Self-interference cancellation method and apparatus, electronic device, and program product

Abstract

The application relates to the wireless technical field, and provides a self-interference cancellation method and device, electronic equipment and program product. The method comprises the following steps: determining a target channel coefficient according to a change trend of a channel coefficient or a system event; and performing self-interference cancellation according to the target channel coefficient; wherein, if the change slopes of the channel coefficients of a plurality of continuous sampling points all exceed a preset threshold, the target channel coefficient is determined according to the change trend; and if the system event occurs, the target channel coefficient is determined according to the system event. The application can solve the technical problems that the existing self-interference cancellation scheme cannot cope with sudden large-scale channel physical changes, and the convergence speed is slow when the self-interference cannot converge.

Description

Technical Field

[0001] This application relates to the field of wireless technology, specifically to a self-interference cancellation method, apparatus, electronic device, and program product. Background Technology

[0002] In wireless communication systems, self-interference refers to the interference between transmitted and received signals within the system. This interference is particularly severe in full-duplex communication systems and repeaters, because full-duplex systems use the same frequency resources for both transmission and reception simultaneously, resulting in strong self-interference between the transmitted and received signals.

[0003] Existing self-interference cancellation schemes mainly include spatial domain cancellation, analog domain self-interference cancellation, and digital domain self-interference cancellation. Spatial domain cancellation primarily relies on designing highly isolated antennas and RF front-ends to reduce self-interference; however, this method is costly and difficult to completely eliminate self-interference. Analog domain self-interference cancellation is highly complex to optimize due to the unknown nature of multipath signals. Digital domain self-interference cancellation generates cancellation signals in digital circuits and can apply adaptive filters, using algorithms such as least squares and Normalized Least Mean Squares (NLMS) to cancel complex multipath echo signals. It offers moderate implementation difficulty and good results.

[0004] Despite some progress in existing self-interference cancellation schemes, several problems remain in practical applications. Most existing digital self-interference cancellation schemes focus on improving the NLMS step size or increasing the number of taps; these methods are post-hoc compensations and cannot cope with sudden, significant changes in channel physical conditions. Furthermore, when self-interference fails to converge, the system is unsure whether the cause is a channel change or a base station signal, requiring analysis of large amounts of data, including error signals and received signals. This computational and logical complexity leads to slow convergence. Summary of the Invention

[0005] This application provides a self-interference cancellation method, apparatus, electronic device, and program product to solve the technical problems that existing self-interference cancellation schemes cannot cope with sudden large-scale channel physical changes and have slow convergence speed when self-interference fails to converge.

[0006] In a first aspect, embodiments of this application provide a self-interference cancellation method, including: Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; Self-interference cancellation is performed based on the target channel coefficients; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0007] In one embodiment, the method further includes constructing a database, which comprises a dynamic database and a static database: Transmit a training sequence, receive the training sequence signal after passing through the self-interference channel, extract the channel coefficient envelope from the training sequence signal, and record the speed and distance information of the corresponding moving object. Store the channel coefficient envelope and the corresponding speed and distance information into the dynamic database. The phase change is calculated based on the switching time difference between the uplink and downlink and the local oscillator frequency, and a rotation factor with the phase change as the exponent is generated. The switching time difference and the corresponding rotation factor are stored in the static database. The channel coefficients corresponding to different gains of the adjustable attenuator are stored in the static database.

[0008] In one embodiment, determining the target channel coefficient based on the changing trend includes: The changing trend is matched with the channel coefficient envelope in the dynamic database to obtain the target channel coefficient envelope; The target channel coefficients are determined based on the target channel coefficient envelope.

[0009] In one embodiment, determining the target channel coefficients based on the target channel coefficient envelope includes: The amplitude and main path delay position of the current channel coefficients are adjusted according to the target channel coefficient envelope to obtain the adjusted channel coefficients; If the adjusted channel coefficients do not converge, the updated trend of change is obtained, the target channel coefficient envelope is rematched according to the updated trend of change, and the adjustment range of the channel coefficients is updated. The channel coefficients are adjusted according to the updated adjustment range until the channel coefficients converge, and the channel coefficients at the time of convergence are taken as the target channel coefficients.

[0010] In one embodiment, the channel coefficient envelope includes at least one of the following: the amplitude of the channel coefficient, the position of the main path delay, and the trajectory of the channel coefficient change.

[0011] In one embodiment, the system event includes at least one of a downlink transmit termination event and an adjustable attenuator gain change event.

[0012] In one embodiment, determining the target channel coefficients based on the system event includes any one of the following: If a downlink transmission termination event occurs, the corresponding rotation factor is obtained from the static database based on the switching time difference between the uplink and downlink. The current channel coefficient is then rotated using the rotation factor to obtain the target channel coefficient. If an adjustable attenuator gain change event occurs, the corresponding channel coefficient is obtained from the static database based on the changed adjustable attenuator gain, and used as the target channel coefficient.

[0013] In one embodiment, the method further includes: If the base station has a sensing function, it can obtain the speed and distance information of the moving object through the sensing function, determine the target channel coefficient envelope from the dynamic database based on the speed and distance information, and determine the target channel coefficient based on the target channel coefficient envelope.

[0014] Secondly, embodiments of this application provide a self-interference cancellation device, comprising: The channel coefficient determination module is used to determine the target channel coefficient based on the changing trend of the channel coefficient or system events; The self-interference cancellation module is used to perform self-interference cancellation based on the target channel coefficient; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0015] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory storing a computer program, wherein the processor executes the program to implement the self-interference cancellation method described in the first aspect.

[0016] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the self-interference cancellation method described in the first aspect.

[0017] Fifthly, this application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the self-interference cancellation method described in the first aspect.

[0018] The self-interference cancellation method, apparatus, electronic device, and program product provided in this application introduce a real-time monitoring mechanism for the slope of channel coefficient changes. When the slope of changes at multiple consecutive sampling points exceeds a preset threshold, a sudden channel change is immediately determined. Existing self-interference cancellation schemes require iterative updates of coefficients based on error signals after a channel change. Due to the large magnitude of the change, iterative convergence takes a long time, during which self-interference cannot be effectively eliminated. However, this application directly determines the target channel coefficient based on the monitored change trend after identifying the change, eliminating the need for iterative updates. Therefore, it can complete coefficient updates in a short time after a channel change, effectively responding to sudden and significant physical changes in the channel. Furthermore, existing self-interference cancellation schemes require analysis of error signals and received data to determine whether the cause is a channel change or base station signal interference when self-interference fails to converge. This analysis process is complex and time-consuming. This application directly selects the corresponding target channel coefficient determination method based on the determination result: when the slope of change exceeds a threshold, the target channel coefficient is determined based on the change trend; when a system event occurs, the target channel coefficient is determined based on the system event. This bypasses the complex analysis of error signals and received data, directly obtaining the target channel coefficient. By eliminating the complex analysis process, the convergence speed is significantly improved, thus solving the technical problem of slow convergence speed when self-interference fails to converge. Attached Figure Description

[0019] 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a block diagram of an existing interference cancellation system. Figure 2 This is a flowchart illustrating the self-interference cancellation method provided in the embodiments of this application; Figure 3 This is a system block diagram of the interference cancellation system provided in the embodiments of this application; Figure 4 This is a flowchart illustrating the database construction process provided in an embodiment of this application; Figure 5 This is a schematic diagram of the channel coefficient envelope when the main path delay position is 12, provided in an embodiment of this application. Figure 6 This is a schematic diagram of the structure of the self-interference cancellation device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] Figure 1 This is a block diagram illustrating the principle of an existing Interference Cancellation System (ICS). (Refer to...) Figure 1 Existing self-interference cancellation systems estimate the self-interference channel using an adaptive filter, generate a cancellation signal, and cancel out the self-interference components in the received signal. The adaptive filter is based on the error signal. Continuously update filter coefficients It gradually approximates the actual self-interference channel response.

[0023] Specifically, the transmitted signal is output through the transmitting antenna. The transmitted signal contains image frequency components caused by I / Q imbalance. These image components leak to the receiver through self-interference channels, interfering with the reception of the useful signal. After passing through self-interference channels (including paths such as spatial radiation and circuit leakage), a portion of the transmitted signal leaks directly to the receiving antenna, forming a self-interference signal. The received signal is then received by the receiving antenna. Including useful signals and self-interference signals, i.e. . With the output signal of the adaptive filter Subtracting them yields the error signal. .in, As a cancellation signal, it is used to cancel out self-interference signals in the received signal. The adaptive filter is based on the input signal. (i.e., transmitted signal) and error signal The NLMS algorithm is used to update the filter coefficients. ,generate This makes it approximate the self-interference signal. When When approaching a self-interference signal, and After subtraction, the self-interference signal is canceled out. Approaching a useful signal Self-interference is effectively eliminated. The basic relationship of the NLMS algorithm is as follows: in, It is the error signal, that is, the error between the input signal and the adaptive filter; For receiving signals (including self-interference components); It is the output signal of the adaptive filter, that is, the estimated self-interference signal, also known as the cancellation signal. It is the first The filter coefficients of the next iteration (i.e., the channel coefficients in this application). It is the input signal; The iteration step size controls the speed of iteration convergence; These are the filter coefficients for the next iteration; This is to prevent the minimum value where the denominator is zero.

[0024] This iterative update method is a post-hoc compensation approach; that is, the error is generated first, and then the coefficients are adjusted based on the error. Existing digital self-interference cancellation schemes have the following two drawbacks: First, most methods focus on improving the NLMS step size or increasing the number of taps, but these are all post-hoc compensations and cannot cope with sudden, significant changes in channel physical conditions. When the channel changes abruptly, coefficients need to be iteratively adjusted step by step, resulting in a lag in response and a deterioration in self-interference cancellation effectiveness. Second, when self-interference fails to converge, the self-interference cancellation system cannot determine whether the problem is caused by channel changes or base station signals, requiring analysis of large amounts of data such as error signals and received signals, making the calculations and logic quite complex.

[0025] In view of the shortcomings of the prior art, the technical problem to be solved by this application is: First, existing self-interference cancellation schemes cannot cope with sudden and significant changes in channel physical conditions, necessitating a self-interference cancellation method that can detect and predict channel changes in advance. Second, when self-interference fails to converge, existing schemes require complex analysis of large amounts of data, resulting in slow convergence. A self-interference cancellation method that simplifies the analysis process and achieves rapid convergence is needed.

[0026] To address the aforementioned technical problems, this application provides a self-interference cancellation method, apparatus, electronic device, and program product. Through a mechanism of pre-storage, sensing, prediction, and coefficient loading, the performance of self-interference cancellation under channel abrupt changes is guaranteed. Furthermore, this application analyzes the changes in channel coefficients ultimately caused by various abrupt changes, allowing for rapid convergence by focusing solely on these coefficient changes.

[0027] Reference Figure 2 As shown, this application provides a self-interference cancellation method, which may include: S110. Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; S120. Perform self-interference cancellation based on the target channel coefficient; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0028] The aforementioned channel coefficients refer to the channel response parameters received by the receiver after the transmitted signal passes through the self-interference channel in a wireless communication system, including the amplitude of the channel coefficients and the position of the main path delay. The aforementioned trend refers to the macroscopic law of channel coefficient change over time, specifically manifested as the trajectory of channel coefficient changes, such as a gradual increase or decrease in amplitude or a shift in the position of the main path delay. The aforementioned slope refers to the speed or rate of change of the channel coefficients between adjacent sampling points, used to quantify the degree of change in the channel coefficients. The aforementioned target channel coefficient refers to the self-interference cancellation coefficient determined after prediction or event triggering, used to generate a cancellation signal to eliminate self-interference. The steady-state mode in this application refers to the normal operating mode of the communication system, where the adaptive filter coefficients are stable and slowly track the gradual changes in the channel.

[0029] The self-interference cancellation method of this application is described below: The communication system operates in steady-state mode, where the adaptive filter coefficients are stable and slowly track gradual changes in the channel. The system continuously monitors changes in the channel coefficients and also detects system events. Specifically, the system acquires channel coefficients in real time and calculates the slope of change between adjacent sampling points. When the slope of change in the channel coefficients at multiple consecutive sampling points exceeds a preset threshold, a sudden change in the channel is determined. For example, when an object moves near the base station, the channel coefficients change rapidly; if the slope of change at multiple consecutive sampling points exceeds the threshold, the communication system identifies this as a dynamic scene. Simultaneously, the system monitors system events. System events are events triggered by changes in the communication system's own operating state, such as events related to switching between transmit and receive states or gain adjustment. These events do not depend on monitoring changes in the channel coefficients but are directly determined by the communication system's operating state. When such events occur, the communication system can clearly recognize their occurrence.

[0030] Depending on whether the slope of the monitored channel coefficient change exceeds a preset threshold and whether a system event occurs, the communication system uses different methods to determine the target channel coefficient.

[0031] When the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the communication system determines the target channel coefficient based on the change trend. Specifically, the communication system matches the monitored change trend with the pre-stored channel coefficient envelope, predicts the target channel coefficient by matching the change trajectory, and adjusts the current channel coefficient accordingly. Since this application determines the target channel coefficient directly based on the change trend after identifying the sudden change, it no longer relies on the step-by-step iteration in traditional schemes, thus enabling coefficient updates to be completed in a short time when a sudden change occurs in the channel.

[0032] When a system event occurs, the communication system determines the target channel coefficients based on the system event. The communication system determines the target channel coefficients using a method corresponding to the specific type of system event that occurred.

[0033] After determining the target channel coefficient, the communication system generates a cancellation signal with the same amplitude but opposite phase as the self-interference signal. This cancellation signal is then superimposed on the received signal to cancel out the self-interference component in the received signal. Since the target channel coefficient is obtained directly through prediction or event triggering, without the need for the iterative convergence process found in traditional schemes, self-interference cancellation can quickly reach a stable state.

[0034] The self-interference cancellation method provided in this application introduces a real-time monitoring mechanism for the slope of channel coefficient changes. When the slope of changes at multiple consecutive sampling points exceeds a preset threshold, a sudden channel change is immediately determined. Existing self-interference cancellation schemes require iterative updates of coefficients based on error signals after a channel change. Due to the large magnitude of the change, iterative convergence takes a long time, during which self-interference cannot be effectively eliminated. In contrast, this application directly determines the target channel coefficient based on the monitored trend after identifying the change, eliminating the need for iterative updates. Therefore, it can complete coefficient updates in a short time after a channel change, effectively addressing sudden and significant physical changes in the channel. Furthermore, existing self-interference cancellation schemes require analysis of error signals and received data to determine whether the cause is a channel change or base station signal interference when self-interference fails to converge. This analysis process is complex and time-consuming. This application directly selects the corresponding target channel coefficient determination method based on the determination result: when the slope of change exceeds a threshold, the target channel coefficient is determined based on the trend; when a system event occurs, the target channel coefficient is determined based on the system event. This bypasses the complex analysis of error signals and received data, directly obtaining the target channel coefficient. By eliminating the complex analysis process, the convergence speed is significantly improved, thus solving the technical problem of slow convergence speed when self-interference fails to converge.

[0035] This application proposes a reference Figure 3 The interference cancellation system shown here, compared to the existing ICS system, adds a channel feature acquisition module, a database module, and a channel detection module. Figure 3 The mirror filter in this context is the same as the adaptive filter mentioned above.

[0036] The channel feature acquisition module is used during the training phase to obtain the received signal after passing through the self-interference channel by transmitting training sequences such as pseudo-random noise (PN) sequences. From this signal, key features of the channel are extracted, including the amplitude of the channel coefficients, the position of the main path delay, and the envelope of the channel coefficients. This module then transmits the optimal channel coefficients for simulating the channel features in this scenario.

[0037] The database module stores trained rotation factors, channel coefficients, and corresponding channel coefficient envelopes, and retrieves the corresponding parameters based on the index from the channel detection module. The database includes a dynamic database and a static database. The dynamic database stores the channel coefficient envelopes and corresponding velocity and distance information for motion scenarios, while the static database stores the rotation factors and channel coefficients corresponding to different gains of the adjustable attenuator.

[0038] The channel detection module is used to detect channel coefficients, downlink transmit termination events, and changes in adjustable attenuator gain in real time. When the slope of the channel coefficient change continuously exceeds a preset threshold, the abrupt change prediction function is activated, the change trend is sent to the database module for matching, and the result after the predicted channel coefficient replacement is continuously monitored.

[0039] In one embodiment, refer to Figure 4 As shown, the method further includes constructing a database in the following manner: This application uses a channel feature acquisition module to record channel coefficients according to different scenarios, including motion scenarios and static scenarios. The database includes a dynamic database and a static database: ① For the dynamic database, a training sequence is transmitted, and the training sequence signal after passing through the self-interference channel is received. The channel coefficient envelope is extracted from the training sequence signal, and the speed information and distance information of the corresponding moving object are recorded. The channel coefficient envelope and the corresponding speed information and distance information are stored in the dynamic database.

[0040] Specifically, a training sequence is transmitted, and the received signal is the training sequence signal after passing through the self-interference channel. This training sequence signal is the received signal. The training sequence is a specific sequence known to both the transmitter and receiver. After passing through the self-interference channel, the received signal contains the response information of the self-interference channel. The channel coefficient envelope is extracted from this received signal. The channel coefficient envelope refers to the trajectory of the channel coefficients over time, including information such as the amplitude changes of the channel coefficients and the position changes of the principal path delay. Simultaneously, the velocity and distance information of the corresponding moving object are recorded; that is, when acquiring the channel coefficient envelope, the object's moving speed and the spatial distance between the object and the base station are recorded synchronously. The channel coefficient envelope is associated with the corresponding velocity and distance information and stored in a dynamic database, so that the corresponding channel coefficient envelope can be directly retrieved based on the velocity and distance information, or the corresponding channel coefficient envelope can be matched based on the monitored trend of change.

[0041] In a moving scenario, taking an open, unmanned area with a principal path delay position of 12 as an example, when someone moves, the channel coefficient envelope closest to the base station is recorded. For instance, when the principal path delay position changes to 8 at a distance of 5 meters from the base station. In addition to storing the channel coefficient envelope, multiple sampling points are continuously collected to record the trajectory and rate of change of the channel coefficient envelope as the principal path delay position changes from 12 to 8. This is equivalent to recording the weight changes of the entire window function as the object moves at different speeds.

[0042] Reference Figure 5 As shown, Figure 5 This diagram shows the channel coefficient envelope when the main path delay is 12. This envelope describes the variation of the channel coefficients over time and serves as the basis for matching and prediction in dynamic scenarios. The horizontal axis represents time, and the vertical axis represents the amplitude of the channel coefficients.

[0043] This application adjusts the current channel coefficients based on the predicted channel coefficient envelope and determines the object's velocity information based on the trend of the adjusted channel coefficients. If the adjusted channel coefficients still do not converge, i.e., the change in the channel coefficients is not less than or equal to the convergence threshold, the change in the channel coefficients is changed each time based on the velocity information, the channel coefficient envelope is rematched and the channel coefficients are adjusted again until the channel coefficients converge, thus achieving secondary adjustment.

[0044] Motion scenarios involve various speed and distance information. Storing a set of channel coefficient envelopes for each distance location would require significant storage space. This application stores the channel coefficient envelopes using an envelope weighted window, rather than storing a separate set of coefficients for each distance location, thus reducing storage space requirements and simplifying retrieval. Furthermore, this application adjusts the channel coefficients in real-time based on the object's dynamic movement, addressing the channel variation problem caused by object movement.

[0045] Furthermore, if the base station is equipped with a sensing function, it can obtain the speed and distance information of moving objects through the sensing function, and more accurately call the channel coefficient envelope in the dynamic database.

[0046] The static scenarios are divided into adjustable attenuator scenarios and on / off switching scenarios. This application stores the corresponding data in a static database for each of these two scenarios.

[0047] ① Calculate the phase change based on the switching time difference between the uplink and downlink and the local oscillator frequency, generate a rotation factor with the phase change as the exponent, and store the switching time difference and the corresponding rotation factor in the static database.

[0048] In uplink / downlink switching scenarios, both the phase and amplitude of the transmitted signal change. Continuously updating the channel coefficients would worsen the interference cancellation results of the actual signal; using training signals for calibration would require sacrificing some time. This application determines the phase difference by calculating the uplink / downlink switching time difference and the system clock frequency, and uses the channel coefficients to compensate for the phase discontinuity problem.

[0049] Specifically, the duration between the end of the previous downlink cycle and the start of the next downlink cycle (e.g., the length of an uplink time slot) is a fixed value, which is the switching time difference between the uplink and downlink. The local oscillator frequency of the communication system is Let the phase before the switch be The phase after switching is Then the phase change satisfies: The amount of phase change For a fixed value, denoted as Through the rotation factor The channel coefficients are complex-rotated to compensate for the phase difference; the rotation compensation formula is as follows: ,in, The channel coefficients before rotation. These are the rotated channel coefficients. This represents the phase change.

[0050] Based on the above principles, this application calculates the phase change based on the uplink and downlink switching time difference and the local oscillator frequency, and generates a phase change... Rotation factor of the exponent The handover time difference and the corresponding rotation factor are stored in a static database. This way, when a downlink transmission termination event occurs subsequently, the corresponding rotation factor can be retrieved from the static database based on the handover time difference, and the current channel coefficients can be rotated to compensate for phase discontinuities.

[0051] ② Store the channel coefficients corresponding to different gains of the adjustable attenuator into the static database.

[0052] In adjustable attenuator scenarios, the adjustable attenuator records the changing trend of the iterative channel coefficients. In interference cancellation systems, the overall gain remains constant, and the adjustable attenuator is adjusted according to the strength of the received signal. In the original ICS process, the channel changes after the attenuator adjustment, and the communication system needs to continuously iterate and lag in tracking the channel changes caused by the attenuator. This application records the channel coefficients that achieve the optimal self-interference cancellation effect at every 3dB attenuator gain change, establishing a correspondence between attenuator gain and channel coefficients. When the value of the attenuator gain after the change falls between two recorded attenuator gain values ​​(e.g., 3dB and 6dB), the channel coefficients corresponding to this intermediate attenuator gain are calculated using an interpolation method (e.g., linear interpolation).

[0053] Based on the above principle, this application stores the channel coefficients corresponding to different gains of the adjustable attenuator in a static database. Thus, when a subsequent event occurs where the adjustable attenuator gain changes, the corresponding channel coefficients can be retrieved from the static database based on the changed gain and used directly as the target channel coefficients, eliminating the need for iterative tracking.

[0054] For each scenario (including motion scenarios, uplink / downlink switching scenarios, and adjustable attenuator scenarios), this application pre-stores a set of initial filter coefficients for the NLMS algorithm. These initial filter coefficients are set with weights matching the channel coefficients for the corresponding scenario, and include the amplitude and phase information of each tap. When switching to the corresponding scenario, these initial filter coefficients are directly used as the starting values ​​for the adaptive filter, eliminating the need for iteration from zero, thus enabling the algorithm to converge quickly. At this point, the database construction is complete.

[0055] This application extracts the channel coefficient envelope by transmitting training sequences and stores the channel coefficient envelope along with the corresponding velocity and distance information in a dynamic database. In motion scenarios, based on the correspondence between the channel coefficient envelope and the velocity and distance information stored in the dynamic database, the channel coefficient can be adjusted in real time according to the dynamic movement of the object, achieving early perception and prediction of channel changes. When the base station is equipped with sensing capabilities, the channel coefficient envelope in the dynamic database can be accessed more accurately using the velocity and distance information obtained through sensing. Thus, this application solves the technical problem of being unable to cope with sudden and significant physical changes in the channel.

[0056] This application calculates the phase change based on the uplink / downlink handover time difference and the local oscillator frequency, generates a rotation factor, and stores the handover time difference and the corresponding rotation factor in a static database. The phase difference is compensated by complex rotation of the channel coefficients using the rotation factor, eliminating the need for continuous iteration or time-consuming training and calibration. This application also stores the channel coefficients corresponding to different gains of the adjustable attenuator in the static database, using the attenuator gain change as an index to directly retrieve the corresponding channel coefficients without further iterative tracking. Therefore, this application achieves seamless handover in static scenarios, enabling rapid initiation of self-interference cancellation without further training, reducing convergence time and training overhead.

[0057] This application constructs dynamic and static databases and analyzes the changes in channel coefficients. It only needs to focus on the changes in channel coefficients to achieve rapid convergence. It replaces iterative tracking with advance prediction, keeps the self-interference cancellation effect in the best state, reduces the loss and time of retraining, and solves the technical problem of slow convergence speed when self-interference cannot converge.

[0058] In one embodiment, the channel coefficient envelope refers to the variation of the channel coefficient over time. The channel coefficient envelope includes at least one of the following: the amplitude of the channel coefficient, the position of the main path delay, and the trajectory of the channel coefficient variation.

[0059] The amplitude of the channel coefficient refers to the magnitude of the channel coefficient, reflecting the change in the strength of the self-interference signal. When an object moves near the base station, the path loss of the self-interference channel changes, causing the amplitude of the channel coefficient to change accordingly.

[0060] The dominant path delay position refers to the relative delay of the most energetic path in a multipath channel, also known as the tap index. When an object moves near a base station, the propagation path length of the dominant path changes, causing a shift in the dominant path delay position. For example, when an object approaches the base station, the dominant path delay position decreases; when the object moves away from the base station, the dominant path delay position increases.

[0061] The channel coefficient variation trajectory refers to a complete record of the channel coefficient changes over time, including the variation trajectory of amplitude and the variation trajectory of principal path delay position. By continuously collecting channel coefficients at multiple sampling points, its variation trajectory can be recorded, thereby allowing analysis of the variation patterns and rates of change of the channel coefficients.

[0062] In practical applications, the channel coefficient envelope can contain only one of the three items mentioned above, or it can contain multiple items. For example, in scenarios where only signal strength changes are of interest, only the amplitude of the channel coefficients can be recorded; in scenarios where delay changes are of interest, only the position of the main path delay can be recorded; in scenarios where a complete analysis of the channel change pattern is required, the amplitude, the position of the main path delay, and the change trajectory can be recorded simultaneously.

[0063] This application sets the channel coefficient envelope to include at least one of the following: the amplitude of the channel coefficient, the position of the main path delay, and the trajectory of the channel coefficient change. This allows the channel coefficient envelope to be flexibly configured according to the actual application scenario. When the channel coefficient envelope includes the amplitude, it can reflect the intensity change of the self-interference signal; when it includes the position of the main path delay, it can reflect the delay offset caused by the movement of the object; when it includes the trajectory of change, it can completely record the change law of the channel coefficient over time. By associating and storing the channel coefficient envelope with velocity and distance information, accurate description and prediction of channel changes are achieved, providing sufficient basis for subsequent matching and adjustment, thereby improving the accuracy and convergence speed of self-interference cancellation.

[0064] In one embodiment, when the slope of the channel coefficient change at multiple consecutive sampling points all exceeds a preset threshold, the communication system determines the target channel coefficient based on the change trend. The determination of the target channel coefficient based on the change trend includes: The changing trend is matched with the channel coefficient envelope in the dynamic database to obtain the target channel coefficient envelope; The target channel coefficients are determined based on the target channel coefficient envelope.

[0065] Specifically, firstly, the communication system matches the monitored trend of change with the channel coefficient envelopes pre-stored in the dynamic database. The trend of change refers to the regularity of channel coefficient changes over time, specifically manifested as the amplitude change trajectory composed of the amplitude changes of multiple consecutive sampling points within a certain time window, and the principal path delay position change trajectory composed of the principal path delay position changes of multiple consecutive sampling points. The dynamic database stores channel coefficient envelopes under different motion scenarios. Each channel coefficient envelope is a channel coefficient change trajectory, including at least one of the amplitude change trajectory and the principal path delay position change trajectory, and is stored in association with the corresponding speed and distance information.

[0066] One specific implementation of the matching is as follows: The currently monitored trend is represented as a vector. For example, the amplitude changes and main path delay position changes of the channel coefficients at multiple consecutive sampling points are combined into a feature vector. Simultaneously, the amplitude change trajectory and main path delay position change trajectory in each channel coefficient envelope in the dynamic database are also represented as feature vectors of the same dimension. Then, the Euclidean distance or cosine similarity between the feature vector of the current trend and the feature vector of each channel coefficient envelope in the database is calculated, and the channel coefficient envelope with the highest similarity (i.e., the smallest distance) is selected as the matching result. For example, when the amplitude of the channel coefficient is detected to increase from 10 to 15 within 5 sampling points, and the main path delay position decreases from 12 to 8, these changes are used to construct a feature vector. The dynamic database is then searched for the channel coefficient envelope whose amplitude change trajectory and main path delay position change trajectory are closest to this feature vector. Through this matching, the communication system obtains the target channel coefficient envelope, that is, the channel coefficient envelope most similar to the current trend.

[0067] Then, the communication system determines the target channel coefficient based on the obtained target channel coefficient envelope. The target channel coefficient envelope includes amplitude variation trajectories and principal path delay position variation trajectories, which describe the amplitude and principal path delay positions that the channel coefficient should reach at different time points. Based on its current position within the variation trajectory, the communication system reads the target amplitude and target principal path delay position corresponding to the current time from the target channel coefficient envelope. Then, it adjusts the amplitude of the current channel coefficient to the target amplitude and adjusts the principal path delay position of the current channel coefficient to the target principal path delay position. After these adjustments, the adjusted channel coefficient matches the target channel coefficient envelope at the current time, and this adjusted channel coefficient is taken as the target channel coefficient.

[0068] Since this application determines the target channel coefficients directly by matching the pre-stored channel coefficient envelope after identifying the mutation, without going through the stepwise iterative convergence process in the existing self-interference cancellation scheme, the coefficient update can be completed in a short time when the channel mutation occurs.

[0069] This application directly obtains the target channel coefficient envelope that most closely resembles the current trend by matching the monitored trend with the channel coefficient envelope in a dynamic database, and then determines the target channel coefficient based on this target channel coefficient envelope. Compared with the prior art, which requires iterative updates of coefficients using error signals after a sudden channel change, this application skips the iterative convergence process by matching the pre-stored channel coefficient envelope, directly obtaining the target channel coefficient. This allows for coefficient updates within a short time after a sudden channel change, achieving rapid response to sudden and significant physical changes in the channel and solving the technical problem that existing technologies cannot cope with sudden channel changes. Furthermore, this application determines the target channel coefficient through matching, eliminating the need for complex analysis of error signals and received signals, simplifying the calculation logic, and improving the convergence speed.

[0070] In one embodiment, when the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the communication system enters a dynamic scenario processing flow. Determining the target channel coefficient based on the target channel coefficient envelope includes: S210. Adjust the amplitude and main path delay position of the current channel coefficient according to the target channel coefficient envelope to obtain the adjusted channel coefficient.

[0071] The target channel coefficient envelope contains the amplitude variation trajectory and the main path delay position variation trajectory. These trajectories describe the amplitude and main path delay position that the channel coefficient should reach at different time points. The communication system takes the moment when a sudden change in the channel is detected as the starting point, calculates the time offset from that starting point to the current moment, and reads the target amplitude and target main path delay position at the corresponding time point from the target channel coefficient envelope based on this time offset. The communication system adjusts the amplitude of the current channel coefficient to the target amplitude and adjusts the main path delay position of the current channel coefficient to the target main path delay position, obtaining the adjusted channel coefficient.

[0072] S220. If the adjusted channel coefficients do not converge, obtain the updated trend of change, rematch the target channel coefficient envelope according to the updated trend of change, update the adjustment range of the channel coefficients, adjust the channel coefficients according to the updated adjustment range until the channel coefficients converge, and take the channel coefficients at the time of convergence as the target channel coefficients.

[0073] The adjustment range mentioned above refers to the amount of change when adjusting the channel coefficients each time, i.e., the iteration step size in the NLMS algorithm. When an object moves at a high speed, the adjustment range needs to be increased to adapt to rapid changes; when the object moves at a low speed, the adjustment range needs to be decreased for fine-tuning. Convergence refers to the coefficients of the adaptive filter stabilizing, with the change in channel coefficients being less than or equal to the convergence threshold. When the channel coefficients converge, self-interference cancellation reaches a stable state. The convergence threshold is a threshold value used to determine whether the channel coefficients have converged. When the change in channel coefficients is less than or equal to this threshold, the channel coefficients are considered to have converged.

[0074] The communication system determines whether the adjusted channel coefficients have converged. If the adjusted channel coefficients have converged, that is, the change in the channel coefficients is less than or equal to the convergence threshold, it indicates that the communication system has entered a stable state. The current adjusted channel coefficients are then used as the target channel coefficients, and the system returns to the steady-state mode.

[0075] If the adjusted channel coefficients fail to converge, meaning the change in channel coefficients exceeds the convergence threshold, it indicates that the communication system is not yet stable and enters the secondary adjustment process. During this secondary adjustment, the communication system continuously monitors the channel coefficients and constantly collects new sampling points. As time progresses and the object continues to move, the trajectory of the channel coefficient changes lengthens, forming an updated trend.

[0076] First, the communication system re-matches the channel coefficient envelope in the dynamic database based on the updated trend to obtain a new target channel coefficient envelope. This new target channel coefficient envelope records the trajectory of channel coefficient changes over time in a specific motion scenario, including amplitude variation trajectory and principal path delay position variation trajectory. Here, time refers to the cumulative time calculated from the moment a sudden change in the channel is detected.

[0077] Secondly, the communication system calculates the object's velocity based on the updated trend of change. Specifically, within a certain time window... Inside, record the change in the position of the principal path delay. The speed of an object ,in This is a proportionality constant, related to the speed of light and the system sampling rate. A decrease in the principal path delay position indicates the object is closer to the base station, while an increase indicates the object is farther away.

[0078] The communication system updates the adjustment range based on this speed. Let the current adjustment range be... The updated adjustment range is set to ,in This is a proportionality coefficient. The faster the speed, the larger the adjustment range; the slower the speed, the smaller the adjustment range.

[0079] The communication system uses the moment a channel abrupt change is detected as the starting point. It calculates the time offset from this starting point to the current moment and reads the target amplitude and target principal path delay position at the corresponding time point from the new target channel coefficient envelope based on this time offset. The communication system determines the adjustment direction based on the direction of the difference between the target amplitude and the current channel coefficient amplitude: if the current channel coefficient amplitude is less than the target amplitude, the current channel coefficient amplitude is increased; if the current channel coefficient amplitude is greater than the target amplitude, the current channel coefficient amplitude is decreased. The updated adjustment magnitude is used as the change amount for a single adjustment, increasing or decreasing the current channel coefficient amplitude by one adjustment magnitude. Similarly, the adjustment direction is determined based on the direction of the difference between the target principal path delay position and the current principal path delay position, and the updated adjustment magnitude is used as the change amount for a single adjustment, increasing or decreasing the current principal path delay position by one adjustment magnitude. After adjustment, the updated current channel coefficient is obtained and used as the current channel coefficient for the next iteration.

[0080] Repeat the above process: continuously monitor the channel coefficients to obtain the updated trend of change, rematch the new target channel coefficient envelope based on the updated trend of change, calculate the motion speed and update the adjustment magnitude based on the updated trend of change, take the current channel coefficient updated in the previous iteration as the starting point, increase or decrease the amplitude and main path delay position of the current channel coefficient by one adjustment magnitude according to the updated adjustment magnitude and the new target channel coefficient envelope, until the channel coefficients converge, and take the channel coefficient at the convergence point as the target channel coefficient.

[0081] This application obtains adjusted channel coefficients by adjusting the amplitude and principal path delay position of the current channel coefficients based on the target channel coefficient envelope. When the channel coefficients have not converged, the updated trend of change is obtained. Based on this updated trend, the target channel coefficient envelope is re-matched, and the adjustment amplitude of the channel coefficients is updated. The channel coefficients are then adjusted according to the updated adjustment amplitude until convergence, and the converged channel coefficients are used as the target channel coefficients. Compared to existing technologies that require fixed-step iterations after channel abrupt changes, this application, if convergence is not achieved after the initial adjustment, can dynamically update the adjustment amplitude based on the updated trend, enabling secondary adjustment. This allows the envelope to better match the object's motion speed, thereby accelerating the convergence speed and solving the technical problem of slow convergence when self-interference fails to converge. Furthermore, by re-matching the target channel coefficient envelope based on the updated trend, this application ensures that each adjustment is based on the latest channel change information, improving the accuracy and stability of self-interference cancellation.

[0082] In one embodiment, the system event includes at least one of a downlink transmit termination event and an adjustable attenuator gain change event.

[0083] The downlink transmit end event refers to the event triggered when the downlink transmit cycle of the communication system ends. When this event occurs, the transmit and receive states switch, and the phase of the self-interference channel jumps. The adjustable attenuator gain change event refers to the event triggered when the gain value of the adjustable attenuator changes. In an interference cancellation system, the overall gain remains constant, and the adjustable attenuator is adjusted according to the strength of the received signal. When the received signal strength changes, the adjustable attenuator gain adjusts accordingly, triggering this event.

[0084] This application clarifies the types of system events that the self-interference cancellation method needs to respond to by defining system events as including at least one of downlink transmission termination events and adjustable attenuator gain change events. Compared with the prior art, which involves uncertainty about whether the interference is caused by channel changes or base station signals and requires analysis of large amounts of data, this application clearly defines the specific types of system events, distinguishing between changes in the system's own state and changes in the external channel. This enables the communication system to identify and respond to these events, thereby simplifying the convergence analysis process and improving the response speed of self-interference cancellation.

[0085] In one embodiment, determining the target channel coefficients based on the system event includes any one of the following: ① If a downlink transmission termination event occurs, the corresponding rotation factor is obtained from the static database based on the switching time difference between the uplink and downlink. The current channel coefficient is then rotated using the rotation factor to obtain the target channel coefficient.

[0086] Switching time difference This refers to the duration from the end of the previous downlink cycle to the start of the next downlink cycle, such as the length of an uplink time slot. Rotation factor. Through phase change The calculation yielded, where , This refers to the local oscillator frequency of the communication system. In the static database, the switching time difference and rotation factor are stored correspondingly.

[0087] The communication system performs a complex rotation on the current channel coefficients using the acquired rotation factor. This involves calculating the product of the current channel coefficients and the rotation factor to obtain the rotated channel coefficients. The rotated channel coefficients compensate for the phase discontinuity caused by uplink / downlink handover, and are used as the target channel coefficients.

[0088] ② If an adjustable attenuator gain change event occurs, the corresponding channel coefficient is obtained from the static database based on the changed adjustable attenuator gain, and used as the target channel coefficient.

[0089] The static database pre-stores channel coefficients corresponding to different gains of the adjustable attenuator. Specifically, for every 3dB gain change of the adjustable attenuator, the channel coefficient that achieves the optimal self-interference cancellation effect at that gain is recorded, establishing a correspondence between the adjustable attenuator gain and the channel coefficients. When the changed gain value falls between two recorded gain values ​​(e.g., 3dB and 6dB), the channel coefficient corresponding to this intermediate gain is calculated using an interpolation method (e.g., linear interpolation). The communication system uses the changed adjustable attenuator gain as an index to search for the corresponding channel coefficient in the static database, directly using this channel coefficient as the target channel coefficient without needing an iterative tracking process.

[0090] This application compensates for the phase discontinuity caused by uplink / downlink handover by obtaining a rotation factor from a static database based on the handover time difference when a downlink transmission termination event occurs, and performing a complex rotation on the current channel coefficients to obtain the target channel coefficients. Compared with existing technologies that require continuous coefficient updates or calibration using training signals, this application directly rotates the channel coefficients by pre-storing the rotation factor, eliminating the need for continuous iteration or time-consuming training and calibration. This avoids the deterioration of interference cancellation effects caused by phase jumps and achieves rapid determination of the target channel coefficients in phase discontinuity scenarios. This application also obtains the corresponding channel coefficients from a static database as the target channel coefficients when an adjustable attenuator gain change event occurs, based on the changed gain. Compared with existing technologies that require continuous iterative tracking of channel changes caused by attenuators, this application directly obtains the corresponding channel coefficients based on the changed gain by pre-storing the correspondence between gain and channel coefficients, eliminating the need for further iterative tracking and achieving one-step determination of the target channel coefficients in gain change scenarios. Therefore, this application achieves seamless handover in static scenarios, quickly determining the target channel coefficients and initiating self-interference cancellation without further training, reducing convergence time and training overhead.

[0091] In one embodiment, the method further includes: If the base station has a sensing function, it can obtain the speed and distance information of the moving object through the sensing function, determine the target channel coefficient envelope from the dynamic database based on the speed and distance information, and determine the target channel coefficient based on the target channel coefficient envelope.

[0092] Specifically, the base station uses sensing capabilities to transmit sensing signals and receives echo signals reflected by moving objects. It then extracts the speed and distance information of the moving object from these echo signals. The speed information includes the magnitude and direction of the object's movement (whether it is moving closer to or further from the base station), and the distance information includes the spatial distance between the object and the base station.

[0093] The communication system determines the target channel coefficient envelope from a dynamic database based on the acquired speed and distance information. The dynamic database pre-stores channel coefficient envelopes corresponding to different speed and distance information. Using the currently acquired speed and distance information as indexes, the communication system searches the dynamic database for a matching channel coefficient envelope and uses that envelope as the target channel coefficient envelope. The communication system then determines the target channel coefficients based on this target channel coefficient envelope. Specifically, the target channel coefficient envelope includes the amplitude variation trajectory and the principal path delay position variation trajectory. The communication system uses the moment a sudden change in channel velocity is detected as the starting point, calculates the time offset from that point to the current moment, and reads the target amplitude and target principal path delay position at the corresponding time point from the target channel coefficient envelope based on this time offset. The current channel coefficients are then adjusted to match the target channel coefficients, ensuring that the current channel coefficient amplitude equals the target amplitude and the principal path delay position equals the target principal path delay position.

[0094] By directly acquiring speed and distance information through sensing capabilities, dynamic scene processing can be triggered without relying on monitoring the slope of channel coefficient changes, enabling faster and more accurate matching of the corresponding channel coefficient envelope.

[0095] This application utilizes the sensing function of a base station to acquire the speed and distance information of moving objects. Based on the acquired speed and distance information, it directly determines the target channel coefficient envelope from a dynamic database and then determines the target channel coefficient based on the target channel coefficient envelope. When the base station has sensing capabilities, this application provides an alternative dynamic scene processing method to trend-based matching. Compared to trend-based matching, which requires continuous monitoring of the channel coefficient change slope, sensing can directly acquire the motion parameters of the object, quickly matching the corresponding channel coefficient envelope without accumulating sampling points, further shortening the dynamic scene recognition time. Simultaneously, the speed and distance information directly provided by sensing is independent of the motion information inferred from trend-based matching, allowing for flexible selection based on base station configuration, thus improving the adaptability and flexibility of self-interference cancellation.

[0096] The self-interference cancellation method provided in this application includes a steady-state operating stage, a channel detection stage, a sudden change prediction stage, a prediction loading stage, a secondary adjustment stage, and a sensing-assisted stage. The operating stages are described in detail below.

[0097] Step a, Steady-state operating phase: The communication system operates in steady-state mode, where the channel coefficients of the NLMS algorithm are stable, and their slow changes track gradual channel variations. The communication system continuously monitors downlink transmit termination events and adjustable attenuator gain change events. When a downlink transmit termination or adjustable attenuator gain change is detected, the system switches to step b.

[0098] Step b, Static Switching Phase: When the downlink transmission signal is completed, in order to make up for the phase difference between the next transmission signal and the current channel, the communication system loads the pre-stored rotation factor from the static database, uses the rotation factor to perform a complex rotation on the current channel coefficients, and uses the rotated channel coefficients as the target channel coefficients.

[0099] When the adjustable attenuator's gain changes, the communication system synchronously switches to the corresponding channel coefficient from the static database based on the changed attenuator gain. The static database pre-stores the channel coefficients corresponding to different attenuator gains. The communication system uses the changed gain as an index to find the corresponding channel coefficient and uses that channel coefficient as the target channel coefficient.

[0100] After the coefficient switching is complete, the communication system returns to step a.

[0101] Step c, Channel Detection Phase: The communication system monitors the slope of channel characteristic changes in real time. When the slope of the channel coefficient changes exceeds a preset threshold, step d is initiated. If the slope does not exceed the preset threshold, the system returns to step a.

[0102] Step d: Mutation prediction stage: The communication system monitors the slope of channel coefficient changes in real time. When the slope exceeds a preset threshold, counting begins. If there are fewer than N sampling points where the slope exceeds the threshold, the system returns to step a.

[0103] When the slope of the channel coefficient change at N consecutive sampling points exceeds a preset threshold, the NLMS algorithm can no longer track the channel effectively, and the communication system begins recording the channel coefficient change trajectory. The communication system matches the current change trend with the change trend of the channel coefficient envelope pre-stored in the dynamic database, and after the matching is completed, it switches to step f.

[0104] Step f: Predictive loading phase: For dynamic scenarios, the communication system adjusts the amplitude and principal path delay position of the current channel coefficients based on the target channel coefficient envelope obtained through matching, thus obtaining the adjusted channel coefficients and achieving the effect of predicting the channel coefficients of the moving object in the next step. The communication system uses these adjusted channel coefficients as the initial channel coefficients for the NLMS algorithm. The NLMS algorithm is based on the iterative formula. The channel coefficients are updated incrementally to more closely approximate the actual self-interference channel response, thereby achieving rapid convergence. The iteration step size is specified in the original text. The current adjustment range is used. Once the channel coefficients converge, the communication system returns to step a. If the channel coefficients remain unstable after step f, the communication system proceeds to step g.

[0105] Step g: Secondary adjustment stage: If the object's motion has not yet stopped or the predicted velocity information is problematic, the communication system adjusts the adjustment range of the channel coefficient in step f (i.e., the iteration step size of the NLMS algorithm) based on the changing trend of the channel coefficient. This allows the channel coefficient envelope to better match the object's motion velocity. The communication system re-matches the channel coefficient envelope and adjusts the channel coefficients based on the updated adjustment magnitude, obtaining the adjusted channel coefficients. These adjusted channel coefficients are then used as the initial channel coefficients for the NLMS algorithm. The NLMS algorithm updates the channel coefficients according to the above iterative formula, making the channel coefficients closer to the real self-interference channel response. The above process is repeated until the channel coefficients converge.

[0106] When the base station has sensing capabilities, the processing flow from steps d to g can be simplified. When an object approaches a distance that affects the self-interference channel, the communication system senses the object's distance and speed information through sensing, directly calls the corresponding channel coefficient envelope in the dynamic database, and determines the target channel coefficient based on this envelope, achieving real-time and accurate channel coefficient matching. Specifically, the base station uses sensing to transmit sensing signals and receives the echo signals reflected by the moving object, extracting the object's speed and distance information from the echo signals. Using the acquired speed and distance information as indexes, the communication system searches for a matching channel coefficient envelope in the dynamic database, uses this envelope as the target channel coefficient envelope, and directly determines the target channel coefficient based on it, without needing to go through steps c, d, f, and g.

[0107] The self-interference cancellation device provided in the embodiments of this application is described below. The self-interference cancellation device described below can be referred to in correspondence with the self-interference cancellation method described above.

[0108] The self-interference cancellation device provided in this application embodiment refers to... Figure 6 As shown, it includes: The channel coefficient determination module 310 is used to determine the target channel coefficient based on the changing trend of the channel coefficient or system events. Self-interference cancellation module 320 is used to perform self-interference cancellation based on the target channel coefficient; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0109] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other via the communication bus 440. The processor 410 can call a computer program in the memory 430 to execute a self-interference cancellation method, such as including: Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; Self-interference cancellation is performed based on the target channel coefficients; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0110] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0111] On the other hand, embodiments of this application also provide a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can perform the self-interference cancellation methods provided in the above embodiments, such as including: Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; Self-interference cancellation is performed based on the target channel coefficients; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0112] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the self-interference cancellation methods provided in the above embodiments, including, for example: Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; Self-interference cancellation is performed based on the target channel coefficients; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend; if a system event occurs, the target channel coefficient is determined based on the system event.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0114] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for eliminating self-interference, characterized in that, include: Determine the target channel coefficient based on the changing trend of the channel coefficient or system events; Self-interference cancellation is performed based on the target channel coefficients; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend. If a system event occurs, the target channel coefficients are determined based on the system event.

2. The self-interference cancellation method according to claim 1, characterized in that, The method also includes constructing a database, which includes both dynamic and static databases, in the following manner: Transmit a training sequence, receive the training sequence signal after passing through the self-interference channel, extract the channel coefficient envelope from the training sequence signal, and record the speed and distance information of the corresponding moving object. Store the channel coefficient envelope and the corresponding speed and distance information into the dynamic database. The phase change is calculated based on the switching time difference between the uplink and downlink and the local oscillator frequency, and a rotation factor with the phase change as the exponent is generated. The switching time difference and the corresponding rotation factor are stored in the static database. The channel coefficients corresponding to different gains of the adjustable attenuator are stored in the static database.

3. The self-interference cancellation method according to claim 2, characterized in that, Determining the target channel coefficient based on the changing trend includes: The changing trend is matched with the channel coefficient envelope in the dynamic database to obtain the target channel coefficient envelope; The target channel coefficients are determined based on the target channel coefficient envelope.

4. The self-interference cancellation method according to claim 3, characterized in that, Determining the target channel coefficients based on the target channel coefficient envelope includes: The amplitude and main path delay position of the current channel coefficients are adjusted according to the target channel coefficient envelope to obtain the adjusted channel coefficients; If the adjusted channel coefficients do not converge, the updated trend of change is obtained, the target channel coefficient envelope is rematched according to the updated trend of change, and the adjustment range of the channel coefficients is updated. The channel coefficients are adjusted according to the updated adjustment range until the channel coefficients converge, and the channel coefficients at the time of convergence are taken as the target channel coefficients.

5. The self-interference cancellation method according to claim 2, characterized in that, The channel coefficient envelope includes at least one of the following: the amplitude of the channel coefficient, the position of the main path delay, and the trajectory of the channel coefficient change.

6. The self-interference cancellation method according to claim 1, characterized in that, The system events include at least one of the following: downlink transmit termination event and adjustable attenuator gain change event.

7. The self-interference cancellation method according to claim 6, characterized in that, The determination of the target channel coefficient based on the system event includes any one of the following: If a downlink transmission termination event occurs, the corresponding rotation factor is obtained from the static database based on the uplink and downlink switching time difference. The current channel coefficient is then rotated using the rotation factor to obtain the target channel coefficient. If an adjustable attenuator gain change event occurs, the corresponding channel coefficient is obtained from the static database based on the changed adjustable attenuator gain, and used as the target channel coefficient.

8. The self-interference cancellation method according to claim 1, characterized in that, The method further includes: If the base station has a sensing function, it can obtain the speed and distance information of the moving object through the sensing function, determine the target channel coefficient envelope from the dynamic database based on the speed and distance information, and determine the target channel coefficient based on the target channel coefficient envelope.

9. A self-interference cancellation device, characterized in that, include: The channel coefficient determination module is used to determine the target channel coefficient based on the changing trend of the channel coefficient or system events; The self-interference cancellation module is used to perform self-interference cancellation based on the target channel coefficient; If the slope of the channel coefficient change at multiple consecutive sampling points exceeds a preset threshold, the target channel coefficient is determined based on the change trend. If a system event occurs, the target channel coefficients are determined based on the system event.

10. An electronic device comprising a processor and a memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the self-interference cancellation method according to any one of claims 1 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the self-interference cancellation method according to any one of claims 1 to 8.

12. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the self-interference cancellation method according to any one of claims 1 to 8.

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