the final estimation result; the right side of the matrix includes the expected semivariance result; represents the Lagrange multiplier.

[0065] Step 6.7, calculate the channel data estimate: According to the following formula, the estimated value of the channel data in the uncollected sub-cube in the tunnel 3D cube area in this example is calculated: ;

[0066] In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents the optimal weight; Indicates the first indivual Collect sub-cubes The true value of the channel data.

[0067] The above steps constitute a complete channel data completion for tunnel scenarios. By weighting the collected channel data and extracting related information, the channel data of uncollected areas can be effectively estimated. This method not only improves the integrity of the channel data, but also optimizes the prediction performance of channel performance.

[0068] Step 7: Channel map display and layout optimization: Based on the constructed channel map, the electromagnetic wave transmission characteristics in the tunnel scenario are intuitively displayed, including propagation path power, propagation path delay, received power, path loss, root mean square delay spread and channel capacity; finally, the channel map is used to optimize the layout of tunnel edge computing terminals to ensure that the wireless transmission from various wireless transmission devices to tunnel edge computing terminals has high reliability and stability.

[0069] In summary, the tunnel edge computing terminal layout method based on channel map construction can accurately and completely construct the channel map of the three-dimensional electromagnetic scene of the tunnel, obtain channel data such as propagation path information, received power, path loss, root mean square delay spread and channel capacity, and realize the effective layout optimization of the tunnel edge computing terminal; the adaptive dynamic noise threshold generation process can generate an adaptive dynamic noise threshold for the channel impulse response of the wireless channel in the tunnel scene, and realize the effective extraction of multiple channel data in the tunnel scene; the completion process of sparse channel data in the tunnel scene can generate an adaptive dynamic noise threshold for the channel impulse response of the wireless channel in the tunnel scene, and realize the effective extraction of multiple channel data in the tunnel scene.

[0070] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A tunnel edge computing terminal layout method based on channel map construction, characterized in that: The following steps are involved: Step 1: Construct a channel impulse response model: Based on the wireless transmission characteristics of electromagnetic waves in tunnels, users build a channel impulse response model in tunnel scenarios ; The influence of direct and indirect paths is described by channel impulse response model; Step 2, adaptive dynamic noise threshold generation: The user generates an adaptive dynamic noise threshold based on the channel impulse response of the wireless channel in the tunnel scenario; Step 3, extract the effective multipath components: Based on adaptive dynamic noise threshold , extracting effective multipath components from the channel impulse response of the tunnel wireless channel; Step 4, calculate sparse channel data: The effective multipath components of the user based on the channel impulse response Calculate sparse channel data in tunnel scenarios; Step 5, channel data mapping: The user maps the acquired sparse channel data in the tunnel scenario to the channel map model; Step 6, complete the channel data matrix: User-to-user sparse channel data matrix in tunnel scenario Complete the channel map in the tunnel scenario. Step 7: Channel map display and layout optimization: Based on the constructed channel map, the electromagnetic wave transmission characteristics in the tunnel scenario are intuitively displayed, including propagation path power, propagation path delay, received power, path loss, root mean square delay spread and channel capacity; the channel map is used to optimize the layout of tunnel edge computing terminals.

2. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 1, the channel impulse response model Specifically, it is the superposition of the direct path component of electromagnetic wave propagation in the tunnel scene and the indirect path component reflected by the tunnel top, ground and side walls; As shown below: ； In the formula, Representatives from The wireless transmission device in the tunnel is The channel impulse response of a tunnel edge computing terminal depends on time t and delay ;in, and Respectively represent The wireless transmission equipment in the tunnel and the Tunnel edge computing terminals, , ; represents the power gain of the direct path; represents the channel fading coefficient of the direct path; and is the impulse function, represents the delay of the direct path, Indicates The delay of the indirect path; represents the number of indirect paths in the wireless channel of the tunnel scenario, ; Indicates The power gain of the indirect path; Indicates The channel fading coefficient of the indirect path.

3. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 2, the adaptive dynamic noise threshold generation process includes the following steps: Step 2.1, set parameters: the user sets the sliding window size to , the number of protected cells is , the false alarm probability is ; False alarm probability , and its calculation formula is as follows: ； In the formula, is a counting variable, which ranges from 0 to change; represents the threshold factor; Step 2.2, Calculate power estimates: Calculate power estimates for the lagged cells , and calculate the power estimate of the look-ahead cell ; The calculation formula is as follows: ； In the formula, The index representing the time or space position currently being processed, where ; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; Step 2.3, Calculate background noise power: Calculate an estimate of the background noise power ; The calculation formula is as follows: ； In the formula, Indicates finding the minimum value; Step 2.4, calculate the threshold factor: based on the false alarm probability The expression for further calculating the threshold factor ; The calculation formula is as follows: ； Step 2.5, calculate the adaptive dynamic noise threshold: Calculate the adaptive dynamic noise threshold ; The calculation formula is as follows: 。 4. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 3, the effective multipath component extraction process in the channel impulse response includes the following steps: Step 3.1, obtaining peak data in the channel impulse response: obtaining peak data in the channel impulse response according to the following formula; ； In the formula, Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; A variable representing the peak index, with a value range of: ; Represents the number of peaks in the channel impulse response; represents the first peak value; Step 3.2, determine and extract the effective multipath component: According to the following formula, the user is based on the adaptive dynamic noise threshold Determine the peak values ​​of the channel impulse response in the tunnel scene and extract the effective multipath components ; ； In the formula, Indicates the first The path amplitude of the effective propagation path; Indicates the first The delay of an effective propagation path; Indicates the peak value operation of the channel impulse response; Representatives from The wireless transmission device in the tunnel is The channel impulse response of the tunnel edge computing terminal depends on time. and delay ; represents the adaptive dynamic noise threshold; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: .

5. The tunnel edge computing terminal layout method based on channel map construction according to claim 4 is characterized in that: In step 3, the peak value must meet the following conditions: ； In the formula, represents the first The previous sampling point of the peak value; represents the first The next sampling point after the peak value.

6. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 4, the sparse channel data calculation in the tunnel scenario includes the following steps: Step 4.1, calculation of path power of effective propagation path: Calculate the path power of the tunnel scenario according to the following formula: Path power of effective propagation paths ; ； In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; It is The path delay The channel impulse response under ; Step 4.2, signal receiving power calculation: According to the following formula, sum the effective propagation path power at each delay to calculate the signal receiving power at the tunnel edge computing terminal ; ； In the formula, Indicates the transmission power of wireless transmission equipment in the tunnel; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; It is The path delay The channel impulse response under ; Step 4.3, path loss calculation: Calculate the path loss in the tunnel scenario according to the following formula: ; ； In the formula, Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The power of each path; Step 4.4, RMS delay spread calculation: Calculate the RMS delay spread in the tunnel scenario according to the following formula: ; ； In the formula, Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates The delay of each path; represents the power-weighted average delay in the channel impulse response; Step 4.5, channel capacity calculation: Calculate the channel capacity in the tunnel scenario according to the following formula: ; ； In the formula, It means to find the determinant operation; represents the identity matrix; represents the signal-to-noise ratio; represents the conjugate transpose operation; represents the channel matrix of the constructed tunnel scenario channel model, ; Step 4.6, define the sparse channel dataset: user-defined sparse channel dataset in tunnel scenario , sparse channel dataset in tunnel scenario The specific expression is as follows: ； In the formula, Indicates The power of each path; Indicates The delay of each path; Calculate the signal received power at the terminal for the tunnel edge; is the path loss in the tunnel scenario; is the RMS delay spread in the tunnel scenario; is the channel capacity in the tunnel scenario; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

7. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 4, the power-weighted average delay in the channel impulse response is The calculation process is as follows: 。 8. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 5, the mapping process of sparse channel data in the tunnel scenario includes the following steps: Step 5.1, divide the 3D cube area: the user divides the tunnel 3D scene into indivual A three-dimensional cube area composed of sub-cubes; assuming that the channel data value collected by each sub-cube is the channel data value in the entire sub-cube area; Step 5.2, construct the channel data matrix: construct a three-dimensional channel map model of the tunnel scene, specifically represented as the channel data matrix between any two sub-cubes in the three-dimensional area of ​​the tunnel , where the matrix elements As shown below: ； In the formula, Indicates the first indivual subcube and indivual subcube When the sparse channel data between When , the two sub-cubes overlap, and there is no channel data; Step 5.3, Mapping sparse channel data: Map the sparse channel data collected in the tunnel 3D cube area to the channel data matrix On the top, obtain the sparse channel data matrix in the tunnel 3D scene ; Step 5.4, define channel dataset: user defines channel dataset in tunnel channel map model , the specific expression is as follows: ； In the formula, Indicates the first The subcube and Channel data sets between sub-cubes; Indicates The subcube and The impulse response of the channel between the sub-cubes Path power in the effective propagation paths; Indicates The subcube and The impulse response of the channel between the sub-cubes The path delay in the effective propagation paths; Indicates The subcube and The received power between sub-cubes; Indicates The subcube and Path loss between sub-cubes; Indicates The subcube and The RMS delay spread between subcubes; Indicates The subcube and The channel capacity between sub-cubes; A variable representing the index of the number of valid propagation paths, with a value range of: ; Indicates the number of valid propagation paths in the wireless channel of the tunnel scenario.

9. The tunnel edge computing terminal layout method based on channel map construction according to claim 1 is characterized in that: In step 6, the tunnel sparse channel data matrix completion process includes the following steps: Step 6.1, estimate the channel data in the uncollected sub-cube: the channel data in the uncollected sub-cube in the tunnel 3D cube area It is specifically expressed as the weighted average of the channel data in all the acquisition sub-cubes in the three-dimensional cube area, which is specifically expressed as follows: ； In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents weight; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the number of acquisition sub-cubes in the tunnel scene; Step 6.2, calculate the Euclidean distance between each acquisition sub-cube: The user calculates the Euclidean distance between each acquisition sub-cube in the tunnel scene. The calculation formula is as follows: ； In the formula, Indicates the tunnel scenario. The collection sub-cube and The Euclidean distance between the collected sub-cubes; indivual The position coordinates of the collection sub-cube are ;No. indivual The position coordinates of the collection sub-cube are ; Step 6.3, calculate semi-variance: According to the following formula, the user calculates the semi-variance between each acquisition sub-cube; ； In the formula, Indicates the tunnel scenario. The collection sub-cube and The semi-variance between the collected sub-cubes; Indicates the first indivual Collect sub-cubes The true value of the channel data; Indicates the first indivual Collect sub-cubes The true value of the channel data; Step 6.4, fitting the semivariance function: the user fits the semivariance function based on the Euclidean distance and semivariance between each acquisition sub-cube in the tunnel scene ; As shown below: ； In the formula, represents the Euclidean distance between any two sub-cubes in the tunnel scene; Step 6.5, calculate the semivariance between the uncollected sub-cube and the collected sub-cube: the user calculates the semivariance function according to the fitted tunnel scene Calculate uncollected subcubes Semivariance with all collected subcubes ;in, Indicates the tunnel scenario. indivual Collect sub-cubes; Step 6.6, calculate the optimal weight: The user calculates the optimal weight according to the following formula : ； In the formula, represents the Lagrange multiplier; Step 6.7, calculate the estimated value of the channel data: Calculate the estimated value of the channel data in the uncollected sub-cube in the tunnel three-dimensional cube area according to the following formula: ; ； In the formula, Indicates that no sub-cubes were collected Estimated value of the mid-channel data; represents the optimal weight; Indicates the first indivual Collect sub-cubes The true value of the channel data.

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