Abnormity positioning method and system of physical layer and storage medium

Abstract

The invention relates to a physical layer anomaly positioning method and system and a storage medium. Comprising the steps that base station data sent by a test machine are received, the base station data are captured from a base station, and the base station data are composed of configuration parameters and data transmitted by a channel under the configuration parameters; channel data transmission simulation processing is conducted on the base station data through an algorithm simulation platform, a first group of node data in the channel data transmission simulation processing process is recorded, and the first group of node data comprises first node data corresponding to all nodes in a physical layer link; channel data transmission processing is conducted on the base station data through the offline verification platform, a second group of node data in the channel data transmission processing process is recorded, and the second group of node data comprises second node data corresponding to all nodes in the physical layer link; and under the condition that the first node data of the node is inconsistent with the second node data of the node, determining that the node is an abnormal node. According to the invention, the abnormity positioning efficiency of the physical layer can be improved.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a physical layer anomaly location method, system, and storage medium. Background Technology

[0002] In mobile communication networks, base stations are a crucial component. The physical layer, located at the lowest level of the base station protocol model, is primarily responsible for encoding / decoding, modulation / demodulation, and mapping / demapping processes for uplink and downlink channels. The implementation of the physical layer involves numerous complex computations and demands high real-time performance and reliability. Therefore, testing the physical layer and rapidly locating problems are essential to ensuring base station reliability.

[0003] In related technologies, problem localization often involves manually collecting parameters of the current environment and data from intermediate processing nodes in the physical layer. The collected parameters are then used to perform simulations on an algorithm simulation platform to obtain standard node data, which is then compared with the node data collected in the environment. However, since most problems are sudden, the necessary data cannot always be collected. Therefore, it is necessary to collect large amounts of node data multiple times for comparison and analysis to identify abnormal nodes, resulting in low efficiency in physical layer anomaly localization. Summary of the Invention

[0004] Therefore, it is necessary to provide a physical layer anomaly localization method, system, and storage medium to address the aforementioned technical problems, so as to improve the efficiency of physical layer anomaly localization.

[0005] Firstly, this application provides a physical layer anomaly localization method applied to a server, the method comprising:

[0006] The base station data is captured from the base station and consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0007] The base station data is processed through channel data transmission simulation using an algorithm simulation platform, and the first set of node data is recorded during the channel data transmission simulation process. The first set of node data includes the first node data corresponding to each node in the physical layer link. The base station data is processed through channel data transmission using an offline verification platform, and the second set of node data is recorded during the channel data transmission process. The second set of node data includes the second node data corresponding to each node in the physical layer link.

[0008] For any given node, if the data of the first node and the data of the second node are inconsistent, the node is identified as an abnormal node.

[0009] In one embodiment, the base station data satisfies a capture condition, which includes at least one of a subframe condition, a UE condition, a channel condition, and a capture count condition.

[0010] Subframe conditions include the requirement that base station data in the frame structure must meet the target subframe settings;

[0011] UE conditions include that the UE type corresponding to the base station data must meet the target UE type;

[0012] Channel conditions include the requirement that the channel type corresponding to the base station data must meet the target channel type;

[0013] The data capture count requirement includes ensuring that the total number of base station data does not exceed the target number of captures.

[0014] In one embodiment, if the target channel type satisfied by the base station data is an uplink channel, then the base station data includes data from the physical layer uplink ingress end and the verification result for the ingress end data; if the target channel type satisfied by the base station data is a downlink channel, then the base station data includes data from the physical layer downlink egress end.

[0015] In one embodiment, a channel data transmission simulation process is performed on base station data using an algorithm simulation platform, and a first set of node data is recorded during the channel data transmission simulation process. This first set of node data includes the first node data corresponding to each node in the physical layer link. Additionally, a second set of node data is recorded during the channel data transmission simulation process using an offline verification platform. This second set of node data includes the second node data corresponding to each node in the physical layer link, including:

[0016] For any base station data, the data transmitted through the channel under the configuration parameters contained in the base station data is decoded to obtain the decoding result of the base station data;

[0017] If the decoding result indicates decoding failure, the base station data is processed through channel data transmission simulation using an algorithm simulation platform. The first set of node data during the channel data transmission simulation process is recorded. The first set of node data includes the first node data corresponding to each node in the physical layer link. The second set of node data is processed through an offline verification platform. The second set of node data includes the second node data corresponding to each node in the physical layer link.

[0018] In one embodiment, the method further includes at least one of the following:

[0019] Send the decoding results of the base station data to the test machine;

[0020] Send the anomaly location results to the test machine. The anomaly location results include the anomaly node.

[0021] Secondly, this application provides a physical layer anomaly localization method, applied to a test machine, the method comprising:

[0022] Send a data capture command to the base station. The data capture command is used to trigger the base station to capture at least one base station data. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0023] The system receives base station data fed back from the base station and forwards the base station data to the server, so that the server executes any of the physical layer anomaly localization methods provided in the first aspect above based on the base station data.

[0024] In one embodiment, sending a data capture command to the base station includes:

[0025] The system sends a data capture command to the base station based on the data capture conditions. The data capture command is used to instruct the base station to capture base station data that meets the data capture conditions.

[0026] In one embodiment, the method further includes:

[0027] Receive the decoding result sent by the server;

[0028] If the decoding result indicates successful decoding, the step of sending a data capture command to the base station based on the data capture condition is repeated until the preset number of rounds is reached or the received decoding result indicates decoding failure.

[0029] Thirdly, this application provides a physical layer anomaly localization method applied to a base station, the method comprising:

[0030] Receive data capture commands sent by the test machine;

[0031] In response to the data capture command, base station data is captured. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0032] The test machine forwards base station data to the server, so that the server can execute the physical layer anomaly localization method provided in the first aspect above based on the base station data.

[0033] In one embodiment, the data capture command includes data capture conditions, and the data capture of base station data in response to the data capture command includes:

[0034] Responding to the data capture command, it captures base station data that meets the data capture conditions.

[0035] Fourthly, this application provides a physical layer anomaly localization system, which includes a test machine, a server, and a base station, wherein:

[0036] The test unit is used to send data capture commands to the base station;

[0037] The base station is used to capture base station data using the data capture command and send the base station data to the test machine. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0038] The test unit is used to send base station data to the server;

[0039] The server is used to perform channel data transmission simulation processing on base station data through an algorithm simulation platform, and record the first set of node data during the channel data transmission simulation processing process. The first set of node data includes the first node data corresponding to each node in the physical layer link. The server also performs channel data transmission processing on base station data through an offline verification platform and records the second set of node data during the channel data transmission processing process. The second set of node data includes the second node data corresponding to each node in the physical layer link. If the first node data and the second node data of any node are inconsistent, the node is identified as an abnormal node.

[0040] Fifthly, embodiments of this application also provide a server, the server comprising: a memory, a transceiver, and a processor; the memory for storing a computer program; the transceiver for sending and receiving data under the control of the processor; and the processor for reading the computer program from the memory and executing the steps of the physical layer anomaly localization method provided in the first aspect.

[0041] In a sixth aspect, embodiments of this application also provide a test machine, which includes: a memory, a transceiver, and a processor: the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and execute the steps of the physical layer anomaly localization method provided in the second aspect above.

[0042] In a seventh aspect, embodiments of this application also provide a base station, which includes: a memory, a transceiver, and a processor: the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and execute the steps of the physical layer anomaly localization method provided in the third aspect above.

[0043] Eighthly, this application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any physical layer anomaly localization method.

[0044] Ninthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of an anomaly localization method for any physical layer.

[0045] The aforementioned physical layer anomaly localization method, system, and storage medium can process a small amount of base station data captured from the base station using an algorithm simulation platform and an offline verification platform to obtain node data corresponding to each node in the physical layer link. For any given node, the node data obtained from the two platforms are compared, and the abnormal node is determined based on the comparison result. This eliminates the need to collect data from each node in the environment multiple times, effectively reducing the number and amount of data collection and thus improving the efficiency of physical layer anomaly localization. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 A schematic diagram of the structure of an anomaly localization system at the physical layer in one embodiment is shown;

[0048] Figure 2 A flowchart illustrating an anomaly localization method applied to the physical layer on the server side in one embodiment is shown.

[0049] Figure 3 A flowchart illustrating the uplink and downlink channel decoding process performed by the server in one embodiment is shown.

[0050] Figure 4 A flowchart illustrating an anomaly localization method for the physical layer applied to the test machine side in one embodiment is shown.

[0051] Figure 5 A flowchart illustrating the process of the test machine issuing a data capture command in one embodiment is shown;

[0052] Figure 6 A flowchart illustrating an anomaly localization method applied to the physical layer on the base station side in one embodiment is shown.

[0053] Figure 7 A flowchart illustrating an anomaly localization method in an anomaly localization system applied to the physical layer in one embodiment is shown.

[0054] Figure 8 This is a structural block diagram of a server in one embodiment;

[0055] Figure 9 This is a structural block diagram of the test machine in one embodiment;

[0056] Figure 10This is a structural block diagram of a base station in one embodiment. Detailed Implementation

[0057] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0058] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0059] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0060] The technical solutions provided in this application can be applied to a variety of systems. For example, applicable systems may include Long Term Evolution (LTE) systems, LTE Frequency Division Duplex (FDD) systems, LTE Time Division Duplex (TDD) systems, Long Term Evolution Advanced (LTE-A) systems, Universal Mobile Telecommunications System (UMTS), Worldwide Interoperability for Microwave Access (WiMAX) systems, 5G New Radio (NR) systems, and their evolved communication systems. These systems may include terminal equipment, network equipment, and core network components, such as Evolved Packet System (EPS) and 5G systems (5GS).

[0061] The terminal devices involved in the embodiments of this application can be devices that provide voice and / or data connectivity to users, handheld devices with wireless connectivity, or other processing devices connected to a wireless modem. The names of the terminal devices may differ in different systems; for example, in a 5G system, a terminal device can be called User Equipment (UE). Wireless terminal devices can be USB storage devices, other personal computer memory devices, and dongles. They can also communicate with one or more core networks (CNs) via a Radio Access Network (RAN). Wireless terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones) and computers with mobile terminal devices. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples of such devices include Personal Communication Service (PCS) phones, cordless phones, Session Initiated Protocol (SIP) phones, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), personal computers, tablets, and Machine-type Communication (MTC) terminal devices. Wireless terminal devices can also be referred to as systems, subscriber units, subscriber stations, mobile stations, mobile terminals, remote stations, access points, remote terminals, access terminals, user terminals, user agents, user devices, and wireless access devices and routers / modems that meet the limitations of this definition; however, this application does not limit the scope of the embodiments described.

[0062] The network device involved in the embodiments of this application can be a base station, which may include multiple cells providing services to terminals. Depending on the specific application, the base station may also be called an access point, or a device in the access network that communicates with wireless terminal devices through one or more sectors on the air interface, or other names. The network device can be used to exchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network. The network device can also coordinate the attribute management of the air interface. For example, the network device involved in the embodiments of this application may be an evolved Node B (eNB or e-NodeB) in a long term evolution (LTE) system, a 5G base station (gNB) in a next generation system, or a Home evolved Node B (HeNB), relay node, femto, pico, network testing equipment, etc., and is not limited in the embodiments of this application. In some network architectures, network devices may include centralized unit (CU) nodes and distributed unit (DU) nodes, which may also be geographically separated.

[0063] In the process of physical layer anomaly localization in base stations, related technologies often employ manual collection of data from intermediate nodes in the actual environment, followed by simulation processing using an algorithm simulation platform to obtain standard node data. This data is then compared with the node data collected in the environment to locate the problem. This method involves collecting large amounts of data and numerous collection attempts, resulting in relatively low efficiency in physical layer anomaly localization.

[0064] The physical layer anomaly localization method provided in this application embodiment does not require multiple data collections of each node in the environment. It only collects a small amount of base station data from the base station, and then performs channel data transmission processing on the small amount of base station data captured from the base station through an algorithm simulation platform and an offline verification platform, thereby obtaining the node data corresponding to each node in the physical layer link. Furthermore, for any node, the node data of that node obtained by the two platforms are compared, and the abnormal node is determined based on the comparison result. This can effectively reduce the number and amount of data collection, thereby improving the efficiency of physical layer anomaly localization.

[0065] Reference Figure 1The diagram illustrates a physical layer anomaly localization system according to an embodiment of this application. The system comprises three parts: a base station 101, a test unit 102, and a server 103. The base station 101 has a baseband processing chip with a storage module, and physical layer processing software and platform software run on this chip. The test unit 102 includes a human-computer interaction interface and a data storage module, and is connected to both the base station and the server. The server 103 runs an algorithm simulation platform, an offline verification platform, and a data comparison tool, and also has a storage module for storing data. These modules can interact with each other. The test unit 102 is connected to the base station 101 and the server 103 via a network cable, enabling data interaction with both and ensuring the automated operation of the entire physical layer problem localization system.

[0066] In one exemplary embodiment, reference is made to Figure 2 As shown, the physical layer anomaly localization method provided in this application embodiment can be applied to... Figure 1 Server 103 may include the following steps 201 to 203, wherein:

[0067] Step 201: Receive base station data sent by the test machine. The base station data is captured from the base station and consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0068] In this embodiment of the application, the test machine can send a data capture command to the base station, and the base station can respond to the data capture command to capture base station data of the physical layer, including data of the physical layer uplink ingress end and the verification result of the data of the ingress end, data of the physical layer downlink egress end, etc.

[0069] Base station data consists of configuration parameters and data transmitted through the channel under those parameters. Configuration parameters, also known as scheduling parameters, include cell-level parameters and UE (User Equipment)-level parameters. Cell-level parameters are determined when the cell is established, such as cell ID (Identification), frame structure type, bandwidth, and CP (Cyclic Prefix) type. UE-level parameters are those that are scheduled in real time by the MAC (Medium Access Control) layer during actual operation, such as RNTI (Radio Network Temporary Indentifier), frame number and subframe number, time-frequency domain resource information, transport block, and MCS (Modulation and Coding Scheme). In other words, UE-level parameters are constantly changing parameters in the real-time environment.

[0070] After receiving the base station data captured by the base station in response to the data capture command, the test machine can forward the base station data to the server.

[0071] Step 202: Perform channel data transmission simulation processing on the base station data through the algorithm simulation platform, and record the first set of node data during the channel data transmission simulation processing. The first set of node data includes the first node data corresponding to each node in the physical layer link. Perform channel data transmission processing on the base station data through the offline verification platform, and record the second set of node data during the channel data transmission processing. The second set of node data includes the second node data corresponding to each node in the physical layer link.

[0072] First, the algorithm simulation platform and offline verification platform will be introduced:

[0073] Algorithm simulation platforms are generally built using the MATLAB simulation environment, but are not limited to it. They include simulations of the uplink and downlink transmission and reception processes of the physical layer. They are often used to build physical layer simulation links, develop algorithms, and serve as a standard to guide the development and verification of physical layer software versions.

[0074] The offline verification platform, also known as the physical layer processing offline platform, is an offline platform used to run the physical layer software of the base station. It includes the downlink channel transmitting end processing process and the uplink channel receiving end processing process of the base station. It is an auxiliary means for offline debugging and locating physical layer software problems.

[0075] In this embodiment, the algorithm simulation platform can perform channel data transmission simulation processing on base station data, that is, run the physical layer channel processing process. For example, if the data is transmitted via the downlink channel, the downlink channel transmitter processing process is run, saving important node data such as scrambling, encoding, modulation, and mapping as the first set of node data; if the data is transmitted via the uplink channel, the uplink channel receiver processing process is run, saving important node data such as demapping, demodulation, decoding, and descrambling as the first set of node data. The first node data corresponding to each node in the generated first set of node data can be named and saved according to parameter numbers, for example, ALG_DATA_N_XY.txt, where ALG represents the data generated by the algorithm simulation platform, N represents the base station data number, and XY represents the physical layer processing node. The first set of node data is stored in a specified directory after generation.

[0076] Similarly, the offline verification platform also performs channel data transmission processing on base station data. That is, it runs the physical layer channel processing procedure. For example, if the data is transmitted via the downlink channel, the downlink channel transmitter processing procedure is run, saving important node data such as scrambling, encoding, modulation, and mapping as the second set of node data; if the data is transmitted via the uplink channel, the uplink channel transmitter processing procedure is run, saving important node data such as demapping, demodulation, decoding, and descrambling as the second set of node data. The second set of node data can be named and saved according to parameter numbers, for example, PHY_DATA_N_XY.txt. PHY represents the data generated by the offline verification platform, N represents the base station data number, and XY represents the physical layer processing node. The second set of node data is stored in a specified directory after generation.

[0077] It should be noted that the output data format of each node in the algorithm simulation platform and the offline verification platform must be consistent, and the data files must use a unified command rule to avoid confusion during data comparison.

[0078] Step 203: For any node, if the data of the first node and the data of the second node are inconsistent, the node is determined to be an abnormal node.

[0079] In this embodiment, after obtaining the first set of node data and the second set of node data, the first set of node data and the second set of node data can be compared. If it is determined that the first set of node data and the second set of node data of a node are inconsistent, the node is identified as an abnormal node. For example, for base station data with data number x, the first set of node data and the second set of node data are obtained. For node i, during the comparison, the first node data of the node is obtained from the first set of node data: ALG_DATA_x_i.txt, and the second node data of the node is obtained from the second set of node data: PHY_DATA_x_i.txt. If the first set of node data and the second set of node data are consistent, then node i is not abnormal; or, if the first set of node data and the second set of node data are inconsistent, then node i is determined to be abnormal and is identified as an abnormal node.

[0080] The physical layer anomaly localization method provided in this application can process a small amount of base station data captured from the base station through an algorithm simulation platform and an offline verification platform to obtain the node data corresponding to each node in the physical layer link. For any node, the node data of the node obtained by the two platforms are compared. Based on the comparison result, the abnormal node can be determined. This eliminates the need to collect data from each node in the environment multiple times, effectively reducing the number and amount of data collection and thus improving the efficiency of physical layer anomaly localization.

[0081] In one exemplary embodiment, the test machine is connected to the base station and the server via network cables. The test machine is configured with IP (Internet Protocol Address) addresses for communication with the base station and the server, ensuring that the test machine can communicate with them. For example, data capture conditions can be set on the test machine. These conditions may include conditions for capturing data and conditions for stopping data capture. The conditions for capturing data can be set according to actual environmental needs, and the conditions for stopping data capture can be a pre-set number of capture attempts N, which can be set based on the probability of physical layer service errors statistically analyzed on-site.

[0082] For example, the human-machine interface displayed on the test machine can be used to set data capture conditions and display abnormal location results. Users can input parameters to set data capture conditions on the human-machine interface. After the data capture conditions are set, the test machine can send a data capture command carrying the data capture conditions to the base station.

[0083] In one exemplary embodiment, the capture conditions may include at least one of subframe conditions, UE conditions, channel conditions, and capture count conditions, wherein,

[0084] Subframe conditions include the requirement that base station data in the frame structure must meet the target subframe settings;

[0085] UE conditions include that the UE type corresponding to the base station data must meet the target UE type;

[0086] Channel conditions include the requirement that the channel type corresponding to the base station data must meet the target channel type;

[0087] The data capture count requirement includes ensuring that the total number of base station data does not exceed the target number of captures.

[0088] In this context, a subframe is a time granularity (or time unit) that conforms to the 3GPP (3rd Generation Partnership Project) protocol frame structure. For example, a radio frame is 10ms long and includes 10 subframes, each 1ms long. The subframe values ​​range from 0 to 9. Subframe conditions are used to limit the captured base station data to meet the target subframe settings. For instance, if the target subframe is set to 1, 3, and 5, then the base station needs to capture data from subframes 1, 3, and 5.

[0089] UE conditions are used to limit the target UE type corresponding to the base station data. The UE type can include system UE and service UE. Specific system UE and service UE each correspond to different UE types. The specific target UE type can be set according to actual needs. For example, assuming the UE type in the data capture condition is system UE, then the base station needs to capture the data corresponding to that system UE. That is, the UE type of the base station data finally captured should be system UE.

[0090] Channel conditions are used to determine whether to capture uplink or downlink data. The base station physical layer is typically used to handle downlink transmission processing and uplink reception processing. Channel conditions can be set according to specific channel information to capture uplink or downlink parameters and data. For example, assuming the target channel type in the capture conditions is downlink, the base station needs to capture downlink data as base station data.

[0091] The number of data captures can be flexibly adjusted based on the probability of problems occurring in the actual environment. For example, the number of data captures is negatively correlated with the probability of problems occurring. If there is a high probability of an anomaly occurring, the target number of data captures N can be set smaller. This can save resources and improve the efficiency of location.

[0092] After receiving the data capture command, the base station can start the data capture process in the physical layer software part of the base station, capture the configuration parameters that meet the data capture conditions and the data transmitted on the channel under the configuration parameters, and store the captured parameters and data as base station data in the base station processor memory.

[0093] For example, the base station physical layer software can initiate a joint data capture process based on the received data capture command, ensuring that the captured physical layer input parameters and output data are the same set of data. The base station can temporarily store multiple sets of base station data that meet the data capture conditions in the processor's memory, and when the number of data captures reaches N, it can extract the N captured base station data from the base station processor's memory and send them to the test machine, so that the base station data can be uploaded to the server through the test machine.

[0094] In this embodiment of the application, the base station can capture base station data based on the data capture conditions, thereby capturing only the data required for testing, reducing the amount of data captured, thereby reducing the amount of data processing in the anomaly location process, and greatly improving the anomaly location efficiency.

[0095] In an exemplary embodiment, if the target channel type satisfied by the base station data is an uplink channel, then the base station data includes data from the physical layer uplink ingress end and the verification result for the ingress end data; if the target channel type satisfied by the base station data is a downlink channel, then the base station data includes data from the physical layer downlink egress end.

[0096] In this embodiment, the base station physical layer primarily handles downlink transmission (encoding) and uplink reception (decoding). The base station data captured by the base station is either the output of the downlink physical layer after processing, meeting the capture conditions, or the data to be processed received by the uplink physical layer and the output data of an important intermediate node. For example, when channel conditions are set, the base station data captured by the base station meets those conditions. When the target channel type is an uplink channel, meaning the captured base station data meets the target channel type of an uplink channel, the captured base station data consists of the data at the physical layer uplink ingress point and the verification result for that ingress point. The verification result may include a CRC (Cyclic Redundancy Check) result. Alternatively, when the target channel type is a downlink channel, meaning the captured base station data meets the target channel type of a downlink channel, the captured base station data consists of the data at the physical layer downlink egress point.

[0097] The physical layer anomaly localization method provided in this application addresses the problem of online anomaly localization. Therefore, the base station needs to operate normally during data capture and cannot consume excessive resources. Consequently, it must capture as little valid data as possible. For downlink anomalies, the target channel type is set to downlink channel, and the base station only captures data from one node at the downlink egress end. For uplink anomalies, the target channel type is set to uplink channel, and the base station only captures data from one node at the ingress end and the verification result (e.g., CRC check result) for that ingress data. The node data at the downlink egress end can be time-domain or frequency-domain data, and the node data at the uplink ingress end can also be time-domain or frequency-domain data; this application does not impose any limitations on these aspects.

[0098] In this way, we can avoid the data capture process consuming too many resources, reduce interference with normal business operations, and reduce the amount of data processing during anomaly localization, thereby improving anomaly localization efficiency.

[0099] In one exemplary embodiment, reference is made to Figure 3 As shown, in step 203, channel data transmission simulation processing of base station data is performed through an algorithm simulation platform, and the first set of node data during the channel data transmission simulation processing is recorded. The first set of node data includes the first node data corresponding to each node in the physical layer link. Additionally, channel data transmission processing of base station data is performed through an offline verification platform, and the second set of node data during the channel data transmission processing is recorded. The second set of node data includes the second node data corresponding to each node in the physical layer link. This may include steps 301 to 302, wherein:

[0100] Step 301: For any base station data, decode the data transmitted through the channel under the configuration parameters based on the configuration parameters contained in the base station data to obtain the decoding result of the base station data;

[0101] Step 302: If the decoding result indicates decoding failure, the base station data is processed through channel data transmission simulation using an algorithm simulation platform, and the first set of node data during the channel data transmission simulation process is recorded. The first set of node data includes the first node data corresponding to each node in the physical layer link. The second set of node data is processed through an offline verification platform, and the second set of node data is recorded during the channel data transmission processing process. The second set of node data includes the second node data corresponding to each node in the physical layer link.

[0102] In this embodiment, after receiving base station data sent by the test machine, the server can filter out abnormal data from the base station data and only input the abnormal data into the algorithm simulation platform and offline verification platform for channel data transmission processing, so as to reduce the amount of data processing and improve the efficiency of anomaly location.

[0103] For example, the algorithm simulation platform, on the one hand, is completely consistent with the downlink transmission and uplink reception processing of the base station physical layer. It can generate data for each node, which can be compared with the data of each node in the physical layer link output by the offline verification platform to identify potential problems in the physical layer implementation process. On the other hand, it can complete the processing (decoding) process of each uplink and downlink channel receiver. This process is the reverse of the transmission process. The base station data is decoded through the decoding process to obtain the decoding result. Based on the decoding result, it can be determined whether the parameters or data of the physical layer downlink exit and uplink entry are abnormal.

[0104] In other words, in this embodiment of the application, the uplink and downlink channel decoding process of the algorithm simulation platform can be used to filter out abnormal data in the base station data. In this way, only a small amount of data and parameters captured from the environment need to be analyzed to determine whether abnormal parameters and data of the physical layer have been captured, which can effectively reduce the amount of data processing in the anomaly localization process.

[0105] For example, uplink and downlink channel decoding can include the receiver processing of uplink and downlink channels, which is the reverse process corresponding one-to-one with the channel scrambling, encoding, modulation, and mapping processes described in the 3GPP protocol.

[0106] For uplink input data, the base station physical layer uplink performs decoding operations. This means the input data to the algorithm simulation platform is transmitted via a wireless channel, potentially introducing noise. However, as a standard platform, the algorithm simulation platform assumes all received data is normal and its decoding results are always correct. Therefore, whether the algorithm simulation platform correctly decodes the originating information does not indicate whether there is an anomaly at the originating end. Thus, the CRC check result obtained solely from the algorithm simulation platform's decoding of base station data is insufficient to determine if the received data is abnormal. Comparison with check results captured in the actual environment is necessary. Therefore, for the uplink channel, when capturing base station data, it is necessary to capture the physical layer uplink input data and the check result for that input data. A consistency comparison between this check result and the CRC check result obtained from the algorithm simulation platform's decoding of the input data is then used to determine if the data is abnormal.

[0107] In one example, base station data includes data from the physical layer uplink ingress point and a verification result for that ingress point. The server can then decode the data transmitted through the channel under those configuration parameters based on the configuration parameters contained in the data, obtaining the corresponding CRC check result. If the transmitting information is correctly decoded, the CRC is 0; otherwise, it is 1. The server compares the currently decoded CRC check result with the captured check result. If the comparison result is consistent (both are 0 or both are 1), the actual check result in the real environment is completely consistent with the check result in the simulation environment, indicating that the captured configuration parameters and data are normal, and a decoding result indicating successful decoding can be obtained. If the comparison result is inconsistent (e.g., the currently decoded CRC check result is 0, while the captured check result is 1), the actual check result in the real environment is inconsistent with the check result in the simulation environment, indicating that the captured configuration parameters and data are abnormal, and a decoding result indicating decoding failure can be obtained.

[0108] For downlink egress data, the base station physical layer performs encoding operations. This encoding process is implemented at the base station physical layer, meaning the input data to the algorithm simulation platform is not transmitted through the wireless channel and therefore does not introduce noise. Thus, if the algorithm simulation platform correctly decodes the originating information, it indicates that the originating end is normal; conversely, if it fails to decode the originating information, it indicates an anomaly at the originating end. In other words, when the algorithm simulation platform decodes the corresponding CRC check result, it can directly determine whether the data is abnormal based on this CRC check result. Therefore, for the downlink channel, when capturing base station data, only the downlink egress data at the physical layer needs to be captured. The CRC check result obtained after decoding this egress data by the algorithm simulation platform can directly determine whether the data is abnormal.

[0109] In one example, the base station data includes data from the physical layer downlink egress point. The server can then decode the data transmitted on the channel under those configuration parameters based on the configuration parameters contained in the data, obtaining the corresponding CRC check result. If the originating information is correctly decoded (CRC = 0), it indicates that the captured configuration parameters and data are normal, and a decoding result indicating successful decoding can be obtained. If the originating information is not decoded (CRC = 1), it indicates that the captured configuration parameters and data are abnormal, and a decoding result indicating decoding failure can be obtained.

[0110] After decoding all base station data, only the base station data whose decoding results indicate decoding failure are input into the algorithm simulation platform and offline verification platform for channel data transmission processing. The processing procedure is the same as described in the aforementioned embodiments and will not be repeated here. In this way, the amount of data processing during anomaly localization can be reduced, and the efficiency of anomaly localization can be improved.

[0111] In one exemplary embodiment, the above method further includes at least one of the following:

[0112] Send the decoding results of the base station data to the test machine;

[0113] Send the anomaly location results to the test machine. The anomaly location results include the anomaly node.

[0114] In this embodiment, if the decoding results of the N sets of base station data captured in this round indicate that all N sets of base station data are normal data, it means that no configuration parameters and channel transmission data under the configuration parameters when the physical layer is abnormal were captured. In this case, the decoding results of each base station data can be sent to the test machine. After receiving the decoding results and confirming that all captured base station data has been successfully decoded, the test machine can display the received decoding results on the human-machine interface.

[0115] After the test machine receives the decoding result and prints it normally, it can resend the data capture command to the base station based on the data capture conditions to start a new round of data capture and analysis process until either of the following conditions is met: the decoding result fed back to the test machine by the server indicates the presence of abnormal data, or the number of data capture rounds executed based on the current data capture conditions has reached the preset data capture threshold. In this case, the test machine does not need to continue data capture based on the current data capture conditions. It only needs to wait for the next analysis result to be printed on the test machine interface, or adjust the data capture conditions and start data capture again.

[0116] Since online issues are not always present but occur with a certain probability, and the amount of base station data captured in each round will affect the efficiency of the final anomaly location, using this method to capture multiple rounds can capture the configuration parameters and data corresponding to physical layer anomalies, which can greatly improve the efficiency of physical layer anomaly location.

[0117] In another exemplary embodiment, reference is made to Figure 4 As shown, the physical layer anomaly localization method provided in this application embodiment can be applied to... Figure 1 The test machine 102 in the middle, the method may include the following steps 401 to 402, wherein:

[0118] Step 401: Send a data capture command to the base station. The data capture command is used to trigger the base station to capture at least one base station data. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0119] Step 402: Receive base station data fed back by the base station and forward the base station data to the server so that the server can execute the physical layer anomaly location method of any of the foregoing embodiments based on the base station data.

[0120] In this embodiment, the tester can control the base station to capture data using a test machine. The test machine can send a data capture command to the base station in response to the tester's operation. The base station can then capture data from N base stations in response to this command and send the captured data back to the test machine. The test machine forwards the base station data to the server, which then executes a physical layer anomaly localization method based on the received base station data to achieve physical layer anomaly localization. The specific process can be referred to the relevant descriptions in the foregoing embodiments, and will not be repeated here.

[0121] Using the physical layer anomaly localization method provided in this application, the server can perform channel data transmission processing on a small amount of base station data captured from the base station through an algorithm simulation platform and an offline verification platform to obtain the node data corresponding to each node in the physical layer link. For any node, the node data of the node obtained by the two platforms are compared. Based on the comparison result, the abnormal node can be determined. Thus, the base station does not need to collect data of each node in the environment multiple times, which can effectively reduce the number and amount of data collection and improve the efficiency of physical layer anomaly localization.

[0122] In an exemplary embodiment, step 401, sending a data capture instruction to the base station, may include the following steps: sending a data capture instruction to the base station based on data capture conditions, wherein the data capture instruction is used to instruct the base station to capture base station data that meets the data capture conditions.

[0123] In this embodiment, the tester can set data capture conditions on the test machine. These conditions may include at least one of subframe conditions, UE conditions, channel conditions, and data capture count conditions. After setting the data capture conditions, the test machine can send a data capture command carrying the data capture conditions to the base station through an interface message between the test machine and the base station. Upon receiving the data capture command, the base station can initiate the data capture process in its physical layer software, capturing configuration parameters that meet the data capture conditions and data transmitted through the channel under those configuration parameters. The captured parameters and data are stored as base station data in the base station processor memory until the number of data captures reaches N. The N sets of captured base station data are then extracted from the processor memory and sent to the test machine, which then uploads the base station data to the server.

[0124] The description of the data capture conditions and the process of the base station capturing base station data in response to the data capture conditions can be referred to the relevant description in the foregoing embodiments, and will not be repeated in this embodiment.

[0125] In one exemplary embodiment, reference is made to Figure 5 As shown, the above method may further include steps 501 to 502, wherein:

[0126] Step 501: Receive the decoding result sent by the server;

[0127] Step 502: If the decoding result indicates successful decoding, repeat the step of sending a data capture command to the base station based on the data capture condition until the number of repetitions reaches the preset number of rounds or the received decoding result indicates decoding failure.

[0128] In this embodiment of the application, after receiving the base station data sent by the test machine, the server can use the uplink and downlink channel decoding process of the algorithm simulation platform to filter out abnormal data in the base station data, and only input the abnormal data into the algorithm simulation platform and the offline verification platform for channel data transmission processing, so as to reduce the amount of data processing and improve the efficiency of anomaly location.

[0129] After the server decodes the data from each base station (the decoding process is described in the previous embodiments and will not be repeated here), it can send the corresponding decoding results back to the test machine. Upon receiving the decoding results from the server, the test machine can display them on the human-machine interface. Once the decoding results indicate that all base station data has been successfully decoded, and it is determined that no abnormal configuration parameters or data were captured in this round, the aforementioned steps of sending a data capture command to the base station based on the data capture conditions are repeated to trigger a new round of data capture by the base station, until the preset number of data capture rounds is reached or the received decoding results indicate decoding failure.

[0130] In this process, after the number of data capture rounds reaches the preset number of rounds, the testers can reset the data capture conditions and repeat the steps of sending data capture instructions to the base station based on the new data capture conditions until the number of data capture rounds reaches the preset number of rounds or the received decoding result indicates that the decoding has failed.

[0131] In another exemplary embodiment, reference is made to Figure 6 As shown, the physical layer anomaly localization method provided in this application embodiment can be applied to... Figure 1 The method for base station 101 may include steps 601 to 603, wherein:

[0132] Step 601: Receive the data capture command sent by the test machine;

[0133] Step 602: In response to the data capture command, base station data is captured. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0134] Step 603: The test machine forwards base station data to the server so that the server can execute the physical layer anomaly localization method of any of the foregoing embodiments based on the base station data.

[0135] In this embodiment, the process of the test machine issuing the data capture command is described in the foregoing embodiments and will not be repeated here. After receiving the data capture command from the test machine, the base station can respond to the command by capturing base station data at the physical layer. For example, the base station physical layer software can initiate a joint data capture process based on the received data capture command to capture data, and ensure that the captured physical layer input parameters and output data are the same set of data. The base station can temporarily store multiple sets of base station data that meet the data capture conditions in the processor's memory, and when the number of data captures reaches N, it can extract the captured N sets of base station data from the base station processor's memory and send them to the test machine, so that the test machine can upload the base station data to the server, and the server can perform physical layer anomaly location operations based on the base station data (the specific process is described in the foregoing embodiments and will not be repeated here).

[0136] Using the physical layer anomaly localization method provided in this application, the server can perform channel data transmission processing on a small amount of base station data captured from the base station through an algorithm simulation platform and an offline verification platform to obtain the node data corresponding to each node in the physical layer link. For any node, the node data of the node obtained by the two platforms are compared. Based on the comparison result, the abnormal node can be determined. Thus, the base station does not need to collect data of each node in the environment multiple times, which can effectively reduce the number and amount of data collection and improve the efficiency of physical layer anomaly localization.

[0137] In one exemplary embodiment, the data capture instruction includes data capture conditions. Retrieving base station data in response to the data capture instruction may include the following steps: retrieving base station data that meets the data capture conditions in response to the data capture instruction.

[0138] In this embodiment, the base station can capture base station data based on capture conditions, thereby capturing only the data required for testing, reducing the amount of data captured, and thus reducing the data processing load of the anomaly localization process, which can greatly improve the efficiency of anomaly localization. The capture conditions can include at least one of subframe conditions, UE conditions, channel conditions, and capture count conditions. After the capture conditions are set, the test machine can send a capture command carrying the capture conditions to the base station through an interface message between the test machine and the base station. After receiving the capture command, the base station can start the capture process in the physical layer software part of the base station, capturing the configuration parameters that meet the capture conditions and the data transmitted through the channel under the configuration parameters. The captured parameters and data are stored as base station data in the base station processor memory until the capture count reaches N. The N sets of captured base station data are then extracted from the processor memory and sent to the test machine, so that the base station data can be uploaded to the server through the test machine. The description of the capture conditions and the base station's response to the capture conditions for capturing base station data are the same as those in the previous embodiments, and will not be repeated in this embodiment.

[0139] In another exemplary embodiment, reference is made to Figure 1 As shown, this application embodiment provides a physical layer anomaly localization system, which includes a test machine 102, a server 103, and a base station 101, wherein:

[0140] Test unit 102 is used to send data capture commands to the base station;

[0141] Base station 101 is used to capture base station data using data capture commands and send the base station data to the test machine. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters.

[0142] Test unit 102 is used to send base station data to the server;

[0143] Server 103 is used to perform channel data transmission simulation processing on base station data through an algorithm simulation platform, and record the first set of node data during the channel data transmission simulation processing process. The first set of node data includes the first node data corresponding to each node in the physical layer link. Server 103 is used to perform channel data transmission processing on base station data through an offline verification platform, and record the second set of node data during the channel data transmission processing process. The second set of node data includes the second node data corresponding to each node in the physical layer link. Server 103 is used to determine an abnormal node if the first node data and the second node data of any node are inconsistent.

[0144] In this embodiment, the operations performed by base station 101, test machine 102 and server 103 during the abnormal location process at the physical layer can be referred to the relevant descriptions in the foregoing embodiments, and will not be repeated here.

[0145] To enable those skilled in the art to better understand the embodiments of this application, the embodiments of this application are described below through specific examples.

[0146] Using the above system, the present invention provides a method for anomaly localization at the physical layer, referring to... Figure 7 As shown, the process includes steps 701 to 706, wherein:

[0147] Step 701: After the tester sets the online data capture conditions on the test machine, the test machine sends a data capture command carrying the data capture conditions to the base station.

[0148] Step 702: According to the data capture instruction, the base station captures base station data (configuration parameters and data transmitted through the channel under the configuration parameters) that meet the data capture conditions in the physical layer software part of the base station, and stores the base station data in the base station processor memory.

[0149] Step 703: Extract base station data from the base station processor memory and send it to the test machine, which then uploads the base station data to the server via a network cable;

[0150] Step 704: The server runs the algorithm simulation platform, performs uplink and downlink physical layer decoding on the data transmitted through the channel under the configuration parameters according to the configuration parameters, feeds back the decoding results to the test machine, and sends the decoding results to the algorithm simulation platform and the offline verification platform to represent the base station data that failed to decode.

[0151] Step 705: The server runs the algorithm simulation platform and the offline verification platform, so that the algorithm simulation platform and the offline verification platform generate node data of each node in the physical layer link based on the received base station data.

[0152] Step 706: Compare the node data generated by the algorithm simulation platform and the offline verification platform on the server to identify abnormal nodes in the physical layer, and feed back the abnormal location results corresponding to the abnormal nodes to the test machine.

[0153] The human-machine interface of the test machine can display data analysis results, which can be the analysis results of captured base station data, showing whether the physical layer processing has an anomaly or the processing is normal under the current configuration parameters; or it can be the physical layer anomaly location results determined by comparing the node data generated by the algorithm simulation platform with the node data of the offline verification platform. The anomaly location results can include the node information of the abnormal node, which can include the physical channel name corresponding to the abnormal node, the abnormal node name, etc.

[0154] In one example, once the base station has captured N sets of data that meet the capture criteria, it will stop the data capture process and store the captured configuration parameters and data that meet the capture criteria in the base station processor's memory. The data can be numbered during storage to facilitate automated subsequent processes. For example, the parameter file can be named PARA_N.lgz, and the data file can be named DATA_N.lgz. To save storage space, the captured configuration parameters and data can also be stored in a compressed format.

[0155] After the base station physical layer software completes data capture and storage, it notifies the platform software that data capture is complete. Upon receiving the message that data capture is complete, the base station platform software uploads the stored data from the base station processing chip's memory to a pre-specified file directory on the test device. After successfully receiving the data uploaded by the base station, the test device triggers data transmission with the server, transferring the received data to the server's designated directory, thus completing data forwarding.

[0156] After the server runs the algorithm simulation platform and the offline verification platform, it stores the node data generated by these platforms and can then notify a comparison tool on the server to perform node data comparison. For example, the comparison tool on the server compares the node data generated by the algorithm simulation platform and the offline verification platform sequentially according to the node data number, corresponding to each set of configuration parameters. The data generated by the algorithm simulation platform is standard node data. By comparing each set of data generated by the offline verification platform with the standard node data, the first abnormal node identified in the comparison is output, thereby determining the location of the anomaly at the physical layer.

[0157] After each set of configuration parameters is compared, the comparison tool records information about abnormal nodes at the physical layer. Once all data comparisons are complete, the recorded abnormal nodes are fed back to the test machine and printed on its user interface, for example: DATA_N_XY node abnormal, where N is the captured parameter number and XY represents the agreed-upon channel and node. This enables rapid and accurate location of online physical layer issues. Locating the problem location facilitates quick and precise troubleshooting by developers.

[0158] The physical layer anomaly localization method provided in this application embodiment allows for configuring online data capture conditions on a test machine. The base station, based on these conditions, captures data and configuration parameters that meet the conditions and uploads the captured base station data to a server via the test machine. An algorithm simulation platform on the server decodes the channel transmission data under the captured configuration parameters and obtains the abnormal configuration based on the decoding result. The algorithm simulation platform and offline verification platform on the server generate node data for each physical layer node based on the abnormal configuration. By comparing the node data from the two platforms, the abnormal nodes in the physical layer are determined. This application embodiment collects very little data each time, only configuration parameters and a small number of node data, allowing for continuous collection of multiple sets of data. This enables rapid identification of abnormal configurations and the determination of abnormal nodes under those configurations. It has advantages such as not affecting the real-time performance of the base station physical layer software processing, rapid location of the sub-modules of the base station physical layer software processing, and a high degree of automation, significantly improving the efficiency of physical layer problem localization.

[0159] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0160] In one exemplary embodiment, a server is provided, the server structure of which can be as follows: Figure 8 As shown. The server includes a memory 820, a transceiver 810, and a processor 800.

[0161] A transceiver is used to receive and send data under the control of a processor.

[0162] Among them, Figure 8 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (represented by a processor) and memory (represented by memory). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor is responsible for managing the bus architecture and general processing, while the memory can store data used by the processor during operation.

[0163] The processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0164] The processor executes the anomaly localization method for any physical layer of a server provided in this application embodiment by calling a program stored in memory, according to the obtained executable instructions. The processor and memory can also be physically separated.

[0165] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above embodiment of the anomaly location method applied to any physical layer of the server, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0166] In one exemplary embodiment, a testing machine is provided, the structure of which can be as follows: Figure 9 As shown. This test unit includes a memory 920, a transceiver 910, and a processor 900. It may also include a user interface 930.

[0167] A transceiver is used to receive and send data under the control of a processor.

[0168] Among them, Figure 9 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits represented by one or more processors (represented by processors) and memories (represented by memory). The bus architecture can also link various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides an interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over a transmission medium, including wireless channels, wired channels, optical fibers, etc. For different user equipment, the user interface can also be an interface capable of connecting external or internal devices, including but not limited to keypads, displays, speakers, microphones, joysticks, etc.

[0169] The processor is responsible for managing the bus architecture and general processing, while the memory can store the data used by the processor 900 when performing operations.

[0170] Optionally, the processor can be a CPU (Central Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or CPLD (Complex Programmable Logic Device), and the processor can also adopt a multi-core architecture.

[0171] The processor executes the anomaly localization method for any physical layer of the test machine provided in this application embodiment by calling a program stored in memory, according to the obtained executable instructions. The processor and memory can also be physically separated.

[0172] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above embodiment of the anomaly localization method applied to any physical layer of the test machine, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0173] In one exemplary embodiment, a base station is provided, the structure of which can be as follows: Figure 10 As shown. The base station includes a memory 1020, a transceiver 1010, and a processor 1000.

[0174] A transceiver is used to receive and send data under the control of a processor.

[0175] Among them, Figure 10 In this context, the bus architecture can include any number of interconnected buses and bridges, specifically linking various circuits together, represented by one or more processors (represented by a processor) and memory (represented by memory). The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. The bus interface provides the interface. A transceiver can be multiple components, including transmitters and receivers, providing a unit for communicating with various other devices over transmission media, including wireless channels, wired channels, optical fibers, etc. The processor is responsible for managing the bus architecture and general processing, while the memory can store data used by the processor during operation.

[0176] The processor can be a central processing unit (CPU), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a complex programmable logic device (CPLD). The processor can also adopt a multi-core architecture.

[0177] The processor executes the anomaly localization method for any physical layer of a base station provided in this application embodiment by calling a program stored in memory, according to the obtained executable instructions. The processor and memory can also be physically separated.

[0178] It should be noted that the apparatus provided in this application embodiment can implement all the method steps implemented in the above embodiment of the abnormal location method applied to any physical layer of the base station, and can achieve the same technical effect. Here, the parts that are the same as those in the method embodiment and the beneficial effects will not be described in detail.

[0179] In one exemplary embodiment, a physical layer anomaly location device is provided. The physical layer anomaly location device may be a terminal device, a base station, or a server, etc., and includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0180] In one exemplary embodiment, a processor-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0181] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0182] Processor-readable storage media can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MOs), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROMs, EPROMs, EEPROMs, non-volatile memory (NAND flash), solid-state drives (SSDs)).

[0183] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0184] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0185] These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0186] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for anomaly localization at the physical layer, characterized in that, Applied to a server, the method includes: The test machine receives base station data, which is captured from the base station and consists of configuration parameters and data transmitted through the channel under the configuration parameters. The base station data is processed through channel data transmission simulation using an algorithm simulation platform, and the first set of node data is recorded during the channel data transmission simulation process. The first set of node data includes the first node data corresponding to each node in the physical layer link. The base station data is processed through channel data transmission using an offline verification platform, and the second set of node data is recorded during the channel data transmission process. The second set of node data includes the second node data corresponding to each node in the physical layer link. If the first node data and the second node data of any of the nodes are inconsistent, the node is determined to be an abnormal node.

2. The method according to claim 1, characterized in that, The base station data meets the capture conditions, which include at least one of the following: subframe conditions, UE conditions, channel conditions, and capture count conditions. The subframe conditions include the requirement that the base station data in the frame structure must meet the target subframe settings; The UE conditions include the requirement that the UE type corresponding to the base station data must meet the target UE type; The channel conditions include the requirement that the channel type corresponding to the base station data must meet the target channel type; The data capture count condition includes the requirement that the total number of base station data must not exceed the target number of captures.

3. The method according to claim 2, characterized in that, If the target channel type satisfied by the base station data is an uplink channel, then the base station data includes data from the physical layer uplink ingress point and the verification result for the data from the ingress point. If the target channel type of the base station data is a downlink channel, then the base station data includes data from the physical layer downlink egress point.

4. The method according to claim 1, characterized in that, The process involves performing channel data transmission simulation processing on the base station data using an algorithm simulation platform and recording the first set of node data during the channel data transmission simulation process. This first set of node data includes the first node data corresponding to each node in the physical layer link. Additionally, the process involves performing channel data transmission processing on the base station data using an offline verification platform and recording the second set of node data during the channel data transmission process. This second set of node data includes the second node data corresponding to each node in the physical layer link, including: For any base station data, the data transmitted through the channel under the configuration parameters contained in the base station data is decoded to obtain the decoding result of the base station data; If the decoding result indicates decoding failure, the base station data is processed through channel data transmission simulation using an algorithm simulation platform, and the first set of node data during the channel data transmission simulation process is recorded. The first set of node data includes the first node data corresponding to each node in the physical layer link. The base station data is processed through channel data transmission using an offline verification platform, and the second set of node data during the channel data transmission process is recorded. The second set of node data includes the second node data corresponding to each node in the physical layer link.

5. The method according to claim 4, characterized in that, The method further includes at least one of the following: Send the decoding result of the base station data to the test machine; The abnormal location result is sent to the test machine, and the abnormal location result includes the abnormal node.

6. A method for anomaly localization at the physical layer, characterized in that, Applied to a testing machine, the method includes: Send a data capture command to the base station. The data capture command is used to trigger the base station to capture at least one base station data. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters. The system receives base station data fed back by the base station and forwards the base station data to the server, so that the server executes the physical layer anomaly location method according to any one of claims 1 to 5 based on the base station data.

7. The method according to claim 6, characterized in that, Sending the data capture command to the base station includes: A data capture instruction is sent to the base station based on the data capture conditions. The data capture instruction is used to instruct the base station to capture base station data that meets the data capture conditions.

8. The method according to claim 7, characterized in that, The method further includes: Receive the decoding result sent by the server; If the decoding result indicates successful decoding, the step of sending the data capture instruction to the base station based on the data capture condition is repeated until the number of repetitions reaches a preset number of rounds or the received decoding result indicates decoding failure.

9. A method for anomaly localization at the physical layer, characterized in that, Applied to a base station, the method includes: Receive data capture commands sent by the test machine; In response to the data capture command, base station data is captured, wherein the base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters; The test machine forwards the base station data to the server, so that the server executes the physical layer anomaly localization method according to any one of claims 1 to 5 based on the base station data.

10. The method according to claim 9, characterized in that, The data capture command includes data capture conditions, and the step of capturing base station data in response to the data capture command includes: In response to the data capture command, base station data that meets the data capture conditions is captured.

11. A physical layer anomaly localization system, characterized in that, The system includes a test machine, a server, and a base station, wherein: The test machine is used to send a data capture command to the base station; The base station is used to capture base station data by the data capture command and send the base station data to the test machine. The base station data consists of configuration parameters and data transmitted through the channel under the configuration parameters. The test machine is used to send the base station data to the server; The server is configured to perform channel data transmission simulation processing on the base station data through an algorithm simulation platform, and record a first set of node data during the channel data transmission simulation processing. The first set of node data includes the first node data corresponding to each node in the physical layer link. The server is also configured to perform channel data transmission processing on the base station data through an offline verification platform, and record a second set of node data during the channel data transmission processing. The second set of node data includes the second node data corresponding to each node in the physical layer link. If the first node data and the second node data of any node are inconsistent, the node is identified as an abnormal node.

12. A server, characterized in that, The server includes: a memory, a transceiver, and a processor. A memory for storing a computer program; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer program from the memory and executing the steps of the method according to any one of claims 1 to 5.

13. A testing machine, characterized in that, The test machine includes: a memory, a transceiver, and a processor. A memory for storing a computer program; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer program from the memory and executing the steps of the method according to any one of claims 6 to 8.

14. A base station, characterized in that, The base station includes: a memory, a transceiver, and a processor. A memory for storing a computer program; a transceiver for sending and receiving data under the control of the processor; and a processor for reading the computer program from the memory and executing the steps of the method according to any one of claims 9 to 10.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5, 6 to 8, or 9 to 10.

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