A wireless signal sensing method for LTE-U and related devices

Abstract

This application provides an LTE-U wireless signal sensing method and related equipment. This application addresses wireless interference monitoring and spectrum management systems in the complex electromagnetic environment of rail transit, deeply integrating licensed and unlicensed spectrum, known and unknown protocols, real-time sensing and long-term optimization. It provides a systematic solution for highly reliable vehicle-to-ground communication, effectively overcoming the licensed spectrum bottleneck. While retaining 1.8GHz LTE-M as the high-reliability main control channel, it intelligently aggregates unlicensed spectrum such as 5.8GHz as a secondary data channel through LTE-U technology. Without changing the spectrum allocation policy, it effectively increases the system's available bandwidth several times. Through envelope analysis and spectrum sensing, it can identify clean sub-bands in the unlicensed frequency band, effectively avoiding interference and maximizing spectrum utilization.

Description

Technical Field

[0001] This application relates to the field of wireless signal technology, and in particular to an LTE-U wireless signal sensing method and related equipment. Background Technology

[0002] In practice, vehicle-to-ground wireless communication systems in the rail transit industry primarily handle the transmission of critical services such as train operation control. Currently, the industry generally adopts LTE-M (LTE for Metro) technology based on the 1.8GHz licensed frequency band as the wireless bearer. This standard operates in the 1785MHz–1805MHz frequency band, with a total bandwidth of only 20MHz. However, in actual deployments, the 1785MHz–1805MHz frequency band is adjacent to the high-power downlink output band of the operator's Band3 (starting frequency of 1805MHz), which easily leads to an increase in the noise floor of the LTE-M system, causing significant interference to the coverage and reception performance of the wireless signal. To avoid such interference, the industry typically further compresses the available radio frequency bandwidth to 1790MHz–1800MHz (a total of 10MHz), making the already limited transmission bandwidth even more strained. This severely restricts the data transmission capacity of vehicle-to-ground communication and makes it difficult to meet the growing demand for high-bandwidth, low-latency services in future intelligent rail transit.

[0003] Therefore, to overcome the bottleneck of spectrum resources, the industry has begun to focus on the application potential of LTE-U (LTE in Unlicensed Spectrum) technology in the rail transit field. LTE-U technology, through carrier aggregation, retains the original 1.8GHz licensed frequency band as the primary carrier (ensuring the reliability and priority of control services) while aggregating unlicensed frequency bands such as 5.8GHz as auxiliary carriers, thereby significantly improving the overall system data bandwidth. LTE-U typically employs channel sensing mechanisms such as LBT (Listen Before Talk) in unlicensed frequency bands to achieve coexistence with other systems, combining spectrum efficiency and deployment flexibility, and is considered a promising vehicle-to-ground communication enhancement solution. However, in the practical application of LTE-U systems, unlicensed frequency bands (such as 5.8GHz), due to their open and shared nature, are susceptible to co-channel or adjacent-channel interference from different devices, severely affecting communication quality and service reliability. Therefore, real-time and accurate monitoring of the electromagnetic environment in unlicensed frequency bands is particularly crucial. Current communication systems urgently need the capability to automatically detect, identify, and report interference signals in this frequency band, including the extraction and analysis of key parameters such as interference type, frequency point, intensity, and time-domain characteristics. This is crucial for network management systems to implement spectrum optimization or avoidance strategies in a timely manner, ensuring the stable operation of LTE-U services in unlicensed frequency bands. Existing solutions lack a robust dedicated monitoring and intelligent interference identification mechanism for LTE-U unlicensed frequency bands in rail transit environments. They lack a systematic monitoring architecture and closed-loop processing flow, making it difficult to meet the operational and maintenance needs of highly reliable vehicle-to-ground communication systems. Summary of the Invention

[0004] This application aims to at least address one of the aforementioned technical deficiencies. In view of this, this application provides an LTE-U wireless signal sensing method and related equipment to solve the technical deficiencies in the prior art regarding the imperfect special monitoring and intelligent interference identification mechanism for the LTE-U unlicensed frequency band in the rail transit environment.

[0005] A wireless signal sensing method for LTE-U includes: decoding wireless signals corresponding to a preset first frequency band in a target area using a preset first radio frequency link to obtain a first decoding result of the wireless signal in the first frequency band; based on the first decoding result, wherein the first frequency band is a licensed spectrum, analyzing the parameters of the wireless signal in the first frequency band to obtain first analytical information of the parameters of the wireless signal in the first frequency band, the first analytical information including the spectrum parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal in the first frequency band and the wireless signal in a preset second frequency band, wherein the second frequency band is an unlicensed spectrum; and based on the first analytical information, using a second radio frequency link to detect the second frequency band in the target area. The method involves performing quality analysis on the corresponding wireless signals to obtain the aggregation success rate and envelope of the wireless signals in the second frequency band. If there are analyzable wireless signals in the wireless signals corresponding to the second frequency band, these analyzable signals are analyzed to determine the type and spectral parameters of the first wireless interference signal in the second frequency band. Based on the type and spectral parameters of the first wireless interference signal, a first monitoring report of the wireless signals in the target area is generated. Based on the first monitoring report, target perception data corresponding to the target area is analyzed to determine the location information of the wireless interference signal in the second frequency band, wherein the target perception data includes information on the wireless signals at the location to be monitored in the target area. Preferably, the method further includes: when performing quality analysis on the wireless signals corresponding to the second frequency band, if there are unanalyzable unknown signals in the wireless signals corresponding to the second frequency band, these unanalyzable unknown signals are analyzed to determine the envelope of the second wireless interference signal in the second frequency band, and the type and spectral parameters of the second wireless interference signal are determined based on the envelope of the second wireless interference signal. Based on the type and spectral parameters of the second wireless interference signal, a second monitoring report of the wireless signals in the target area is generated. Preferably, the method further includes: determining whether the power intensity of the first wireless interference signal or the second wireless interference signal exceeds a preset first threshold; if there is a target interference signal in the first wireless interference signal or the second wireless interference signal whose power intensity exceeds the preset first threshold, then analyzing the target interference signal to obtain the analysis result of the target interference signal; based on the analysis result of the target interference signal, determining whether the target bandwidth is backed up due to the target interference signal; if the target bandwidth is backed up due to the target interference signal, then identifying the target interference signal to determine the interference type of the target interference signal. Preferably, the method further includes: after determining the interference type of the target interference signal, reporting the wireless interference type of the target interference signal, and determining the interference analysis result of the target area according to the positioning information of the target interference signal.Preferably, identifying the target interference signal to determine its interference type includes: sampling the intermediate frequency signal in the target interference signal to obtain a second target interference signal; performing analog-to-digital conversion on the second target interference signal to obtain a third target interference signal; performing extreme value processing on the filtering in the third target interference signal according to a preset sampling frequency and a preset bandwidth threshold, and determining the center frequency point of the third target interference signal; based on the center frequency point of the third target interference signal, performing a sliding scan from the low frequency band to the high frequency band in a preset target frequency band according to a preset scanning step threshold to determine the envelope of the third target interference signal in the low frequency band to the high frequency band of the target frequency band; normalizing the envelope of the third target interference signal in the low frequency band to the high frequency band of the target frequency band to obtain the normalized processing result of each envelope of the third target interference signal in the target frequency band; and performing a mean square error calculation on the normalized processing result of each envelope of the third target interference signal in the target frequency band and the parameters corresponding to each modulation mode of the third target interference signal in the target frequency band to determine the interference type of the third target interference signal. Preferably, the method further includes: real-time monitoring of channel status information reports issued by terminal devices in the target area; uploading the channel status information reports to the base station so that the base station can monitor the channel status of each terminal device in the target area. Preferably, the preset sampling frequency includes 122.88MHz; the preset bandwidth threshold range is [20MHz, 40MHz]; the preset target frequency band range is [5725MHz, 5850MHz]; and the preset scan step threshold range is set to (0KHz, 1KHz).

[0006] An LTE-U wireless signal sensing device includes: a decoding unit, configured to decode a wireless signal corresponding to a preset first frequency band in a target area using a preset first radio frequency link, to obtain a first decoding result of the wireless signal in the first frequency band; wherein the first frequency band is a licensed spectrum; a parsing unit, configured to parse the parameters of the wireless signal in the first frequency band based on the first decoding result, to obtain first parsing information of the parameters of the wireless signal in the first frequency band, the first parsing information including spectral parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal in the first frequency band and a wireless signal in a preset second frequency band; wherein the second frequency band is an unlicensed spectrum; and a first analysis unit, configured to analyze the parameters of the wireless signal in the second frequency band in the target area based on the first parsing information using a preset second radio frequency link. The system performs quality analysis on the corresponding wireless signals to obtain the aggregation success rate and envelope of the wireless signals in the second frequency band; a second analysis unit is used to analyze the analyzable wireless signals in the wireless signals corresponding to the second frequency band when there are analyzable wireless signals, and determine the type and spectrum parameters of the first wireless interference signal corresponding to the second frequency band; a generation unit is used to generate a first monitoring report of the wireless signals in the target area based on the type and spectrum parameters of the first wireless interference signal; a third analysis unit is used to analyze the target perception data corresponding to the target area based on the first monitoring report to determine the location information of the wireless interference signal in the second frequency band, wherein the target perception data includes information on the wireless signals at the location to be monitored in the target area.

[0007] An LTE-U wireless signal sensing device includes: one or more processors and a memory; the memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of any of the LTE-U wireless signal sensing methods described herein.

[0008] A readable storage medium storing computer-readable instructions, which, when executed by one or more processors, cause the one or more processors to implement the steps of any of the LTE-U wireless signal sensing methods described above.

[0009] As can be seen from the above description, when it is necessary to monitor the wireless signal interference in a target area, this application can use a preset first radio frequency link to decode the wireless signal corresponding to a preset first frequency band in the target area, and obtain a first decoding result of the wireless signal of the first frequency band; then, based on the first decoding result, the parameters of the wireless signal of the first frequency band can be analyzed to obtain the first analysis information of the parameters of the wireless signal of the first frequency band. The first analysis information includes the spectrum parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal of the first frequency band and the wireless signal of the second frequency band; in order to determine the basic interference information of the wireless signal corresponding to the first frequency band, and then, based on the first analysis information, a preset second radio frequency link can be used to analyze the wireless signal interference of the second frequency band in the target area. The signal quality is analyzed to obtain the aggregation success rate and envelope of the second frequency band wireless signals. If there are analyzable wireless signals in the second frequency band, these analyzable signals can be analyzed to determine the type and spectral parameters of the first wireless interference signal in the second frequency band. Based on the type and spectral parameters of the first wireless interference signal, a first monitoring report of the wireless signals in the target area can be generated. The basic information of the second frequency band wireless interference signals in the target area is determined. Based on the first monitoring report, the target perception data corresponding to the target area is analyzed to determine the location information of the second frequency band wireless interference signals, and finally, the location information of the specific wireless interference signals in the target area.

[0010] Therefore, this application addresses the wireless interference monitoring and spectrum management system in the complex electromagnetic environment of rail transit. It integrates licensed and unlicensed spectrum, as well as known and unknown protocols, to provide a solution for reliable vehicle-to-ground communication, effectively overcoming the bottleneck of licensed spectrum. While retaining 1.8GHz LTE-M as the reliable main control channel, it aggregates unlicensed spectrum such as 5.8GHz as a secondary data channel through LTE-U technology. Without changing the spectrum allocation policy, it increases the available bandwidth of the system, enabling high-bandwidth services such as high-definition video backhaul and in-vehicle IoT. Through envelope analysis and spectrum sensing, it can identify clean sub-bands within the unlicensed frequency band. Employing techniques such as spectrum puncturing and dynamic channel selection, it effectively avoids interference without abandoning entire frequency bands, maximizing spectrum utilization and avoiding resource waste. It constructs a multi-layered defense system, identifying unknown interference through blind signal analysis to establish the first line of defense. Based on CSI reports and interference type identification, it achieves adaptive modulation and coding, beamforming, and fast retransmission. Through interference location and reporting, it triggers network-level collaborative avoidance and physical frequency clearing. By prioritizing the primary carrier (licensed frequency band) and employing a dynamic service offloading mechanism, the system ensures that safety-critical services such as train control enjoy the highest priority and stable channels. When the secondary carrier is interfered with, the system can seamlessly fall back to the primary carrier. The entire process is automated, from signal detection, feature extraction, type identification, impact assessment to strategy generation, all completed automatically by the system, reducing reliance on experienced maintenance personnel and improving response speed. Furthermore, it provides comprehensive environmental awareness through the first and second radio frequency links, and generates optimized strategies based on interference type, intensity, and location, automatically adjusting carrier aggregation, power, and scheduling parameters. The continuous accumulation of structured monitoring reports and interference feature databases enables the system to learn environmental patterns and achieve predictive interference avoidance. This not only solves the specific problems of insufficient bandwidth and excessive interference, but also changes the survival and development model of rail transit vehicle-to-ground communication systems, making them a reliable and powerful digital nervous system supporting the future development of smart rail. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a wireless signal sensing method for implementing LTE-U provided in this application; Figure 2 This is a schematic diagram of an LTE-U wireless signal sensing system architecture as an example of this application; Figure 3 This is a schematic diagram of another LTE-U wireless signal sensing system architecture as an example of this application; Figure 4 This is a schematic diagram illustrating the structure of an LTE-U wireless signal sensing device as an example of this application; Figure 5 This is a hardware structure block diagram of an LTE-U wireless signal sensing device disclosed in this application. Detailed Implementation

[0013] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, and not all 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.

[0014] The method provided in this application can be used in a variety of general-purpose or special-purpose computing device environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor devices, distributed computing environments including any of the above devices, etc. This application provides an LTE-U wireless signal sensing method, which can be applied to various wireless signal management systems, as well as to various computer terminals or smart terminals. The executing entity can be the processor or server of the computer terminal or smart terminal.

[0015] The following is combined with Figure 1 This application describes the flowchart of the LTE-U wireless signal sensing method. Figure 1 As shown, the process may include the following steps:

[0016] Step S101: Use a preset first radio frequency link to decode the wireless signal corresponding to the preset first frequency band in the target area to obtain the first decoding result of the wireless signal of the first frequency band.

[0017] Specifically, in practice, simple energy detection (such as measuring RSSI or noise floor) can only determine that there is noise / interference in the target area. If a standard frame structure, synchronization signal, and control information can be successfully decoded using an LTE-U or WiFi demodulator, it can be determined that this is a legitimate communication signal. If the frame structure of any known communication protocol cannot be decoded at all, it can be determined to be non-communication noise interference or off-protocol interference. More proactive avoidance or investigation of physical interference sources is needed. Different interference sources have distinct fingerprints in the time domain, frequency domain, and modulation domain. Therefore, in order to analyze whether there is interference in the wireless signal in the target area, a preset first radio frequency link can be used to decode the wireless signal corresponding to a preset first frequency band in the target area to obtain the first decoding result of the wireless signal in the first frequency band. The decoding process can extract these fingerprints. For example, decoding will identify the specific preamble, OFDM symbol structure, and MAC frame header of 802.11a / n / ac / ax. It is possible to further distinguish between beacon frames, data frames, and management frames, and possibly resolve their BSSID (AP MAC address), thereby locating the identity of the interference source. It can also identify FHSS (frequency hopping) modes and specific GFSK modulation characteristics. It can also decode LTE PSS / SSS synchronization signals, and even PCI (Physical Cell ID), to determine whether the signal originates from a neighboring vehicle or another system's LTE-U signal. Sometimes decoding may fail, but combined with spectrum analysis, it can be found that the energy is concentrated in an extremely narrow frequency band, which could be from wireless cameras, sensors, etc. If decoding fails, but periodic bursts of high-energy pulses are observed in the time domain, this could originate from industrial equipment such as motors or frequency converters.

[0018] The first decoding result is a series of structured, analyzable data. For example, successful decoding may allow for the identification of the interference source, tracing, and network coordination. If the signal strength (RSRP / RSSI) is decoded, the interference intensity can be quantified. If the signal quality (SINR, CQI) is decoded, the impact of the interference on communication can be quantified. The decoding rate can also be used to estimate the channel occupancy of the other party and determine its activity level. If decoding fails, the bit error rate or block error rate can be used to quantify the destructive power of the interference. The location of synchronization failure can indicate the protocol layer affected by the interference. Soft information during the decoding process can depict specific interference patterns (such as phase noise, amplitude compression, etc.). The preset first radio frequency link can be a hardware receiving channel specifically configured for monitoring purposes, independent of or partially shared with the main service link. It is pre-configured with demodulation and decoding parameters (such as center frequency, bandwidth, and standard) and can perform diagnostic tasks 24 / 7 without affecting the main link's services. The first preset frequency band can be a licensed frequency band. In the LTE-U scenario, this frequency band refers to the unlicensed frequency band that needs to be protected and is susceptible to interference (such as the 5.8GHz ISM band).

[0019] Step S102: Based on the first decoding result, the parameters of the wireless signal in the first frequency band are analyzed to obtain the first analysis information of the parameters of the wireless signal in the first frequency band.

[0020] Specifically, the first decoding result might simply be a Boolean value indicating successful or failed decoding, or a bunch of raw soft bit information. Therefore, to identify and locate the source of interference and understand the wireless signal interference situation in the target area, the parameters of the wireless signal in the first frequency band can be further analyzed based on the first decoding result to obtain the first analytical information of the wireless signal parameters in the first frequency band (i.e., the specific frequency point and bandwidth information of the licensed frequency band). In practice, simply knowing that there is a WiFi signal in the target area is insufficient; it is necessary to know which WiFi network it is. For LTE / LTE-U signals, parsing the PCI can determine the Physical Cell ID. If an unknown PCI or an excessively strong PCI signal from a neighboring cell is detected, it can be determined as cross-coverage interference or illegal private installation of equipment. Parsing the PLMNID can determine the Public Land Mobile Network identifier, identifying whether it is one's own network, a competitor's network, or a test network. For WiFi signals, parsing the BSSID / SSID can determine the MAC address and network name of the AP (master station). For Bluetooth / Zigbee, etc., the MAC address or network ID can be parsed. Once a specific interference source is identified, the network management system can add it to the list of known interference sources for long-term monitoring and strategic avoidance. Determining the first analytical information quantifies the intensity and impact of the interference; the strength of the successfully decoded signal itself is the interference strength. Analyzing RSRP / RSSI accurately measures the absolute received power of the interfering signal. Analyzing SINR / CQI measures the signal-to-interference-plus-noise ratio and channel quality while successfully decoding the signal. Analyzing the bit error rate directly measures the degree of damage caused by interference to data transmission. Determining the first analytical information allows for interference classification (mild, moderate, severe) and serves as a key input for selecting response strategies. Analyzing service channel occupancy / load can estimate the activity of the opposing network by decoding and analyzing PDCCH / PUSCH grants or WiFi NAV (Network Assignment Vector). Analyzing the signal's time-domain characteristics allows for analysis of the signal's temporal patterns (periodic pulses, random bursts, sustained full occupancy) through continuous decoding. This can distinguish between motor interference, radar interference, and normal data packets. Analyzing the transmission's QoS characteristics allows for analysis of data packet size and intervals to infer the service type (video streaming, web browsing, or heartbeat packets). The first parsed information may include the spectral parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signals in the first frequency band and the second frequency band. The first frequency band may be a licensed frequency band, and the second frequency band may be an unlicensed frequency band.

[0021] Step S103: Based on the first parsing information, the wireless signal corresponding to the second frequency band in the target area is analyzed using a preset second radio frequency link to obtain the aggregation success rate of the wireless signal in the second frequency band and the envelope of the wireless signal in the second frequency band.

[0022] Specifically, by decoding and analyzing the wireless signals corresponding to the first radio frequency link, the type, intensity, and behavior of the interference source can be identified. To further understand the network status of the wireless signal system in the target area, based on the first analysis information, a preset second radio frequency link (LTE-U unlicensed frequency band) can be used to perform quality analysis on the wireless signals corresponding to the preset second frequency band in the target area, obtaining the aggregation success rate of the second frequency band's wireless signals and the envelope of the first wireless interference signal in the second frequency band. In wireless environments, especially shared unlicensed frequency bands, the results of a single analysis may become completely invalid within milliseconds. In practice, the traffic load of a WiFi AP is sudden, potentially changing from idle to full-load download in an instant, with its duty cycle and transmit power dynamically changing. In rail transit scenarios, interference sources (such as passenger WiFi hotspots and onboard equipment) may move at high speed with the train, causing their path loss, Doppler shift, and angle of arrival relative to the base station to change rapidly. New devices may be powered on and connected to the network at any time, and new services may be launched at any time. Therefore, only by continuously driving the second link to perform quality analysis based on the latest first-level analysis information can we avoid decision-making errors (such as aggregation failure and service interruption) caused by information lag. The initially formulated spectrum strategy (such as selecting a certain channel or adopting a certain LBT parameter) may not be optimal, and may even be wrong. The ultimate goal of the system is not to eliminate interference, but to ensure the reliable transmission of critical services (such as train control signals) in a high-interference environment. The aggregation success rate is a direct reflection of the user's perceived quality. Continuously extracting and updating the interference envelope (especially time-domain and frequency-domain characteristics) can predict the development trend of interference. For example, continuous monitoring may reveal that the intensity of a certain pulse interference is regularly increasing, and its envelope indicates that it is about to enter an active period. The system can proactively switch critical services back to pure licensed frequency band transmission before the active period arrives, achieving predictive maintenance-level reliability assurance. The system changes from passively responding to alarms to proactively and forward-lookingly managing spectrum resources to ensure service SLAs. This enables the vehicle-to-ground communication system to truly meet the stringent requirements of intelligent rail transit for high reliability, high bandwidth, and low latency.

[0023] Step S104: If there are analyzable wireless signals in the wireless signals corresponding to the second frequency band, then analyze the analyzable wireless signals in the wireless signals corresponding to the second frequency band to determine the type and spectrum parameters of the first wireless interference signal corresponding to the second frequency band.

[0024] Specifically, in wireless communication systems (such as Wi-Fi, 5G, radar, Bluetooth, etc.), different devices operate in different frequency bands. As mentioned above, the second frequency band is an unlicensed band, with complex and diverse sources of interference. It may originate from similar devices (co-channel interference) or from dissimilar devices (heterogeneous interference). In unlicensed bands, the signals that can be analyzed are usually signals with known protocols (e.g., Wi-Fi beacon frames, Bluetooth broadcast packets, etc.). If the goal is to identify the primary wireless interference signal, and Wi-Fi is the source, then analyzing the Wi-Fi signal directly yields the interference type and parameters. If the known signal is not the source of interference but rather the object being interfered with, then by analyzing its reception state changes, the characteristics of other interference signals that cannot be directly analyzed can be inferred. For example, a Wi-Fi AP (analyzable signal) may suddenly experience a drop in reception performance. Analyzing its received data frames reveals that the increased frame error rate is concentrated in certain specific time windows. Combining spectrum sensing, a periodic broadband burst signal can be identified within these time windows. From this, it can be inferred that the interference signal type may be a wireless camera, and its spectral parameters (period, bandwidth, power, etc.) can be obtained. For example, the reception quality of a known signal (RSSI, signal-to-noise ratio, bit error rate, retransmission rate) is affected by interference. By monitoring the changing patterns of these parameters, the time-domain characteristics (continuous, bursty, periodic, etc.) and frequency-domain occupancy (narrowband, wideband) of the interfering signal can be inferred. Another example is analyzing a Wi-Fi beacon frame and finding that its declared channel is 6, but during actual reception, a large amount of energy is detected on channel 6 that is not in Wi-Fi format. This indicates that a non-Wi-Fi interference exists on that channel. Combining energy detection with the channel information of the known signal, the interfering frequency band can be located. Furthermore, multiple signals often coexist in unlicensed frequency bands. Spectrum analyzers can only see the energy but do not know the protocol of each energy block. By analyzing the resolvable portion, the influence of known signals can be eliminated, and the remaining energy / features may originate from unknown interfering signals.

[0025] For example, in practice, when troubleshooting Wi-Fi network performance degradation, maintenance personnel detected Wi-Fi signals (analyzable) in the 5 GHz band (second band). Analyzing the Wi-Fi signal revealed a large number of uplink frame retransmissions from certain terminals, concentrated around 5.2 GHz. Further spectrum analysis revealed a constant narrowband carrier during Wi-Fi frame transmission. Therefore, the primary type of wireless interference signal could be identified as a fixed wireless transmission device illegally occupying the frequency band, with spectral parameters of a 5.21 GHz center frequency, a bandwidth of 1 MHz, and relatively high power. Without first analyzing the Wi-Fi signal, it might be impossible to quickly pinpoint the specific pattern and timing of the interference.

[0026] In unlicensed frequency bands, due to the high diversity of devices, interference sources can be any civilian equipment, or even non-communication devices. Unlike base stations, there is no unified management, so interference troubleshooting must rely on active detection and analysis. Analyzing known signals is the lowest-cost and most direct entry point for interference diagnosis, as many devices (such as WiFi access points) provide detailed reception statistics. Therefore, in unlicensed frequency bands, although the signals are cluttered, the measurable signals can be used to directly identify whether a signal itself is an interference source, or the characteristics of interference sources that cannot be directly analyzed can be indirectly inferred by observing the interference behavior of the signal. Therefore, if there are measurable wireless signals in the second frequency band, these measurable wireless signals can be analyzed to determine the type and spectral parameters of the first wireless interference signal corresponding to the second frequency band.

[0027] Step S105: Based on the type and spectrum parameters of the first wireless interference signal, generate a first monitoring report of the wireless signal in the target area.

[0028] Specifically, decoding, parsing, and envelope analysis are primarily used to identify interference, adjust parameters, and ensure current communication. After determining the type and spectrum of the first wireless interference signal, a first monitoring report of the wireless signal in the target area is generated based on the type and spectrum parameters of the first wireless interference signal. Generating this first monitoring report allows for the structured and standardized archiving of scattered real-time data for accident analysis, airspace planning, rule revision, and as a legal basis. This enables standardized recording and historical tracing of the situation. In practice, the wireless environment is dynamic, but management requires static, queryable records. The signal characteristics (type, spectrum, intensity) generated in the first monitoring report are constantly changing. The monitoring report binds this instantaneous data with contextual information such as timestamps, geographical locations (e.g., track kilometer markers), and cell IDs to form a complete event archive. For systems like rail transit that operate along fixed lines, specific types of interference that repeatedly occur at a specific location have extremely high reproducibility. After the reports are collected, big data analysis can be performed. For example, the analysis report might find that the 5.8GHz band is severely affected by WiFi interference in commercial areas, but relatively clean in tunnel areas. This can guide future network design: cautiously enabling or downgrading LTE-U in commercial areas, while actively using LTE-U as a capacity supplement in tunnel areas. The report demonstrates that, at a specific time and location, the LTE-U system transmitted signals only after assessing the environment (detecting the presence of WiFi), and may record key compliance parameters such as transmit power and duty cycle. When complaints arise regarding interference between the LTE-U system and other services, the monitoring report is the most authoritative basis for determining responsibility. The report can automatically trigger maintenance work orders. For example, if multiple reports continuously record illegal high-power interference at the same location, the system can automatically generate a work order, notifying spectrum maintenance personnel to physically clear the frequency at that location. For example, by combining video data to discover that interference consistently occurs during the business hours of specific merchants, collaboration with city management departments can be used to address the electromagnetic environment in commercial areas. Generating the first monitoring report marks the formal closure of the single interference incident handling process and is also the starting point for building long-term system intelligence.

[0029] Step S106: Based on the first monitoring report, analyze the target perception data corresponding to the target area to determine the location information of the wireless interference signal in the second frequency band.

[0030] Specifically, after determining the first monitoring report, the target perception data corresponding to the target area can be analyzed based on the first monitoring report to determine the location information of the wireless interference signal in the second frequency band. The location information of the wireless interference signal in the second frequency band can include the location, frequency, maximum power, and MSE confidence level of the communication signal of the first wireless interference signal. Determining the location information of the wireless interference signal in the second frequency band enables physical eradication and source control of the interference. For example, the monitoring report repeatedly shows that there is a high-intensity illegal broadband interference signal in the downlink direction between People's Square Station and Nanjing East Road Station on Metro Line X. Without location information, the system can only continuously adopt avoidance strategies within this section. With location information, by analyzing the signal strength, angle of arrival, and time of arrival data reported by multiple base stations or vehicle-mounted sensing terminals, the source can be located to a camouflaged device on the 5th streetlight pole outside Exit 3 of Nanjing East Road Station or an office on the 18th floor of the XX Building along the line. Maintenance personnel can carry portable positioning equipment for precise on-site positioning and, in conjunction with the Radio Management Committee or the public security department, physically dismantle the device, thereby eliminating the interference source once and for all. The system predicted the interference source as "merchant WiFi" and initially determined it to be located on the station hall level. Through location tracking, it was ultimately confirmed that it was indeed the wireless router of convenience store A on the station hall level. This verified the accuracy of the model that infers the type of interference from signal characteristics. The long-term accumulated interference source location map allows for proactive avoidance of known areas of strong interference or the early deployment of interference cancellation facilities.

[0031] Target perception data refers to multi-dimensional data collected from a spatial dimension that can be used for positioning. It typically requires the collaborative work of multiple monitoring points. For example, target perception data may include information about the wireless signals at the location to be monitored within the target area. Multiple sensing nodes (which can be dedicated sensors or even the train itself acting as a mobile sensing node) distributed in different locations measure the arrival direction of the same interference signal. When analyzing target data, these direction lines can be intersected on a map; the intersection point indicates the possible location of the interference source. Accuracy depends on the angular measurement accuracy of the antenna array and the node deployment geometry. Secondly, multiple strictly time-synchronized nodes record the precise arrival time of the same interference signal pulse. When analyzing target data, the time difference of signal arrival at different nodes can be calculated; each time difference corresponds to a hyperbola, and the intersection of multiple hyperbolas indicates the location of the interference source. Thirdly, the received power of the same interference signal measured by multiple nodes. When analyzing target data, known wireless propagation models can be combined, and through fingerprint matching or model fitting, the location of the signal source most likely to produce this power distribution can be deduced. Therefore, when a report indicates the presence of new, strong, or malicious interference, the system automatically initiates a high-precision multi-node collaborative positioning process. The system needs to accurately match and separate the specific signal requiring location from a massive amount of ambient radio waves. For the LTE-U system in rail transit, achieving interference location means upgrading the way interference is resolved from indefinite wireless avoidance to one-time physical elimination. This ensures that all sophisticated signal analysis and network optimization can ultimately be translated into effective action against physical sources of interference, truly guaranteeing the stability of the vehicle-to-ground communication lifeline in complex electromagnetic environments.

[0032] In practical applications, when performing quality analysis on wireless signals corresponding to the second frequency band, if there are unresolved unknown signals in the wireless signals corresponding to the second frequency band, a preset analysis method is used to analyze the envelope corresponding to the unresolved unknown signals in the wireless signals corresponding to the second frequency band. This is to determine the envelope of the second wireless interference signal corresponding to the unresolved unknown signals in the wireless signals corresponding to the second frequency band, and to determine the type and spectral parameters of the second wireless interference signal based on the envelope of the second wireless interference signal. Based on the type and spectral parameters of the second wireless interference signal, a second monitoring report of the wireless signals in the target area is generated.

[0033] Specifically, in practice, the system may encounter undecipherable unknown signals. These signals may be based on proprietary protocols or may not have any protocols at all, such as analog signals without protocols. The system may be unable to decode these unknown signals. Unknown signals often imply higher risks. The potential harm of unknown signals is the highest. For example, unknown signals may be malicious interference, or jammers specifically designed to obstruct communications. Their signals often deliberately do not follow any public protocols to avoid easy identification and countermeasures. They may also be high-priority private network signals or encrypted signals for emergency communications. Their high priority means that the LTE-U system must actively and thoroughly avoid them; otherwise, it may cause serious cross-system interference or even security incidents. They may be strong stray radiation generated by faulty or illegal equipment, or non-standard industrial equipment. Their signal characteristics are strange and cannot be deciphered by standard protocols. The threat level must be determined by analyzing their physical layer envelope. When the decoder fails, the system must retreat to a lower-level signal processing layer. The envelope of the second type of wireless interference signal is the physical layer fingerprint. Performing time-domain envelope analysis on it can reveal its specific type. Frequency domain envelope analysis reveals the shape of the power spectral density. Determining the type and spectrum is a feature-based classification. By comparing with a pre-defined interference feature library, extracted envelope features can be mapped to a general type label. For unknown interference, generating a report is more important than for known interference. The second monitoring report, when labeling the type of the second wireless interference signal, will mark it as an unknown signal and include a predicted type based on blind analysis. The second monitoring report will also record detailed envelope characteristics (pulse width, period, bandwidth, spectral shape), signal statistical characteristics (peak-to-average power ratio, cyclic spectrum), and can provide a threat level based on signal strength, duty cycle, bandwidth, and pre-defined rules. The second monitoring report will notify maintenance personnel with higher priority to initiate manual spectrum analysis, on-site investigation, or joint reconnaissance and location with radio management agencies. Each successful analysis and report of an unknown signal supplements the system's threat feature library. When the same unknown signal reappears, the system can more quickly identify it as a previously recorded but unanalyzed signal type.

[0034] In practice, after determining the relevant information of the first and second wireless interference signals, it is also possible to determine whether the power intensity of the first or second wireless interference signal exceeds a preset first threshold. If there is a target interference signal in the first or second wireless interference signal whose power intensity exceeds the preset first threshold, the target interference signal is analyzed to obtain the analysis result. Based on the analysis result of the target interference signal, it is determined whether the target bandwidth is backed up due to the target interference signal. If the target bandwidth is backed up due to the target interference signal, the target interference signal is identified to determine the type of interference.

[0035] Specifically, to facilitate the critical decision-making process from phenomenon perception to impact assessment and root cause diagnosis, after determining the relevant information of the first and second wireless interference signals, it is further possible to determine whether the power intensity of either the first or second wireless interference signal exceeds a preset first threshold. If a target interference signal with a power intensity exceeding the preset first threshold exists in either the first or second wireless interference signal, the target interference signal is analyzed to obtain the analysis results. Based on the analysis results of the target interference signal, it is determined whether the target bandwidth is backed up due to the target interference signal. If the target bandwidth is backed up due to the target interference signal, the target interference signal is identified to determine its interference type. Interference signals in the electromagnetic environment are massive, but not all interference is worth consuming valuable computing resources and network adjustment overhead for in-depth processing. The preset first threshold can be set to -70dBm. Setting a first threshold categorizes interference signals into two main types: low-power interference (weak energy, possibly originating from distant devices or low-power components, with an impact on the current communication link within a tolerable range (e.g., minimal SINR drop. The system may only log or employ mild adaptive measures (e.g., slight coding adjustments)) and high-power interference (target interference signals) (powerful energy, sufficient to cause severe blocking, saturation, or decoding failure in the receiver. This type of interference is the primary concern that must be addressed immediately). By setting a preset threshold, the system achieves precise allocation of processing resources, initiating complex analysis only for signals that truly pose a threat, ensuring the system's real-time performance and efficiency. Detecting a strong interference signal does not necessarily mean the communication link has failed. In wireless communication (especially in shared bands such as LTE-U), a common adaptive strategy when strong interference is detected is to proactively reduce the bandwidth used. For example, if a system was originally using 20MHz bandwidth, to avoid collisions with a strong narrowband interference, it might automatically shut down the affected 5MHz subcarriers, using only the remaining 15MHz bandwidth for transmission. Determining whether the target bandwidth has been avoided directly and objectively demonstrates that the strong interference has forced the system to make performance-sacrificing compromises. Not all strong interference will lead to backoff. A constant strong narrowband interference may trigger backoff; while a transient strong pulse interference may result in the system choosing not to backoff. If WiFi is identified, channel negotiation or handover can be attempted. If radar is identified, due to its high priority, strict time-domain avoidance must be performed. If an illegal jammer or faulty device is identified, location and physical removal procedures need to be initiated. After identifying the type of interference, the optimal algorithm parameters (such as filter parameters and LBT parameters) preset for that type of interference can be invoked. This avoids overreacting to weak interference and underreacting to strong interference. The system only backs off when necessary, and after backoff, it can quickly find the optimal next operating point based on the interference type.

[0036] Specifically, after determining the interference type of the target interference signal, this application can also report the radio interference type of the target interference signal and determine the interference analysis results of the target area according to the location information of the target interference signal. For complex systems like rail transit, the termination of an interference event does not depend on the communication equipment adjusting its parameters, but on the elimination or controllable management of the potential risks of this interference to the entire network operation. Therefore, after determining the interference type of the target interference signal, the radio interference type of the target interference signal can be reported, and the interference analysis results of the target area can be determined according to the location information of the target interference signal. The interference perceived by a single base station or vehicle terminal is a local view. The value of reporting type and location can construct a global electromagnetic situation map. The network management center collects the four-tuple information (interference type, location, intensity, time) reported by all nodes and can present the distribution, type hotspots, and movement trajectories of the entire network interference in real time on a digital twin map. For example, if the center finds that multiple trains have reported the same type of illegal jamming signal at different locations along the line, connecting them on the map through location information may reveal that the source of the interference is a private vehicle moving along the track. This is a conclusion that cannot be drawn from a single node.

[0037] The process of identifying target interference signals and determining their type can begin by sampling the intermediate frequency (IF) signal within the target interference signal. This involves converting the high-frequency radio frequency (RF) signal to a fixed, lower IF for easier subsequent processing. This is a standard receiver architecture. Specifically, the high-frequency RF signal of the target interference signal can be down-converted to an IF signal before sampling, thus obtaining the second target interference signal. The sampled data can be over 100kHz. Next, analog-to-digital conversion (A / D) is performed on the second target interference signal, converting the continuous analog signal into a discrete digital signal. This is the foundation for all subsequent digital signal processing (such as filtering and Fourier transform), resulting in the third target interference signal. The obtained third target interference signal is a digital sequence that can be used for calculations. To capture the core frequency of the signal, the filtering of the third target interference signal can be further optimized by taking extreme values ​​based on a preset sampling frequency and a preset bandwidth threshold, thus determining the center frequency of the third target interference signal. The center frequency is one of the most stable features of the signal. Preliminary filtering removes out-of-band noise and irrelevant components, focusing on the main energy portion of the signal. Extreme value extraction is a peak detection algorithm that finds the point with the highest energy in the frequency domain or the time domain envelope after specific filtering; the corresponding frequency is determined as the center carrier frequency of the signal. Then, based on the center frequency of the third target interference signal, a sliding scan is performed from low to high frequencies within a preset target frequency band, according to a preset scanning step threshold, to determine the envelope of the third target interference signal in the target frequency band from low to high frequencies. To obtain the spectral shape of the third target interference signal, using the center frequency as a reference, the signal is slid from low to high within the target frequency band in small steps, extracting the energy of the signal at each tiny frequency point or narrow band, forming an envelope. This contains richer information than simply the center frequency and bandwidth. The shape of the envelope is a key fingerprint for distinguishing signal types. For example, radar pulses typically have narrow, sharp spectral lines. OFDM signals (such as WiFi and LTE) have approximately rectangular spectra, but with fluctuations. Analog FM signals have wider spectra, and their shape depends on the modulation content. Narrowband digital signals have more concentrated spectra. The envelope of the third-target interference signal from the low to high frequency bands of the target frequency band is then normalized (e.g., the maximum energy of the entire envelope is set to 1, or zero-mean, unit-variance processing is applied) to eliminate absolute intensity and focus on shape characteristics, thus obtaining the normalized results of each envelope of the third-target interference signal in the target frequency band. In practice, the received power of the interference signal is greatly affected by distance and fading. Paying attention to the normalization results of each envelope ensures the stability of subsequent mode matching and prevents misjudgment due to fluctuating signal strength. Finally, the normalized results of each envelope of the third target interference signal in the target frequency band are compared with the parameters corresponding to each modulation mode of the third target interference signal in the target frequency band to perform mean square error calculation, so as to determine the interference type of the third target interference signal.The normalized measured envelope is compared with the ideal or typical parametric envelope models of various modulation schemes (such as 2FSK, 4FSK, QPSK, 16QAM, FM, etc.) in the preset template library using mean square error (MSE). The MSE measures the shape difference between the measured envelope and each template. The template with the smallest difference is identified as the most likely interference type. When facing proprietary, encrypted, or unknown protocols, protocol parsing is completely useless. In this case, physical layer feature analysis is the only means. Normalization and MSE comparison provide objective and quantifiable decision criteria, avoiding subjective misjudgments. A lighter envelope shape matching method is used. A fine envelope is obtained through sliding scanning and then matched with parametric templates, reducing the difficulty of real-time processing while ensuring a certain level of recognition accuracy. The output interference type is not just a text label (such as QPSK); it implicitly includes inferences about signal bandwidth, modulation scheme, and possible uses. Combined with the center frequency and envelope obtained in the previous steps, this information is sufficient for the network management system to make advanced decisions. This approach effectively bypasses the need to understand the semantics (data content) of signals, instead relying on precise measurement and pattern matching of their physical characteristics. In scenarios with unknown protocols, it provides the most crucial technical basis for identifying interference sources and subsequent precise control. Specifically, the preset sampling frequency can include 122.88MHz; the preset bandwidth threshold range is [20MHz, 40MHz]; the preset target frequency band range is [5725MHz, 5850MHz]; and the preset scan step threshold range is set to (0KHz, 1KHz).

[0038] For example, the method for identifying interference signals can be as follows: 1) Perform AD intermediate frequency sampling. More than 100 sets of data need to be sampled to improve signal quality and ensure sampling integrity; 2) According to the sampling frequency of 122.88MHz, the AD chip first targets filtering, which means taking the center frequency of the 5G unlicensed band in LAA as the frequency point and the bandwidth of the preset 20 / 40MHz as the bandwidth, and taking the extreme value to analyze the center frequency of the interference signal through the extreme value; 3) Obtain the envelope of the interference signal by sliding scan from low frequency to high frequency from the target frequency band, with a scan step of 1kHz [TT1.1][GT1.2]; 4) After normalization according to the envelope model, perform MSE operation with AM, DSB, SSB, FM, 2ASK, 2FSK, and 2FSK to obtain the corresponding interference type.

[0039] Preferably, this application can also monitor the channel state information reports sent by terminal devices in the target area in real time; and upload the channel state information reports (CSI reports) to the base station so that the base station can monitor the channel state of each terminal device in the target area. This is the core mechanism to achieve high reliability, high efficiency and adaptability.

[0040] In MIMO multi-antenna systems, PMI and RI in the CSI report are crucial information. RI determines how many independent data streams the current channel can support. The base station then decides how many data streams to transmit simultaneously. PMI reports to the base station which specific phase and amplitude combinations can drive the system's multiple antennas. Essentially, this means the terminal is guiding the base station to form a precise beam towards it. Without a precise PMI, beamforming is blind and may cause interference; without an accurate RI, spatial multiplexing cannot be performed, and the capacity advantage of the multi-antenna system cannot be realized. In LTE-U scenarios, the CSI report is core to interference coordination and avoidance to ensure core services. Terminal-reported SINR drops and CQI degradation are the most direct and reliable evidence, more convincing than the base station's self-measured noise floor. When the base station receives simultaneous reports of a sudden drop in CQI from multiple terminals on a secondary carrier (5.8GHz), it can immediately determine that the frequency band is experiencing broad-spectrum interference, thus triggering a carrier aggregation strategy switch. In practice, if all terminals in a fixed area consistently report low CQI, it indicates a coverage gap in that area. By combining geographical location information, long-term CSI data can be used to create channel quality maps and interference hotspot maps for the entire line, providing data support for network planning, antenna adjustment, and interference source identification. Specifically, the reported type of wireless interference is analyzed based on the interference signal's location, frequency, maximum power, and MSE confidence level, thus completing the characterization analysis of the interference signal. Therefore, for rail transit communication, CSI reports for the licensed frequency band (1.8GHz) ensure absolute reliability of the control plane; while CSI reports for the unlicensed frequency band (5.8GHz) reflect the real-time service availability in a shared environment.

[0041] For example, through Figure 2 and Figure 3 The system architecture diagram shown is from Figure 2The system comprises two radio frequency (RF) links. RF link 1 decodes the wireless signal corresponding to the licensed frequency band, while RF link 2 analyzes the wireless signal quality of the LTE-U unlicensed frequency band, determining the LTE-U aggregation success rate and the envelope of wireless interference signals in the LTE-U unlicensed band. The system identifies the type and spectrum of the interference signal through its envelope and pushes the sensed data, carrying location information, to the backend for wireless interference signal localization. The backend analyzes the data from the front-end sensing devices, assessing whether the wireless signal is interfered with and providing information about the interference signal. For demodulated signals in the interference signal, a WiFi protocol parsing module is used to parse the wireless parameters. For unparseable proprietary protocol signals and unknown signals, or for analyzing wireless interference signals, a variable sampling bandwidth approach is used. The sampling bandwidth is adjusted according to the interference signal bandwidth, and digital filters are employed for frequency shifting and filtering to obtain the corresponding interference signal envelope. The signal type is then analyzed based on the envelope signal. Signals above -70dBm require analysis because the LBT signal monitoring threshold is -70dBm. Above -70dBm, the LAA specification stipulates that the channel is occupied; below -70dBm, the channel is idle and can be used for carrier aggregation. The sensing data refers to the data acquired by front-end sensing devices, either vehicle-mounted or fixedly deployed. This front-end sensing is combined with back-end analysis to form intelligent analysis of wireless signals in LTE-U, a new technology. Analysis of LTE-U is crucial because the unlicensed frequency bands in LTE-U are contentious. If other wireless signals exist with a strength exceeding -70dBm, the unlicensed frequency bands in LTE-U cannot be used. Therefore, real-time monitoring and back-end analysis are necessary. The resulting wireless signal information guides maintenance personnel in timely management of unlicensed frequency bands, improving channel utilization.

[0042] The LTE-U wireless signal sensing device provided in this application will be described below. The LTE-U wireless signal sensing device described below corresponds to the LTE-U wireless signal sensing method described above. See also... Figure 4 , Figure 4 This is a schematic diagram of the structure of an LTE-U wireless signal sensing device disclosed in this application. Figure 4As shown, the LTE-U wireless signal sensing device may include: a decoding unit 101, configured to decode a wireless signal corresponding to a preset first frequency band in a target area using a preset first radio frequency link, to obtain a first decoding result of the wireless signal of the first frequency band; a parsing unit 102, configured to parse the parameters of the wireless signal of the first frequency band based on the first decoding result, to obtain first parsing information of the parameters of the wireless signal of the first frequency band, the first parsing information including the spectrum parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal of the first frequency band and the wireless signal of a preset second frequency band; and a first analysis unit 103, configured to perform quality analysis on the wireless signal corresponding to the second frequency band in the target area using a preset second radio frequency link based on the first parsing information. The analysis yields the aggregation success rate and envelope of the wireless signals in the second frequency band. A second analysis unit 104, when a parseable wireless signal exists in the wireless signal corresponding to the second frequency band, analyzes the parseable wireless signal to determine the type and spectral parameters of the first wireless interference signal corresponding to the second frequency band. A generation unit 105, based on the type and spectrum of the first wireless interference signal, generates a first monitoring report of the wireless signal in the target area. A third analysis unit 106, based on the first monitoring report, analyzes the target sensing data corresponding to the target area to determine the location information of the wireless interference signal in the second frequency band, wherein the target sensing data includes information about the wireless signal at the location to be monitored in the target area. The specific processing flow of each unit in the LTE-U wireless signal sensing device described above can be found in the preceding section on the LTE-U wireless signal sensing method, and will not be repeated here.

[0043] The LTE-U wireless signal sensing device provided in this application can be applied to LTE-U wireless signal sensing equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 5 The hardware structure block diagram of the LTE-U wireless signal sensing device is shown, with reference to... Figure 5The hardware structure of the LTE-U wireless signal sensing device may include at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4. In this application, the number of processor 1, communication interface 2, memory 3, and communication bus 4 is at least one, and the processor 1, communication interface 2, and memory 3 communicate with each other through the communication bus 4. The processor 1 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement this application; the memory 3 may include high-speed RAM, and may also include non-volatile memory, such as at least one disk storage device; wherein, the memory stores a program, which the processor can call, and the program is used to implement various processing flows in the aforementioned LTE-U wireless signal sensing scheme for the terminal. This application also provides a readable storage medium that stores a program suitable for processor execution, the program being used to implement various processing flows in the aforementioned LTE-U wireless signal sensing scheme for the terminal. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. The various embodiments can be combined with each other. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A wireless signal sensing method for LTE-U, characterized in that, include: The wireless signal corresponding to the preset first frequency band in the target area is decoded using a preset first radio frequency link to obtain the first decoding result of the wireless signal of the first frequency band, wherein the first frequency band is the licensed spectrum; Based on the first decoding result, the parameters of the wireless signal in the first frequency band are analyzed to obtain the first analysis information of the parameters of the wireless signal in the first frequency band. The first analysis information includes the spectrum parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal in the first frequency band and the wireless signal in a preset second frequency band. The second frequency band is an unlicensed spectrum. Based on the first parsed information, a preset second radio frequency link is used to perform quality analysis on the wireless signal corresponding to the second frequency band in the target area to obtain the aggregation success rate of the wireless signal of the second frequency band and the envelope of the wireless signal of the second frequency band. If there are analyzable wireless signals in the wireless signals corresponding to the second frequency band, then analyze the analyzable wireless signals in the wireless signals corresponding to the second frequency band to determine the type and spectrum parameters of the first wireless interference signal corresponding to the second frequency band. Based on the type and spectrum parameters of the first wireless interference signal, a first monitoring report of the wireless signal in the target area is generated; Based on the first monitoring report, the target perception data corresponding to the target area is analyzed to determine the location information of the wireless interference signal in the second frequency band, wherein the target perception data includes information on the wireless signal at the location to be monitored in the target area.

2. The method according to claim 1, characterized in that, The method also includes: When performing quality analysis on the wireless signal corresponding to the second frequency band, if there is an unknown signal in the wireless signal corresponding to the second frequency band that cannot be resolved, the envelope corresponding to the unknown signal in the wireless signal corresponding to the second frequency band is analyzed to determine the envelope of the second wireless interference signal corresponding to the second frequency band. Based on the envelope of the second wireless interference signal, the type and spectral parameters of the second wireless interference signal are determined. Based on the type and spectral parameters of the second wireless interference signal, a second monitoring report of the wireless signal in the target area is generated.

3. The method according to claim 2, characterized in that, The method also includes: Determine whether the power intensity of the first wireless interference signal or the second wireless interference signal exceeds a preset first threshold. If there is a target interference signal in the first wireless interference signal or the second wireless interference signal whose power intensity exceeds a preset first threshold, then the target interference signal is analyzed to obtain the analysis result of the target interference signal. Based on the analysis results of the target interference signal, determine whether the target bandwidth is backed up due to the target interference signal; If the target bandwidth backs off due to the target interference signal, the target interference signal is identified to determine the type of interference.

4. The method according to claim 3, characterized in that, The method also includes: After determining the interference type of the target interference signal, the wireless interference type of the target interference signal is reported, and the interference analysis result of the target area is determined according to the positioning information of the target interference signal.

5. The method according to claim 3, characterized in that, The step of identifying the target interference signal to determine the type of interference includes: The intermediate frequency signal in the target interference signal is sampled to obtain the second target interference signal; The second target interference signal is converted from analog to digital to obtain the third target interference signal; Based on a preset sampling frequency and a preset bandwidth threshold, the filtering in the third target interference signal is subjected to extreme value processing, and the center frequency point of the third target interference signal is determined. Based on the center frequency of the third target interference signal, a sliding scan is performed in the preset target frequency band, from the low frequency band to the high frequency band, according to a preset scanning step threshold, to determine the envelope of the third target interference signal in the target frequency band from the low frequency band to the high frequency band. The envelope of the third target interference signal from the low frequency band to the high frequency band in the target frequency band is normalized to obtain the normalized processing results of each envelope of the third target interference signal in the target frequency band. The normalized results of each envelope of the third target interference signal in the target frequency band are compared with the parameters corresponding to each modulation mode of the third target interference signal in the target frequency band by a mean square error calculation to determine the interference type of the third target interference signal.

6. The method according to claim 1, characterized in that, The method also includes: Real-time monitoring of channel status information reports sent by terminal devices in the target area; The channel state information report is uploaded to the base station so that the base station can monitor the channel state of each terminal device in the target area.

7. The method according to claim 5, characterized in that, The preset sampling frequency includes 122.88MHz; The preset bandwidth threshold value range is [20MHz, 40MHz]; The preset target frequency band has a range of [5725MHz, 5850MHz]; The preset scan step threshold value range is set to (0KHz, 1KHz).

8. An LTE-U wireless signal sensing device, characterized in that, include: The decoding unit is used to decode the wireless signal corresponding to the preset first frequency band in the target area using a preset first radio frequency link, and obtain a first decoding result of the wireless signal of the first frequency band; wherein, the first frequency band is a licensed spectrum; The parsing unit is used to parse the parameters of the wireless signal in the first frequency band based on the first decoding result, and obtain first parsing information of the parameters of the wireless signal in the first frequency band. The first parsing information includes the spectrum parameters, modulation and coding parameters, and hybrid automatic repeat request configuration information of the wireless signal in the first frequency band and the wireless signal in a preset second frequency band; wherein, the second frequency band is an unlicensed spectrum. The first analysis unit is used to perform quality analysis on the wireless signal corresponding to the second frequency band in the target area based on the first parsing information and using a preset second radio frequency link, so as to obtain the aggregation success rate of the wireless signal of the second frequency band and the envelope of the wireless signal of the second frequency band. The second analysis unit is used to analyze the analyzable wireless signal in the wireless signal corresponding to the second frequency band when there is an analyzable wireless signal in the wireless signal corresponding to the second frequency band, and determine the type and spectrum parameters of the first wireless interference signal corresponding to the second frequency band. The generation unit is used to generate a first monitoring report of the wireless signal in the target area based on the type and spectrum parameters of the first wireless interference signal. The third analysis unit is used to analyze the target perception data corresponding to the target area based on the first monitoring report, so as to determine the location information of the wireless interference signal in the second frequency band, wherein the target perception data includes information on the wireless signal of the location to be monitored in the target area.

9. An LTE-U wireless signal sensing device, characterized in that, include: One or more processors, and a memory; the memory stores computer-readable instructions that, when executed by the one or more processors, implement the steps of the LTE-U wireless signal sensing method as described in any one of claims 1 to 7.

10. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to implement the steps of the LTE-U wireless signal sensing method as described in any one of claims 1 to 7.

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