A method and system for interference diagnosis of dual-mode communication

Abstract

The application discloses a kind of interference diagnosis method and system of dual-mode communication, belong to communication transmission monitoring field, this interference diagnosis method of dual-mode communication includes the following steps: real-time acquisition multi-band signal, detect abnormal power fluctuation or spectrum distortion, identify the frequency range of interference signal;Based on the space-time characteristics of interference signal, determine as external interference or internal interference;Judge the physical mechanism type of interference, if external interference, further judge is adjacent frequency interference or same frequency interference;If internal interference, further judge is intermodulation distortion or harmonic leakage;The beneficial effects of the application are: the application is based on multi-band signal analysis technology, interference detection and tracing can be automatically realized: by determining the existence of interference, distinguish external interference and internal interference type, and accurately locate the physical mechanism of interference, compared with traditional artificial troubleshooting method, this technology does not need to rely on experience, and through algorithm automatic processing significantly improves detection efficiency and diagnostic intelligent level.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of communication transmission monitoring, and particularly relates to a dual-mode communication interference diagnosis method and system. BACKGROUND

[0002] Dual-mode communication refers to that a terminal or a base station integrates two heterogeneous communication systems (such as 5G and Wi-Fi, Sub-6GHz and millimeter wave coordination), and realizes scene adaptive connection through multi-mode concurrency. However, due to the non-linear coupling between dual-mode radio frequency links, frequency band overlap and other reasons, spectrum pollution (such as harmonic intermodulation, adjacent frequency leakage) and hardware crosstalk (such as antenna isolation failure) are easily caused, which leads to signal signal-to-noise ratio degradation, throughput drop and even link interruption.

[0003] Traditional interference troubleshooting highly depends on manual experience, and operation and maintenance personnel need to manually switch modes and exclude fault sources one by one, which is low in efficiency and needs to be improved. SUMMARY

[0004] Therefore, it is necessary to provide a dual-mode communication interference diagnosis method and system aiming at the above problems.

[0005] The embodiment of the present application is implemented as follows: a dual-mode communication interference diagnosis method, comprising the following steps:

[0006] Real-time acquisition of multi-band signals, detection of abnormal power fluctuation or spectrum distortion, and identification of the frequency band range of the interference signal;

[0007] Based on the space-time characteristics of the interference signal, it is determined whether it is external interference (usually showing randomness and suddenness, such as electromagnetic radiation) or internal interference (having periodicity or synchronization with the dual-mode working state, such as dual-mode radio frequency circuit coupling);

[0008] Judging the physical mechanism type of the interference, if it is external interference, further judging whether it is adjacent frequency interference or same frequency interference; if it is internal interference, further judging whether it is intermodulation distortion or harmonic leakage.

[0009] In one embodiment, the present application provides a dual-mode communication interference diagnosis method, wherein the step of real-time acquisition of multi-band signals, detection of abnormal power fluctuation or spectrum distortion, and identification of the frequency band range of the interference signal specifically comprises:

[0010] Through a wideband receiver (such as a software-defined radio SDR), multi-band mixed analog signals (such as 5G+Wi-Fi) are captured in real time, an ADC (analog-to-digital converter) is controlled to sample at a super-Nyquist rate, and the dual-mode working frequency band (such as Sub-6GHz to millimeter wave) is ensured to be covered, so as to obtain an original digital time domain signal;

[0011] Adopt DSP to original digital time domain signal to carry on fast Fourier transform (FFT) or short time Fourier transform (STFT), generate frequency spectrum, pass through power spectrum density (PSD) analysis detects abnormality, obtains abnormal frequency band list (such as 2.4GHz power sudden increase, 3.5GHz harmonic distortion) and corresponding time-frequency characteristics;

[0012] Combine dynamic spectrum database (record legal signal characteristics) to exclude the legal frequency band signal (such as 2.4GHz power sudden increase) in abnormal frequency band list, lock the interference frequency band range (such as 3.5GHz±50MHz).

[0013] In one embodiment, the present application provides a dual-mode communication interference diagnosis method, which determines external interference or internal interference based on the space-time characteristics of interference signals, specifically comprising:

[0014] Statistical interference signal duration, interval and burst law;

[0015] If it presents random burst (such as duration <10ms, interval has no periodicity), it is determined as external interference (such as electromagnetic pulse); if it is synchronized with the working period of dual-mode device (such as appearing regularly every 100ms) or accompanied by radio frequency switching action, it is classified as internal interference (such as circuit mutual interference).

[0016] In one embodiment, the present application provides a dual-mode communication interference diagnosis method, which determines external interference or internal interference based on the space-time characteristics of interference signals, further comprising:

[0017] Adopt multi-antenna array (such as 8×8 MIMO) to calculate the direction of arrival of interference signal, use beam forming technology to obtain spatial spectrum (accuracy ±5°), if the direction angle is discrete distribution (such as variance >45°) or deviates from the main lobe of device antenna >60°, it is determined as external interference; if the direction is concentrated in the radio frequency front end of device (such as ±10° range), it is classified as internal interference;

[0018] Protocol unpacking (such as parsing Wi-Fi frame header BSSID, PLMN ID in 5G NR) is carried out on interference signal, if it carries external network identification (such as neighbor base station ID, strange MAC address), it is confirmed as external interference; if there is no valid identification or the identification is consistent with the local device, it is internal interference.

[0019] In one embodiment, the present application provides a dual-mode communication interference diagnosis method, which judges the physical mechanism type of interference, if it is external interference, further judges whether it is adjacent frequency interference or same frequency interference; if it is internal interference, further judges whether it is intermodulation distortion or harmonic leakage, specifically comprising:

[0020] If it is external interference, through spectrum analysis, it is detected whether there is power leakage on both sides of the main frequency band (for example, 5G main frequency 3.5GHz, 3.4 / 3.6GHz anomaly is detected), if the bandwidth of the interference signal overlaps with the adjacent frequency legal signal and has no time correlation, it is determined as adjacent frequency interference;

[0021] If the center frequency of the interference signal is consistent with the main frequency (for example, both are located at 3.5GHz), and carries the conflict network identifier, it is determined as co-frequency interference;

[0022] If it is internal interference, it is detected whether there is an equally spaced spurious signal at an integer multiple of the fundamental frequency, and if the spurious power exceeds a threshold (for example, -30dBc), it is determined as intermodulation distortion.

[0023] It is analyzed whether there is abnormal power uplift (for example, higher than background noise by 20dB) at an integer multiple of the fundamental frequency, and the harmonic leakage source is confirmed in combination with the non-linear characteristics (for example, PA compression point) of the radio frequency front end.

[0024] In one embodiment, the present application provides a dual-mode communication interference diagnosis system, comprising:

[0025] An interference signal frequency band identification unit is configured to collect multi-frequency band signals in real time, detect abnormal power fluctuation or spectrum distortion, and identify the frequency band range of the interference signal;

[0026] An interference distinguishing unit is configured to determine external interference (usually showing randomness and suddenness, such as electromagnetic radiation) or internal interference (having periodicity or synchronization with the dual-mode working state, such as dual-mode radio frequency circuit coupling) based on the space-time characteristics of the interference signal;

[0027] A physical mechanism identification unit is configured to determine the physical mechanism type of the interference, if it is external interference, further determine whether it is adjacent frequency interference or co-frequency interference; if it is internal interference, further determine whether it is intermodulation distortion or harmonic leakage.

[0028] In one embodiment, the present application provides a dual-mode communication interference diagnosis system, and the interference signal frequency band identification unit comprises:

[0029] An original signal acquisition module is configured to capture multi-frequency band mixed analog signals (such as 5G+Wi-Fi) in real time through a wideband receiver (such as a software defined radio SDR), control an ADC (analog-to-digital converter) to sample at a super-Nyquist rate, ensure to cover the dual-mode working frequency band (such as Sub-6GHz to millimeter wave), and obtain the original digital time domain signal;

[0030] An abnormal frequency band list obtaining module is configured to perform fast Fourier transform (FFT) or short-time Fourier transform (STFT) on the original digital time domain signal by using a DSP to generate a frequency spectrum, detect abnormalities by power spectrum density (PSD) analysis, and obtain an abnormal frequency band list (such as 2.4 GHz power surge and 3.5 GHz harmonic distortion) and corresponding time-frequency characteristics.

[0031] An interference frequency band range locking module is configured to exclude a legal frequency band signal (such as 2.4 GHz power surge) in the abnormal frequency band list in combination with a dynamic spectrum database (recording legal signal characteristics) to lock an interference frequency band range (such as 3.5 GHz ± 50 MHz).

[0032] In one of the embodiments, the present application provides an interference diagnosis system for dual-mode communication, and the interference distinguishing unit comprises:

[0033] An interference signal statistics module is configured to count the duration, interval and burst rule of the interference signal.

[0034] An external or internal distinguishing module is configured to determine external interference (such as electromagnetic pulse) if the interference signal presents random burst (such as duration < 10 ms and interval without periodicity); and determine internal interference (such as circuit mutual interference) if the interference signal is synchronized with the working period of the dual-mode device (such as appearing regularly every 100 ms) or accompanied by radio frequency switching action.

[0035] In one of the embodiments, the present application provides an interference diagnosis system for dual-mode communication, and the interference distinguishing unit further comprises:

[0036] A signal direction distinguishing module is configured to calculate the direction of arrival of the interference signal by using a multi-antenna array (such as 8x8 MIMO), obtain a spatial spectrum (accuracy ± 5°) by using beamforming technology, determine external interference if the direction angle is discretely distributed (such as variance > 45°) or deviates from the main lobe of the device antenna by > 60°, and determine internal interference if the direction is concentrated in the radio frequency front end of the device (such as within a range of ± 10°).

[0037] A network identification distinguishing module is configured to perform protocol unpacking (such as analyzing Wi-Fi frame header BSSID and PLMN ID in 5G NR) on the interference signal, confirm external interference if the interference signal carries external network identification (such as neighbor base station ID and unfamiliar MAC address), and determine internal interference if there is no valid identification or the identification is consistent with the local device.

[0038] In one of the embodiments, the present application provides an interference diagnosis system for dual-mode communication, and the physical mechanism identification unit comprises:

[0039] An adjacent frequency interference determination module is configured to, if it is external interference, detect whether there is power leakage on both sides of the main frequency band through spectrum analysis (for example, if the main frequency of 5G is 3.5 GHz, 3.4 / 3.6 GHz is detected as abnormal), and if the bandwidth of the interference signal overlaps with the adjacent frequency legal signal and there is no time correlation, it is determined as adjacent frequency interference;

[0040] A co-frequency interference determination module is configured to, if the center frequency of the interference signal is consistent with the main frequency (for example, both are located at 3.5 GHz), and carries the conflict network identifier, it is determined as co-frequency interference.

[0041] An intermodulation distortion determination module is configured to, if it is internal interference, detect whether there is an equally spaced spurious signal at an integer multiple of the fundamental frequency, and if the spurious power exceeds a threshold value (for example, -30 dBc), it is determined as intermodulation distortion.

[0042] A harmonic leakage determination module is configured to analyze whether there is abnormal power lifting (for example, higher than the background noise by 20 dB) at an integer multiple of the fundamental frequency, and confirm the source of the harmonic leakage in combination with the non-linear characteristics (for example, the PA compression point) of the radio frequency front end.

[0043] Compared with the prior art, the beneficial effects of the present application are: based on the multi-frequency signal analysis technology, the present application can automatically realize interference detection and tracing, by determining the existence of interference, distinguishing the types of external interference and internal interference, and accurately positioning the physical mechanism of interference, compared with the traditional manual troubleshooting method, this technology does not need to rely on experience, and through the automatic processing of the algorithm, the detection efficiency and the intelligent level of diagnosis are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 A flowchart of a dual-mode communication interference diagnosis method is provided for the embodiments of the present application.

[0045] Figure 2 A flowchart of identifying the frequency range of the interference signal is provided for the embodiments of the present application.

[0046] Figure 3 A flowchart of distinguishing external interference and internal interference under normal conditions is provided for the embodiments of the present application.

[0047] Figure 4 A flowchart of distinguishing external interference and internal interference under special conditions is provided for the embodiments of the present application.

[0048] Figure 5 A flowchart of judging the type of the physical mechanism of interference is provided for the embodiments of the present application.

[0049] Figure 6 A schematic diagram of a dual-mode communication interference diagnosis system is provided for the embodiments of the present application.

[0050] Figure 7A schematic diagram of the interference signal frequency band identification unit provided for the embodiment of the present application.

[0051] Figure 8 A first part schematic diagram of the interference differentiation unit provided for the embodiment of the present application.

[0052] Figure 9 A second part schematic diagram of the interference differentiation unit provided for the embodiment of the present application.

[0053] Figure 10 A schematic diagram of the physical mechanism identification unit provided for the embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0055] In one embodiment, as shown in Figure 1 A dual-mode communication interference diagnosis method, comprising the following steps:

[0056] Step S1, real-time acquisition of multi-frequency band signals, detection of abnormal power fluctuation or spectrum distortion, identification of the frequency band range of the interference signal;

[0057] Step S2, based on the space-time characteristics of the interference signal, determine whether it is external interference (usually showing randomness and suddenness, such as electromagnetic radiation) or internal interference (having periodicity or synchronization with the dual-mode working state, such as dual-mode radio frequency circuit coupling);

[0058] Step S3, determine the physical mechanism type of the interference, if it is external interference, further determine whether it is adjacent frequency interference or same frequency interference; if it is internal interference, further determine whether it is intermodulation distortion or harmonic leakage.

[0059] Specifically, in the 5G+Wi-Fi dual-mode base station scenario:

[0060] Step S1, through SDR (software defined radio) with 2.4Gsps oversampling to capture Sub-6GHz (frequency range below 6GHz, usually 450MHz to 6GHz wireless communication frequency band, 3.5GHz 5G and 2.4GHz Wi-Fi are within this range) mixed signal, after generating a spectrum graph through fast Fourier transform, a sudden increase of 30dB at 3.5GHz and the existence of second harmonic (7GHz) are detected, and after the dynamic database excludes the Wi-Fi frequency band, 3.5GHz±50MHz is locked as the interference frequency band.

[0061] Step S2, analyze the periodic occurrence of interference signals every 50 ms and synchronize with radio frequency switching, beamforming measures the direction angle concentrated in the main lobe of the device antenna (±8°), protocol analysis without external ID, determine as internal interference.

[0062] Step S3, find the second harmonic (7GHz) power of -25dBc (threshold) at the main frequency of 3.5GHz, combined with the nonlinear characteristics of PA (power amplifier) (the non-ideal proportional relationship between input and output signals in the amplification process) to confirm the harmonic leakage.

[0063] In one embodiment, as shown in Figure 2 The interference diagnosis method of the dual-mode communication, the step S1, real-time acquisition of multi-band signal, detection of abnormal power fluctuation or spectrum distortion, identification of the frequency band range where the interference signal is located, specifically includes:

[0064] Step S11, capture multi-band mixed analog signals (such as 5G+Wi-Fi) in real time through a wideband receiver (such as software-defined radio SDR), control the ADC (analog-to-digital converter) to sample at a super-Nyquist rate, ensure coverage of dual-mode working frequency bands (such as Sub-6GHz to millimeter wave), and obtain the original digital time domain signal;

[0065] Step S12, use DSP to perform fast Fourier transform (FFT) or short-time Fourier transform (STFT) on the original digital time domain signal to generate a spectrum graph, detect abnormalities through power spectral density (PSD) analysis, and obtain an abnormal frequency band list (such as 2.4GHz power surge, 3.5GHz harmonic distortion) and corresponding time-frequency characteristics;

[0066] Step S13, exclude legal frequency band signals (such as 2.4GHz power surge) in the abnormal frequency band list in combination with a dynamic spectrum database (records legal signal characteristics), and lock the interference frequency band range (such as 3.5GHz±50MHz).

[0067] In the 5G+Wi-Fi dual-mode base station scenario:

[0068] Step S11, use USRP X310 SDR (a high-performance software-defined radio hardware platform) to configure dual-channel reception (2.4GHz+3.5GHz) to capture time domain signals synchronously at a sampling rate of 3Gsps, covering dual-mode frequency bands.

[0069] Step S12, DSP performs 4096-point FFT on 2ms time window signal, calculates PSD to find that the 3.5GHz power peak-to-average ratio exceeds the threshold (>15dB), and STFT shows that it lasts for 10ms every 100ms.

[0070] Step S13, compare the communication technology standard 3GPP standard spectrum template (such as 3.5GHz allows ±10MHz fluctuation), exclude compliant signals, mark 3.5-3.55GHz as an abnormal frequency band.

[0071] In one embodiment, as shown in Figure 3 A dual-mode communication interference diagnosis method, said step S2, based on the space-time characteristics of the interference signal, determines whether it is external interference or internal interference, specifically includes:

[0072] Step S21, statistics of the duration, interval and burst of the interference signal;

[0073] Step S22, if it presents random burst (such as duration <10ms, interval without periodicity), it is determined as external interference (such as electromagnetic pulse); if it is synchronized with the working period of the dual-mode device (such as appearing regularly every 100ms) or accompanied by radio frequency switching action, it is classified as internal interference (such as circuit mutual interference).

[0074] In the 5G+Wi-Fi dual-mode base station scenario:

[0075] Step S21, statistics of the interference pulse lasting 10ms, interval 100ms (consistent with the base station scheduling period), burst rule synchronized with the radio frequency front-end clock.

[0076] Step S22, due to periodicity (variance <5ms) and strong correlation with device working state (such as triggered when MIMO switching), external interference such as electromagnetic pulse is excluded, and it is determined as internal circuit coupling.

[0077] In one embodiment, as shown in Figure 4 A dual-mode communication interference diagnosis method, said step S2, based on the space-time characteristics of the interference signal, determines whether it is external interference or internal interference, further includes:

[0078] Step S23, calculate the direction of arrival of the interference signal using a multi-antenna array (such as 8x8 MIMO), obtain the spatial spectrum (accuracy ±5°) using beamforming technology, if the direction angle is discretely distributed (such as variance >45°) or deviates from the device antenna main lobe by >60°, it is determined as external interference; if the direction is concentrated in the device radio frequency front-end (such as within ±10° range), it is classified as internal interference;

[0079] Step S24, protocol unpackaging (such as parsing Wi-Fi frame header BSSID, PLMNID in 5G NR) is performed on the interference signal, if it carries external network identification (such as neighbor base station ID, unfamiliar MAC address), it is confirmed as external interference; if there is no valid identification or the identification is consistent with the local device, it is internal interference.

[0080] In the 5G+Wi-Fi dual-mode base station scenario:

[0081] Step S23, the 8x8 MIMO array receives the signal and calculates the spatial spectrum to find the interference direction angle set (mean 12°, variance 5°), which matches the antenna main lobe (10° direction) and points to the radio frequency front end, which is internal interference.

[0082] Step S24, the encapsulated 5G NR frame is extracted to extract the PLMN ID, which is consistent with the local base station (such as PLMN = 46011) and does not have a neighbor base station identifier (such as PLMN = 46001), which excludes external interference.

[0083] In one embodiment, as shown in Figure 5 the interference diagnosis method of a dual-mode communication, the step S3 judges the physical mechanism type of the interference, and if it is external interference, further judges whether it is adjacent frequency interference or same frequency interference; if it is internal interference, further judges whether it is intermodulation distortion or harmonic leakage step, which specifically includes:

[0084] Step S31, if it is external interference, detect whether there is power leakage on both sides of the main frequency band (such as 5G main frequency 3.5GHz, detect 3.4 / 3.6GHz anomaly) through spectrum analysis, if the interference signal bandwidth overlaps with the adjacent frequency legal signal and has no time correlation, it is determined as adjacent frequency interference.

[0085] Step S32, if the center frequency of the interference signal is consistent with the main frequency (such as both located at 3.5GHz), and carries the conflict network identifier, it is determined as same frequency interference.

[0086] Step S33, if it is internal interference, detect whether there is an equally spaced spurious signal at an integer multiple of the fundamental frequency, and if the spurious power exceeds the threshold (such as -30dBc), it is determined as intermodulation distortion.

[0087] Step S34, analyze whether there is abnormal power rise (such as higher than background noise by 20dB) at the integer multiple frequency point of the fundamental frequency, and confirm the harmonic leakage source in combination with the non-linear characteristics (such as PA compression point) of the radio frequency front end.

[0088] In the 5G+Wi-Fi dual-mode base station scenario:

[0089] Step S31, external interference example: 3.4GHz (adjacent frequency) power surge is detected and has no time domain synchronization with 3.5GHz main frequency, and the bandwidth overlaps 20MHz (3.4-3.42GHz), which is determined as adjacent frequency interference.

[0090] Step S32, same frequency interference scenario: the interference center frequency 3.5GHz carries the conflict cell ID (such as PCI = 101 conflicts with local PCI = 100), which is confirmed as same frequency interference.

[0091] Step S33, Internal Interference Case: A spurious power of 4.8 GHz (second harmonic) was detected at the fundamental frequency of 2.4 GHz, reaching -28 dBc (threshold -30 dBc). Combined with the PA compression characteristics (the nonlinear phenomenon exhibited by the power amplifier when operating at high power, mainly manifested as the gain decreasing with the increase of input power, eventually leading to output power saturation), intermodulation distortion was confirmed.

[0092] In step S34, during 5G base station RF testing, a power spike of the second harmonic at 7.0GHz (base frequency 3.5GHz) was detected to -35dBm (background noise -55dBm), exceeding the threshold by 20dB. Simultaneously, the PA output power was monitored at +23dBm (close to the 1dB compression point of +24dBm), indicating even-order harmonic leakage due to its nonlinear characteristics. Combining this with the PA's AM-AM characteristic curve (distortion increases sharply when input > +10dBm), the 7GHz anomaly was confirmed as harmonic leakage, requiring optimization of the PA bias or the addition of a filter.

[0093] In one embodiment, such as Figure 6 As shown, an interference diagnosis system for dual-mode communication includes:

[0094] Interference signal frequency band identification unit 1 is used to collect multi-frequency band signals in real time, detect abnormal power fluctuations or spectral distortions, and identify the frequency band range where the interference signal is located.

[0095] Interference differentiation unit 2 is used to determine whether the interference signal is external interference (usually manifested as randomness or suddenness, such as electromagnetic radiation) or internal interference (periodicity or synchronization with dual-mode operation, such as dual-mode radio frequency circuit coupling) based on the spatiotemporal characteristics of the interference signal.

[0096] The physical mechanism identification unit 3 is used to determine the type of physical mechanism of the interference. If it is external interference, it is further determined to be adjacent channel interference or co-channel interference; if it is internal interference, it is further determined to be intermodulation distortion or harmonic leakage.

[0097] Interference signal frequency band identification unit 1 quickly captures the characteristics of signals across the entire frequency band through oversampling and spectrum analysis, and filters known legitimate signals using a dynamic database to achieve initial interference screening. Its necessity lies in accurately locating abnormal frequency bands (such as harmonics and sudden increases) from massive signals to avoid missed detections or misjudgments.

[0098] Interference Differentiation Unit 2 distinguishes between internal and external interference by fusing multi-dimensional features including temporal patterns, spatial orientation, and protocol identifiers. For example, sudden randomness and external spatial orientation can eliminate the coupling of the device itself, ensuring the physical accuracy of interference source location and providing direction for subsequent suppression measures.

[0099] The physical mechanism identification unit 3 reveals the causes of interference (such as adjacent frequency overlap or circuit distortion) based on the correlation analysis between physical layer characteristics (such as spectral leakage and harmonic spacing) and hardware nonlinear characteristics (such as PA compression points). It gradually narrows down the problem range, improving diagnostic efficiency and reliability.

[0100] In one embodiment, such as Figure 7 As shown, an interference diagnosis system for dual-mode communication includes an interference signal frequency band identification unit 1 comprising:

[0101] The raw signal acquisition module 11 is used to capture multi-band mixed analog signals (such as 5G+Wi-Fi) in real time through a broadband receiver (such as software-defined radio SDR), control the ADC (analog-to-digital converter) to sample at the super Nyquist rate, ensure coverage of dual-mode operating frequency bands (such as Sub-6GHz to millimeter wave), and obtain the raw digital time domain signal.

[0102] The abnormal frequency band list acquisition module 12 is used to perform Fast Fourier Transform (FFT) or Short Time Fourier Transform (STFT) on the original digital time domain signal using DSP to generate a spectrum diagram, detect anomalies through power spectral density (PSD) analysis, and obtain an abnormal frequency band list (such as 2.4GHz power surge, 3.5GHz harmonic distortion) and corresponding time and frequency characteristics.

[0103] The interference frequency band range locking module 13 is used to combine the dynamic spectrum database (which records the characteristics of legal signals) to exclude legal frequency band signals (such as a sudden increase in power at 2.4 GHz) from the abnormal frequency band list and lock the interference frequency band range (such as 3.5 GHz ± 50 MHz).

[0104] An adaptive learning mechanism for the dynamic spectrum database can also be added. In real-world scenarios, legitimate signals may change dynamically (such as drone image transmission signals temporarily accessed within licensed frequency bands). Historical spectrum data can be analyzed in real time through online machine learning (such as unsupervised clustering) to automatically identify newly added legitimate signal patterns (such as specific modulation schemes or time slot allocations) and dynamically update the database. For example, when a periodic QPSK modulated signal (a modulation technique that transmits digital information through carrier phase changes) carrying an authentication identifier is detected in a certain frequency band (such as 3.6GHz), it can be classified as a legitimate signal even without a pre-stored template, avoiding misjudgment as interference.

[0105] In one embodiment, such as Figure 8 As shown, an interference diagnosis system for dual-mode communication includes an interference differentiation unit 2 comprising:

[0106] Interference signal statistics module 21 is used to count the duration, interval and burst pattern of interference signals;

[0107] The external or internal differentiation module 22 is used to determine external interference (such as electromagnetic pulse) if it presents random bursts (such as duration <10ms, interval without periodicity); and internal interference (such as circuit interference) if it is synchronized with the working cycle of dual-mode equipment (such as appearing regularly every 100ms) or accompanied by radio frequency switching action.

[0108] For example, external interference: A 5G base station detected a sudden surge in power in the 2.6GHz band (lasting 8ms with irregular intervals). The beamforming signal direction was consistent with the radiation source of a nearby industrial microwave oven, and there was no protocol identification. This was confirmed as external electromagnetic interference.

[0109] Internal interference: In 160MHz bandwidth mode, the Wi-Fi device experiences 5GHz band spectrum leakage every 33ms (synchronized with the Beacon frame period). The directional angle is concentrated on the device's antenna array, and the protocol resolves to the local BSSID (MAC address). This is determined to be intermodulation interference caused by insufficient linearity of the internal radio frequency front-end.

[0110] In one embodiment, such as Figure 9 As shown, in a dual-mode communication interference diagnosis system, the interference differentiation unit 2 further includes:

[0111] The signal direction differentiation module 23 is used to calculate the arrival direction of the interference signal using a multi-antenna array (such as 8×8 MIMO) and to obtain the spatial spectrum (accuracy ±5°) using beamforming technology. If the direction angle is discretely distributed (such as variance > 45°) or deviates from the main lobe of the device antenna by > 60°, it is determined to be external interference; if the direction is concentrated in the RF front end of the device (such as within ±10° range), it is classified as internal interference.

[0112] The network identifier differentiation module 24 is used to decapsulate the interference signal according to the protocol (such as parsing the Wi-Fi frame header BSSID and the PLMN ID in 5G NR). If it carries an external network identifier (such as the neighboring cell base station ID or an unfamiliar MAC address), it is identified as external interference; if there is no valid identifier or the identifier is consistent with the local device, it is internal interference.

[0113] If an external interference source synchronizes with the dual-mode device's operating cycle (e.g., a neighboring base station and a local device operate on the same frequency), its interference time-domain characteristics (e.g., periodicity) are similar to internally coupled interference, causing the interference differentiation unit 2 to misjudge the source attribute. Ultimately, the physical mechanism identification unit 3 also makes a misjudgment. Therefore, the signal direction differentiation module 23 and the network identification differentiation module 24 are used to distinguish the synchronization status of the external interference source with the dual-mode device's operating cycle to avoid misjudgment. The signal direction differentiation module 23 and the network identification differentiation module 24 can be used individually or in combination. In addition, a module in the dual-mode system can be temporarily shut down to observe whether the interference disappears, quickly verifying the source of the interference (external interference is not affected by the start / stop of the local module).

[0114] In one embodiment, such as Figure 10 As shown, a dual-mode communication interference diagnosis system includes a physical mechanism identification unit 3 comprising:

[0115] The adjacent channel interference determination module 31 is used to detect whether there is power leakage on both sides of the main frequency band (e.g., 3.4 / 3.6GHz anomaly detected in 5G main frequency 3.5GHz) by spectrum analysis if it is external interference. If the bandwidth of the interference signal overlaps with the adjacent channel legitimate signal and there is no time correlation, it is determined to be adjacent channel interference.

[0116] The co-channel interference determination module 32 is used to determine co-channel interference if the center frequency of the interference signal is the same as the main frequency (e.g., both are located at 3.5GHz) and carries a conflict network identifier.

[0117] The intermodulation distortion determination module 33 is used to detect whether there are equally spaced spurious signals at integer multiples of the fundamental frequency if it is internal interference. If the spurious power exceeds the threshold (e.g., -30dBc), it is determined to be intermodulation distortion.

[0118] The harmonic leakage determination module 34 is used to analyze whether there is abnormal power rise (such as 20dB above the background noise) at integer multiples of the fundamental frequency, and to confirm the source of harmonic leakage by combining the nonlinear characteristics of the RF front end (such as PA compression point).

[0119] In the harmonic leakage determination module 34, the fundamental frequency integer multiples (e.g., the second harmonic of 3.5GHz, 7GHz) are detected by spectrum scanning. High-resolution FFT (e.g., 32768 points) is used to quantize the harmonic power and compare it to a preset threshold (e.g., -40dBc). Simultaneously, combined with RF front-end nonlinearity testing (e.g., the PA's 1dB compression point and third-order intermodulation coefficient), a standard single-tone signal (e.g., 3.5GHz@+20dBm) is injected, and the harmonic power growth slope is measured to verify the nonlinear distortion characteristics. For example, if the 7GHz power increases quadratically with increasing input power (slope ≈ 2), and the PA is close to saturation (e.g., gain compression of 1dB at +24dBm output), then the harmonic leakage is confirmed to originate from PA nonlinear distortion, requiring optimization of the matching circuit or the addition of an out-of-band suppression filter.

[0120] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention 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 various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0122] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0125] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for diagnosing interference in dual-mode communication, characterized in that, The interference diagnosis method for this dual-mode communication includes the following steps: Real-time acquisition of multi-frequency band signals, detection of abnormal power fluctuations or spectral distortion, and identification of the frequency band range where interference signals are located; Based on the spatiotemporal characteristics of the interference signal, it is determined to be either external interference or internal interference. Determine the type of physical mechanism of the interference. If it is external interference, further determine whether it is adjacent-channel interference or co-channel interference; if it is internal interference, further determine whether it is intermodulation distortion or harmonic leakage. The step of determining whether interference is external or internal based on the spatiotemporal characteristics of the interference signal specifically includes: The duration, intervals, and burst patterns of statistical interference signals were analyzed. If it occurs randomly and suddenly, it is considered external interference; if it is synchronized with the working cycle of dual-mode equipment or accompanied by radio frequency switching, it is considered internal interference. The direction of arrival of interference signals is calculated using a multi-antenna array, and the spatial spectrum is obtained using beamforming technology. If the direction angle is discretely distributed or deviates from the main lobe of the device antenna by more than 60°, it is determined to be external interference; if the direction is concentrated in the radio frequency front end of the device, it is classified as internal interference. The interference signal is decapsulated according to the protocol. If it carries an external network identifier, it is confirmed as external interference; if there is no valid identifier or the identifier is consistent with the local device, it is internal interference.

2. The interference diagnosis method for dual-mode communication according to claim 1, characterized in that, The steps of real-time acquisition of multi-band signals, detection of abnormal power fluctuations or spectral distortion, and identification of the frequency band range where interference signals are located specifically include: Multi-band hybrid analog signals are captured in real time by a broadband receiver, and the ADC is controlled to sample at a super Nyquist rate to ensure coverage of the dual-mode operating frequency band and obtain the original digital time domain signal. The original digital time-domain signal is subjected to fast Fourier transform or short-time Fourier transform using DSP to generate a spectrum. Anomalies are detected by power spectral density analysis, and a list of abnormal frequency bands and their corresponding time-frequency characteristics are obtained. By combining the dynamic spectrum database to exclude legitimate frequency band signals from the abnormal frequency band list, the range of interfering frequency bands can be locked.

3. The interference diagnosis method for dual-mode communication according to claim 1 or 2, characterized in that, The step of determining the physical mechanism type of interference, if it is external interference, further determines whether it is adjacent-channel interference or co-channel interference; if it is internal interference, further determines whether it is intermodulation distortion or harmonic leakage. Specifically, this includes: If it is external interference, the spectrum analysis is used to detect whether there is power leakage on both sides of the main frequency band. If the bandwidth of the interference signal overlaps with the adjacent channel legitimate signal and there is no time correlation, it is determined to be adjacent channel interference. If the center frequency of the interfering signal is the same as the main frequency and carries a conflict network identifier, it is determined to be co-channel interference. If it is internal interference, check whether there are equally spaced spurious signals at integer multiples of the fundamental frequency. If the spurious power exceeds the threshold, it is determined to be intermodulation distortion. Analyze whether there is abnormal power rise at integer multiples of the fundamental frequency, and confirm the source of harmonic leakage by combining the nonlinear characteristics of the RF front end.

4. An interference diagnosis system for dual-mode communication, characterized in that, include: The interference signal frequency band identification unit is used to collect multi-frequency band signals in real time, detect abnormal power fluctuations or spectral distortions, and identify the frequency band range where the interference signal is located. The interference discrimination unit is used to determine whether the interference signal is external or internal based on its spatiotemporal characteristics. The physical mechanism identification unit is used to determine the type of physical mechanism of the interference. If it is external interference, it is further determined whether it is adjacent channel interference or co-channel interference. If it is internal interference, further investigation is needed to determine whether it is intermodulation distortion or harmonic leakage; The interference differentiation unit includes: The interference signal statistics module is used to count the duration, interval, and burst patterns of interference signals. An external or internal differentiation module is used to determine whether the interference is random and sudden, and whether it is synchronized with the working cycle of a dual-mode device or accompanied by radio frequency switching. The signal direction differentiation module is used to calculate the arrival direction of interference signals using a multi-antenna array and obtain the spatial spectrum using beamforming technology. If the direction angle is discretely distributed or deviates from the main lobe of the device antenna by more than 60°, it is determined to be external interference; if the direction is concentrated in the RF front end of the device, it is classified as internal interference. The network identifier differentiation module is used to decapsulate interference signals according to protocols. If the signal carries an external network identifier, it is identified as external interference. If there is no valid identifier or the identifier is consistent with the local device, it is considered internal interference.

5. The interference diagnosis system for dual-mode communication according to claim 4, characterized in that, The interference signal frequency band identification unit includes: The raw signal acquisition module is used to capture multi-band mixed analog signals in real time through a broadband receiver, control the ADC to sample at the super Nyquist rate, ensure coverage of the dual-mode operating frequency band, and obtain the raw digital time domain signal. The abnormal frequency band list acquisition module is used to perform fast Fourier transform or short-time Fourier transform on the original digital time domain signal using DSP, generate a spectrum, detect anomalies through power spectral density analysis, and obtain an abnormal frequency band list and corresponding time-frequency characteristics. The interference frequency band range locking module is used to combine a dynamic spectrum database to exclude legitimate frequency band signals from the abnormal frequency band list and lock the interference frequency band range.

6. The interference diagnosis system for dual-mode communication according to claim 4 or 5, characterized in that, The physical mechanism identification unit includes: The adjacent channel interference determination module is used to detect whether there is power leakage on both sides of the main frequency band if it is external interference. If the bandwidth of the interference signal overlaps with the legal adjacent channel signal and there is no time correlation, it is determined to be adjacent channel interference. The co-channel interference determination module is used to determine co-channel interference if the center frequency of the interference signal is the same as the main frequency and carries a conflict network identifier. The intermodulation distortion determination module is used to detect whether there are equally spaced spurious signals at integer multiples of the fundamental frequency if it is internal interference. If the spurious power exceeds the threshold, it is determined to be intermodulation distortion. The harmonic leakage determination module is used to analyze whether there is abnormal power rise at integer multiples of the fundamental frequency, and to confirm the source of harmonic leakage by combining the nonlinear characteristics of the RF front end.

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