Rapid path loss calculation method considering environmental factors
By segmenting the scene into multiple regions and iteratively adjusting the path loss index, the problem of high complexity and insufficient accuracy of the radio propagation path loss calculation model in the prior art is solved, and efficient and accurate paths under low computational complexity are achieved Loss calculation.
Patent Information
- Application Number
- CN202380038037.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2022-05-06
- Filing Date
- 2023-05-04
- Publication Date
- 2024-12-13
AI Technical Summary
The existing radio propagation path loss calculation model has high computational complexity and insufficient accuracy when dealing with complex outdoor environments, making it difficult to effectively consider a variety of environmental factors, resulting in high costs and large errors.
The path loss calculation method based on parameterized environmental factors is adopted. By dividing the scene into multiple areas, marking environmental factors, generating linear paths, accumulating path losses, and updating the path loss index through measurement data, iterative adjustment until the termination standard is reached, realizing Fast and accurate path loss calculation.
It realizes capturing the influence of multiple environmental factors under low computing complexity, improves the accuracy and efficiency of path loss calculation, reduces costs, reduces errors, and provides a more reliable radio propagation path loss model.
Smart Images
Figure CN119137989A_ABST
Abstract
Description
Technical Field
[0001] The present invention generally relates to wireless communications. More specifically, the present invention relates to a method for calculating radio propagation path loss in outdoor scenarios with low computational complexity. The present invention takes into account environmental factors, including but not limited to buildings, trees, leaves, street furniture, pedestrians, weather conditions, etc. Background Art
[0002] Radio propagation path loss calculation plays a key role in wireless network planning and optimization. This work, which is traditionally done by radio frequency (RF) engineers using physical and statistical propagation models, is labor-intensive and time-consuming, especially for large outdoor areas. It is also very costly to correct the under- or over-sizing of the radio access network due to path loss errors after the radio access network is deployed. Therefore, an efficient, economical and accurate radio propagation path loss model is highly desirable.
[0003] Currently, deterministic models and empirical models are the two most widely used types of radio propagation models. Ray tracing models (e.g., as described in V. Degli Esposti, “Ray tracing: techniques, applications and prospect,” in 2020 International Symposium on Antennas and Propagation (ISAP), pp. 307–308) and ray launching models (e.g., as described in BEGschwendtner, G. B.Burk,andF.M.Landstorfer,“Ray tracing vs.ray launching in 3-D microcell modelling,”1995) belong to the former, which are based on ray optics; they numerically solve Maxwell's equations by taking into account multipath propagation. Ray tracing models calculate rays from the receiver (Rx) to the transmitter (Tx), while ray launching models calculate rays the other way around, i.e., from Tx to Rx. The accuracy of these models depends on several factors, such as ray intensity, propagation phenomena modeled, resolution of the map, etc. A disadvantage of deterministic models is that they consume a lot of computing resources and time. Such as the Okumura-Hata model (e.g., as described in TSRappaport, Wireless communications: principles and practice, vol. 2. Prentice Hall PTR New Jersey, 1996), the Stanford University Interim (SUI) model (e.g., as described in SIUmana, NOAkpbio, and SEMbong, “Extended Stanford University Interim Model Loss Stanford University Interim Propagation Loss Model for aGmelina Arborea Tree-Lined Road,” Rev. Comput. Eng. Res., vol. 5, no. 2, pp. 57–63, 2018), and the COST-231 Hata model (e.g., as described in RV Akhpashev and AV Andreev, “COST 231 Hata adaptation model for urban conditions in LTE networks,” in 2016 17th International Conference of Young Specialists on Micro / Nanotechnologies and Electron Empirical models such as those described in Devices (EDM), pp. 64–66, use simple formulas to calculate path loss for a class of typical scenarios, but sacrifice accuracy. The path loss calculated using empirical models depends on only a few parameters, including the scenario-dependent path loss exponent (which applies to the entire scenario) and the distance between Tx and Rx, among others. However, empirical models are only valid for the same type of environment for which the model was trained.Applying empirical models to different types of environments can result in root mean square errors (RMSE) of 10 dB or more.
[0004] Since environmental factors are difficult to model, several parameterized models have been proposed for path loss prediction, including the MiWEBA channel model (e.g., as described in A. Maltsev et al., “Channel modeling and characterization,” Deliv. FP7-ICT, vol. 368721, p. D5, 2014), QuaDRiGa (e.g., as described in SJ et al., QuaDRiGa—Quasi deterministic radio channel generator, user manual and documentation. 2017), mmMAGIC channel model (e.g., as described in M. Peter, K. Haneda, SLH Nguyen, A. Karttunen, and J. “Measurement results and final mmMAGIC channel models,” Deliv. D2, vol. 2, p. 12, 2017), METIS channel model (e.g., as described in V. Nurmela et al., “Deliverable D1.4 METIS channel models,” Proc. Mob. Wirel. Commun. Enablers Inf. Soc. (METIS), p. 1, 2015), 5G CMSIG (e.g., as described in A. Univ, 5G channel model for bands up to 100 GHz, v2.0.2014), 3GPP channel model (e.g., as described in S. Antipolis, “Study on channel model for frequencies from 0.5 to 100 GHz (release 14) V14.0.0,” vol. Rep. TR38.2017), IMT-2020 channel model (e.g., as described in I. Telecommun, “Preliminary Draft New ReportITU-RM.[IMT-2020.EVAL],” vol. document R. 2017) and a more general 5G channel model (MG5GCM) (e.g., as described in S. Wu, C.-X. Wang, M. M. Alwakeel, and X. You, “A general 3-D non-stationary 5G wireless channel model,” IEEE Trans. Commun., vol. 66, no. 7, pp. 3065–3078, 2017). The values of the parameters in these models can be extracted from measured data or provided by ray tracing simulation results (e.g., as described in C.-X. Wang, J. Bian, J. Sun, W. Zhang, and M. Zhang, “A survey of 5G channel measurements and models,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3142–3168, 2018). Among these models, the MiWEBA channel model, mmMAGIC channel model, METIS channel model, 5GCMSIG, 3GPP channel model, and IMT-2020 channel model accommodate the propagation characteristics of blockage and gas absorption, and can be expanded to incorporate high blocking effects caused by environmental factors.In addition, the IMT-2020 channel model also incorporates the impact of vegetation. However, there is still a lack of comprehensive channel models that can incorporate environmental factors and have low computational complexity. Summary of the invention
[0005] In order to overcome the above-mentioned shortcomings and deficiencies of the existing radio propagation models, the present invention proposes a method for calculating outdoor radio propagation path loss based on parameterized environmental factors.
[0006] The present invention relates to a path loss model, which can make up for the shortcomings of the above-mentioned path loss calculation model. The model can capture all environmental factors and calculate the path loss with lower computational complexity. The steps of developing such a model may include the following:
[0007] 1. Raster maps, including but not limited to satellite images and Geographic Information Systems (GIS), are divided into regions that may contain any number of horizontally and vertically contiguous pixels and have any shape.
[0008] 2. Label environmental factors for each region manually or automatically using digital image processing techniques. The region-by-region labeling is parameterized to predict radio propagation path loss.
[0009] 3. Generate a straight line path between Tx (transmitter, such as a base station) and Rx (receiver, such as a mobile terminal); record environmental factors and path distances in each area where the path travels.
[0010] 4. The path loss between Tx and Rx is calculated by accumulating the path loss (in dB) of each area on the straight path.
[0011] 5. Using the measurement data of the same scene to modify the value of the path loss index associated with each environmental factor, for example, by minimizing the sum of squared errors between the measured and predicted values of the path loss in each area, and / or by minimizing the sum of squared errors between the measured and predicted values of the path loss.
[0012] According to a first aspect of the present invention, there is provided a method for calculating radio path loss taking into account parameterized environmental factors, the method comprising the following steps:
[0013] (a) Segment the scene of interest into multiple regions;
[0014] (b) using environmental factors to mark each area, wherein the environmental factors of each area are mapped to a path loss index;
[0015] (c) Initialize the path loss index of each area;
[0016] (d) generating straight-line path information for each of a plurality of transmitter-receiver pairs in the scene of interest, the straight-line path information comprising a straight-line path between a transmitter (Tx) and a receiver (Rx) in the scene of interest;
[0017] (e) for each transmitter-receiver pair in the plurality of transmitter-receiver pairs in the scene of interest, calculating a path loss between the transmitter (Tx) and the receiver (Rx) by accumulating path losses of respective regions passed by the straight path between the transmitter (Tx) and the receiver (Rx), so as to obtain a calculated value;
[0018] (f) obtaining, for each of the plurality of transmitter-receiver pairs in the scene of interest, a measurement result of a path loss between the transmitter (Tx) and the receiver (Rx) so as to obtain measurement data;
[0019] (g) updating the path loss index of each area related to the environmental factor according to the calculated value and the measurement data;
[0020] (h) iterating steps (e) to (g) until a predefined termination criterion is reached; and
[0021] (i) Output the updated path loss index of each area for future path loss prediction or training.
[0022] In an example, a method of radio propagation path loss calculation taking into account parameterized environmental factors is provided, the method comprising:
[0023] a) segmenting a two-dimensional or three-dimensional scene of interest (e.g., an area or volume) into a number of regions;
[0024] b) marking each area with environmental factors, where the environmental factors of each area are regarded as path loss index;
[0025] c) Initialize the path loss index of each area;
[0026] d) generating straight path information between a transmitter (Tx) region and a receiver (Rx) region;
[0027] e) Calculate the path loss by accumulating the path loss of each area in the straight path;
[0028] f) Use the measurement data to update the path loss index related to environmental factors in each area;
[0029] g) iterating steps e) and f) until a predefined termination criterion is reached; and
[0030] h) Save the path loss index of each area for future path loss prediction or training reference.
[0031] In another example, a method of radio path loss calculation taking into account parameterized environmental factors is provided, the method comprising:
[0032] a) Segment the scene of interest into multiple regions;
[0033] b) marking each area with environmental factors, wherein the environmental factors of each area are mapped to a path loss index;
[0034] c) Initialize the path loss index of each area;
[0035] d) generating straight path information, which includes a straight path between a transmitter (Tx) and a receiver (Rx) in a scene of interest;
[0036] e) Calculate the path loss between the transmitter and the receiver by accumulating the path loss of each area traversed by the straight-line path;
[0037] f) Use the measurement data to update the path loss index related to environmental factors in each area;
[0038] g) iterating steps e) and f) until a predefined termination criterion is reached; and
[0039] h) Output the updated path loss index of each area for future path loss prediction or training.
[0040] The method can be applied to any frequency band.
[0041] Each region may include one or more pixels and may be regular or irregular in shape, and wherein each region may include any one or more of the following: a building, a group of buildings, a group of leaves, one or more people, one or more vehicles, or other objects.
[0042] The environmental factors may include any one or more of the following: buildings, roads, leaves, rain, snow, vehicles, people, facilities, landscape, terrain, weather conditions, or other obstacles.
[0043] The marking step (b) may be performed using a pixel-by-pixel marking method, including any one or more of the following methods:
[0044] Manual labeling;
[0045] Automatic labeling, wherein the automatic labeling uses an algorithm based on a machine learning framework to identify geographic information, optionally wherein the machine learning framework is a neural network (NN) method.
[0046] In the labeling step (b), the labels of the various regions may be calculated based on:
[0047] The average gray value of all pixels that make up the area; or
[0048] The weighted grayscale value of all pixels that make up the region.
[0049] The step of initializing the path loss index of each area may include any one or more of the following methods:
[0050] Initializing the path loss index based on a pre-trained model;
[0051] Randomly initializing the path loss index;
[0052] Manually initializing the path loss index;
[0053] Initializing the path loss index based on measurement data; and
[0054] The path loss index is initialized based on a theoretically calculated value.
[0055] The step of generating the straight path information between Tx and Rx may include generating a matrix containing the straight path information, wherein the matrix has the following form:
[0056]
[0057] Among them, from the Tx area (X S ,Y S ) starts and ends in the Rx region The straight line passes through N k The first and second rows represent the N k The X and Y coordinates of the region, (X1, Y1) is the position of Tx, is the position of Rx, and the third line indicates the position of N k The distance of the path traveled in each of the areas.
[0058] The path loss PL of the i-th area in the straight path i This can include:
[0059] The path loss experienced at the region of Tx is calculated as:
[0060] PL0=C
[0061] where C is a constant; and
[0062] The path loss experienced at each of the other regions is calculated as:
[0063]
[0064] Among them, n i is the path loss index of region i, which depends on the environmental factors of the region, d j is the distance of the path within region j, is the distance of the path from region Tx to region i, is the ratio between the distance from region i to region Tx and the distance from region i-1 to region Tx.
[0065] The path loss experienced in each area is accumulated to calculate the total path loss between Tx and Rx, i.e., PL, which is calculated as:
[0066]
[0067] The step of updating the path loss index related to the environmental factors may also include:
[0068] For each area, calculating an error between the measurement data and the path loss calculated in step (e); and
[0069] The path loss index of each area related to the environmental factor is updated according to the calculated error.
[0070] The error between the measured data and the path loss calculated in step (e) may be calculated using any of the following errors:
[0071] the sum of all absolute differences between the measured values and the calculated values;
[0072] the sum of all squared differences between the measured values and the calculated values; or
[0073] The minimum mean square error (MMSE) between the measured value and the calculated value.
[0074] The step of updating the path loss index of each area related to the environmental factors may include any one of the following methods:
[0075] Manual update;
[0076] updating using an optimization algorithm, the optimization algorithm comprising any one or more of simulated annealing or a genetic algorithm;
[0077] Updated using a machine learning algorithm, which includes any one or more of a neural network method or a reinforcement learning method.
[0078] The predefined termination criteria may include any of the following criteria:
[0079] The error is less than the threshold;
[0080] the error remains constant after a number of consecutive epochs; or
[0081] The maximum number of operating epochs has been reached.
[0082] The path loss index for each area may be a property of a digital map in any format such as Google Maps, Bing Maps, Street Maps and any Geographic Information System. BRIEF DESCRIPTION OF THE DRAWINGS
[0083] The present invention will now be described by way of example with reference to the accompanying drawings. In the drawings:
[0084] Figure 1 A block diagram illustrating a method of path loss calculation using parameterized environmental factors according to the principles described herein.
[0085] Figure 2 A block diagram is shown that generates paths between a transmitter (ie, "Tx") and all receivers (ie, "Rx").
[0086] Figure 3 The path generation between Tx and Rx is shown.
[0087] Figure 4 An example graph is shown with grayscale markings of buildings, foliage, roads, etc. DETAILED DESCRIPTION
[0088] The following description is presented to enable any person skilled in the art to make and use the invention, and is provided in the context of a particular application. It will be apparent to those skilled in the art that various modifications to the disclosed embodiments may be made.
[0089] The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but should be accorded the widest scope consistent with the principles and features disclosed herein.
[0090] The present invention relates to a novel radio propagation path loss calculation method that comprehensively considers environmental factors (e.g., including buildings, roads, leaves, pedestrians, etc.). In an example, the path loss calculation method includes the following steps: dividing a scene of interest (e.g., an area or volume) into several regions; assigning a path loss index to each region; generating straight path information between a Tx region and an Rx region; calculating the path loss by accumulating the weighted path loss of each region in the straight path; and using measurement data to update the path loss index related to environmental factors. The main contribution of the present invention is the introduction of a path loss index related to each environmental factor, thereby making it possible to calculate the path loss quickly and accurately.
[0091] The block diagram of the present invention is as follows Figure 1 As shown. The 3D coordinates of the transmitter (i.e., "Tx") and the receiver (i.e., "Rx") in 101 are used to generate paths between Tx and all Rx in 102, and each path contains all the distances traveled in all areas from the area where Tx is located to the area where Rx is located. The raster map with red, green, and blue (RGB) or red, green, blue, and depth (RGBD) values in 103 is converted into a raster map with regional information through manual labeling or automatic labeling, and then a path loss index is assigned to each area. The assignment can be random or based on previous learning. The paths between Tx and all Rx in 102 are compared with the raster map with path loss index in 104. Figure 1 The measured path loss data for the same scenario and the Tx and Rx configurations in 106 are used to calculate the loss function in 107 based on the path loss index in 104. The loss function in 107 can be calculated as (but not limited to) L1 error (i.e., the sum of all absolute differences between the true value (e.g., measured value) and the predicted value (e.g., calculated value), L2 error (i.e., the sum of all square differences between the true value (e.g., measured value) and the predicted value (e.g., calculated value)) or minimum mean square error (MMSE) (e.g., between the measured value and the calculated value). If the loss function in 107 meets the termination criteria in 108, the path loss index for each area is output in 109 for future path loss calculation. If the termination criteria in 108 are not met, the path loss index for each area in 104 is updated according to a certain algorithm, which will be described later.
[0092] Figure 2 Demonstrates how to generate Figure 1 The path between the transmitter (ie, "Tx") in 101 and all the receivers (ie, "Rx"). The location of Tx in 201 (X S ,Y S ) and the positions of all K Rx in 202 It is used to generate the path between Tx and each Rx, such as Figure 2 The path between Tx in 203 and the kth Rx is from (X S ,Y S ) starts and ends at The straight line that passes through N k 3xN of 204 containing path information between the transmitter and the kth Rx k The matrix can be generated as follows:
[0093]
[0094] The first and second rows represent N k The X and Y coordinates of the region. (X1, Y1) is the location of Tx, is the location of Rx. The third line indicates N k The distance of the path within each of the regions.
[0095] Tx( Figure 3 The area 0) and Rx( Figure 3 The path loss between regions N in the figure can be calculated as the accumulation of the path loss experienced in each region between Tx and Rx in decibels (dB) as follows:
[0096]
[0097] Among them, PL i is the path loss experienced in region i and is calculated as:
[0098]
[0099] Where C is the path loss at a reference distance (e.g., one meter), which is a constant for a certain radio wave frequency; n i is the path loss index of region i, which is only related to the environmental factors of the region; d j is the distance of the path in region j; is the distance of the path from region 0 to region i; is the ratio of the distance from region i to region 0 and the distance from region i-1 to region 0, where a region may contain one or more pixels and may be a regular shape such as a square or an irregular shape. Figure 3 The path from the transmitter (Tx) to the receiver (Rx) is illustrated, where d j is a distance over which the path loss is exponential.
[0100] Figure 1 The measured path loss in 106 is used to train the above path loss parameters to satisfy Figure 1 The termination criteria in 108 of . The termination criteria can be implemented in various ways, including but not limited to L1, L2 or MMSE being less than a threshold, or remaining unchanged after a number of epochs, or reaching a maximum value of the running epochs. The updating of the tunable parameters can be performed using various algorithms, including but not limited to random updating, gradient descent, etc., as understood by those skilled in the art.
[0101] In other words, the update algorithm can determine the errors (e.g., differences) between the measured path loss and the calculated (e.g., predicted) path loss for each transmitter-receiver pair, and minimize these errors (e.g., differences) by adjusting the path loss index for each area. That is, the path loss index is a tunable parameter. The errors (e.g., differences) determined for multiple transmitter-receiver pairs can be combined (e.g., in order to define an L1, L2, or MMSE error, sometimes referred to as a "cost function"). The calculation, measurement, and update steps can be repeated until a termination criterion associated with the combined error (e.g., "cost function") is met. For example, the update algorithm can search in the solution space to find a path loss index that results in a minimum or near-minimum value of a cost function (e.g., L1, L2, or MMSE).
[0102] Path loss may not be directly measurable and may be extracted from the measurement of received power: Path loss = Tx power + Tx antenna gain + Rx antenna gain - received signal power.
[0103] For each transmitter-receiver pair, a measurement result of the path loss (e.g., a measurement result indicating the path loss, such as received power) can be obtained at the receiver (e.g., Rx), which indicates the path loss of the entire straight-line path between the transmitter (e.g., Tx) and the receiver (e.g., Rx) in the transmitter-receiver pair.
[0104] A transmitter (Tx) may be a member of more than one transmitter-receiver pair. For example, a scene of interest may include a transmitter (Tx) and multiple receivers (Rx). The position of the transmitter (Tx) may be fixed. In an example, in order to obtain measurement data of multiple transmitter-receiver pairs, a mobile receiver may be moved around the scene of interest, and path loss measurements (e.g., measurements indicating path loss, such as received power) may be obtained by the mobile receiver at multiple receiver positions. That is, a physical receiver may be used to measure path loss values of multiple receivers in the scene of interest. For example, a receiver may be placed on a vehicle (e.g., a car, a truck, or a cart). The vehicle may move within the scene of interest along a specified route. In another example, in order to obtain measurement data of multiple transmitter-receiver pairs, crowd-sourced measurement data may be obtained from the phones of multiple mobile network subscribers (e.g., the phones act as receivers within the scene of interest).
[0105] Figure 4 An example graph with environmental factors labeled is shown. Pixels with increasing grayscale indicate roads, grass, cars, leaves, and buildings. The grayscale intensity of different materials reflects their potential contribution to path loss.
[0106] Therefore, the present invention can be summarized as follows: 1) establish a straight line between each pair of transmitters (Tx) and receivers (Rx) in the coverage area; 2) divide each straight line along each Tx-Rx path into one or more regions according to how the environment affects radio propagation; 3) obtain the path loss index of all regions; and 4) calculate the path loss of each pair of Tx and Rx, for example according to formula (3).
[0107] In other words, the scene of interest (eg, coverage area) may be divided into a plurality of regions (eg, including regions A, B, C, D, and E). Each region is assigned a path loss index (eg, n A 、n B 、n C 、n D 、n E). The scene of interest includes at least one transmitter (Tx) and at least one receiver (Rx). A corresponding straight path is drawn for each of a plurality of transmitter-receiver pairs (e.g., Tx and Rx). It should be understood that a transmitter (Tx) may be a member of more than one transmitter-receiver pair. Similarly, it should be understood that a receiver (Rx) may be a member of more than one transmitter-receiver pair. For each transmitter-receiver pair, the path loss between the transmitter (Tx) and the receiver (Rx) is calculated by accumulating the predicted path loss in each area passed by the straight path drawn for the transmitter-receiver pair. This may be referred to as "prediction data". The prediction data may include a plurality of calculated values (e.g., calculated values for each transmitter-receiver pair). For each transmitter-receiver pair, the path loss calculation may depend on the path loss index (e.g., n) of each area passed by the straight path drawn for the transmitter-receiver pair. A 、n B 、n C 、n D 、n E ), and the length of the straight path in each area (e.g., the distance covered). For each transmitter-receiver pair, the path loss between the transmitter (e.g., Tx) and the receiver (e.g., Rx) in the same arrangement is also measured (e.g., in "real life"). For each transmitter-receiver pair, a measurement result of the path loss (e.g., a measurement result indicating the path loss, such as the received power) can be obtained at the receiver (e.g., Rx), which indicates the path loss of the entire straight path between the transmitter (e.g., Tx) and the receiver (e.g., Rx) in the transmitter-receiver pair. This can be referred to as "measurement data". The measurement data may include multiple measurement values (e.g., measurement values for each transmitter-receiver pair). The path loss index (e.g., n) for each area can be updated based on the predicted data and the measured data. A 、n B 、n C 、n D 、n E For example, the path loss index (e.g., n ) for each region may be updated by evaluating the difference (e.g., error) between the corresponding calculated and measured path losses for each transmitter-receiver pair. A 、n B 、n C 、n D 、n E). The update algorithm can determine errors (e.g., differences) between measured path losses and calculated path losses for each transmitter-receiver pair and minimize these errors (e.g., differences) by adjusting the path loss index. The errors (e.g., differences) defined for each transmitter-receiver pair can be combined (e.g., to define an L1, L2, or MMSE error, sometimes referred to as a "cost function"), and the calculation, measurement, and update steps can be repeated until a termination criterion associated with the combined error is met.
[0108] The path loss index calculated according to the principles described herein can be used to plan a radio access network. In other words, the path loss index calculated according to the principles described herein can be used in the process of configuring a wireless communication system (e.g., a radio access network) for implementation. The path loss index calculated according to the principles described herein is much more accurate than the empirical model currently used. Once the model is trained, the time to calculate the path loss is much shorter than that of a deterministic model. In addition, the path loss index can become a property of a digital map. Finally, a wireless communication system (e.g., a radio access network) configured with a more accurate path loss index (i.e., a path loss index calculated according to the principles described herein) is technically improved (e.g., provides better connectivity) than a wireless communication system designed based on the empirical model currently used.
[0109] Applicants hereby disclose in isolation each individual feature described herein and any combination of two or more such features, as long as such features or combinations can be implemented based on the entirety of this specification and according to the common knowledge of those skilled in the art, regardless of whether such features or combinations of features solve any problem disclosed herein, and do not limit the scope of the claims. Applicants point out that aspects of the present invention may consist of any such individual features or combinations of features. In view of the above description, it will be apparent to those skilled in the art that various modifications may be made within the scope of the present invention.
Claims
1. A method for calculating radio path loss taking into account parameterized environmental factors, the method comprising the following steps: (a) Segment the scene of interest into multiple regions; (b) using environmental factors to mark each area, wherein the environmental factors of each area are mapped to a path loss index; (c) Initialize the path loss index of each area; (d) generating straight-line path information for each of a plurality of transmitter-receiver pairs in the scene of interest, the straight-line path information comprising a straight-line path between a transmitter (Tx) and a receiver (Rx) in the scene of interest; (e) for each transmitter-receiver pair in the plurality of transmitter-receiver pairs in the scene of interest, calculating a path loss between the transmitter (Tx) and the receiver (Rx) by accumulating path losses of respective regions passed by the straight path between the transmitter (Tx) and the receiver (Rx), so as to obtain a calculated value; (f) obtaining, for each of the plurality of transmitter-receiver pairs in the scene of interest, a measurement result of a path loss between the transmitter (Tx) and the receiver (Rx) so as to obtain measurement data; (g) updating the path loss index of each area related to the environmental factor according to the calculated value and the measurement data; (h) iterating steps (e) to (g) until a predefined termination criterion is reached; and (i) Output the updated path loss index of each area for future path loss prediction or training.
2. The method according to claim 1, wherein: The method can be applied to any frequency band.
3. The method according to claim 1 or 2, wherein: Each region includes one or more pixels and is regular or irregular in shape, and wherein each region includes any one or more of the following: a building, a group of buildings, a group of leaves, one or more persons, one or more vehicles, or other objects.
4. The method according to any one of claims 1 to 3, wherein: The environmental factors include any one or more of the following: buildings, roads, leaves, rain, snow, vehicles, people, facilities, landscape, terrain, weather conditions or other obstacles.
5. A method according to any preceding claim, wherein: The marking step (b) is performed using a pixel-by-pixel marking method, wherein the pixel-by-pixel marking method includes any one or more of the following methods: Manual labeling; Automatic labeling, wherein the automatic labeling uses an algorithm based on a machine learning framework to identify geographic information, optionally wherein the machine learning framework is a neural network (NN) method.
6. A method according to any preceding claim, wherein: In the labeling step (b), the label of each region is calculated based on: The average gray value of all pixels that make up the area; or The weighted grayscale value of all pixels that make up the region.
7. A method according to any preceding claim, wherein: The step of initializing the path loss index of each area includes any one or more of the following methods: Initializing the path loss index based on a pre-trained model; Randomly initializing the path loss index; Manually initializing the path loss index; Initializing the path loss index based on measurement data; and The path loss index is initialized based on a theoretically calculated value.
8. A method according to any preceding claim, wherein: The step of generating the straight path information between Tx and Rx includes generating a matrix containing the straight path information, wherein the matrix has the following form: Among them, from the Tx area (X S ,Y S ) starts and ends in the Rx region The straight line passes through N k The first and second rows represent the N k The X and Y coordinates of the region, (X1, Y1) is the position of the Tx, is the position of the Rx, and the third line indicates the position of the N k The distance of the path traveled in each of the regions.
9. A method according to any preceding claim, wherein: The path loss PL of the i-th area in the straight path i include: The path loss experienced at the area of the Tx is calculated as: PL0=C where C is a constant; and The path loss experienced at each of the other regions is calculated as: Among them, n i is the path loss index of region i, which depends on the environmental factors of the region, d j is the distance of the path within region j, is the distance of the path from the region of Tx to region i, is the ratio between the distance from region i to the region of Tx and the distance from region i-1 to the region of Tx.
10. The method according to claim 9, wherein: The path loss experienced in each area is accumulated to calculate the total path loss between the Tx and the Rx, i.e., PL, which is calculated as:
11. A method according to any preceding claim, wherein: The step of updating the path loss index associated with the environmental factor further includes: calculating an error between the measured data and the calculated value; and The path loss index of each area related to the environmental factor is updated according to the calculated error.
12. The method according to claim 11, wherein: The error between the measured data and the calculated value is calculated using any of the following errors: the sum of all absolute differences between the measured values and the calculated values; the sum of all squared differences between the measured values and the calculated values; or The minimum mean square error (MMSE) between the measured value and the calculated value.
13. The method according to claim 11 or 12, wherein: The step of updating the path loss index of each area related to the environmental factor comprises any one of the following methods: Manual update; updating using an optimization algorithm, the optimization algorithm comprising any one or more of simulated annealing or a genetic algorithm; Updated using a machine learning algorithm, which includes any one or more of a neural network method or a reinforcement learning method.
14. A method according to any preceding claim, wherein: The predefined termination criteria include any one of the following criteria: The error is less than the threshold; the error remains constant after a number of consecutive epochs; or The maximum number of operating epochs has been reached.
15. A method according to any preceding claim, wherein: The path loss index for each area may be a property of a digital map in any format such as Google Maps, Bing Maps, Street Maps and any Geographic Information System.