Method, system, automobile and storage medium for processing wireless communication signals

Abstract

The application provides a wireless communication signal processing method, system, automobile and storage medium, aiming at improving the technical problem of interference. The method part comprises the following steps: determining the interference degree based on the channel state data, and analyzing the service data to determine the dynamic transmission priority of the corresponding communication link; based on the interference degree and the dynamic transmission priority, a cooperative scheduling strategy is generated to control the anti-interference transmission of the transmitting end corresponding to the communication link; a mixed signal is received through a signal receiving link, the mixed signal contains corresponding interference noise and a target communication signal carrying service data; the target communication signal is extracted from the mixed signal; the real-time communication performance index of the target communication signal is monitored, the transmission parameters of the transmitting end are adjusted based on the real-time communication performance index, and the real-time communication performance index is fed back to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, system, vehicle, and storage medium for processing wireless communication signals. Background Technology

[0002] With the rapid development of electronic communication technology, highly integrated smart car cockpits typically deploy multiple WiFi hotspots and Bluetooth devices simultaneously. Since both WiFi and Bluetooth primarily operate in the overlapping 2.4GHz frequency band, their radio frequency signals are highly susceptible to collisions within the confined space of a car cabin filled with metallic reflectors, leading to severe co-channel interference and multipath effects.

[0003] To mitigate this interference, existing technologies typically employ adaptive frequency hopping or packet arbitration. However, these existing solutions are essentially limited to scheduling at the Media Access Control (MAC) layer or protocol communication layer: adaptive frequency hopping passively avoids frequency points, while packet arbitration merely separates the data transmission timing of WiFi and Bluetooth in the time domain. This purely MAC-layer intervention mechanism has significant physical limitations. When air interface signals undergo complex physical overlap due to metal reflections within the vehicle, even if the transmitter avoids interference in protocol time or frequency bands, the receiver antenna still captures a mixed signal containing severe multipath echoes and residual noise. Simple MAC-layer time or frequency scheduling is completely ineffective against this spatially and physically superimposed clutter.

[0004] Therefore, a core technical problem that urgently needs to be solved in the existing technology is: in the complex electromagnetic environment of multi-metal reflection, how to overcome the limitations of pure MAC layer protocol scheduling, effectively deal with and eliminate multipath echoes and residual co-channel interference that have physically superimposed at the receiving end, so as to reduce interference. Summary of the Invention

[0005] This application provides a method, system, vehicle, and storage medium for processing wireless communication signals, aiming to improve the technical problem of dealing with and eliminating multipath echoes and residual co-channel interference that have physically superimposed at the receiving end, thereby reducing interference.

[0006] A method for processing wireless communication signals, comprising: Acquire channel status data of the wireless environment, as well as service data on the corresponding communication link; The interference level is determined based on the channel state data, and the service data is parsed to determine the dynamic transmission priority corresponding to the communication link; Based on the interference level and the dynamic transmission priority, a collaborative scheduling strategy is generated to perform anti-interference transmission control on the transmitter corresponding to the communication link. The mixed signal is received through a signal receiving link. The mixed signal includes corresponding interference noise and the target communication signal carrying the service data. An adaptive filter located at the front end of the baseband demodulator in the signal receiving link is used to filter the mixed signal in order to extract the target communication signal from the mixed signal. The system monitors the real-time communication performance indicators of the target communication signal, adjusts the transmission parameters of the transmitting end based on the real-time communication performance indicators, and feeds the real-time communication performance indicators back to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter.

[0007] A wireless communication signal processing system, comprising: The spectrum sensing unit is used to acquire channel state data of the wireless environment; A collaborative scheduler is used to acquire service data on the corresponding communication link, determine the level of interference based on the channel state data, and parse the service data to determine the dynamic transmission priority of the corresponding communication link. Based on the interference level and the dynamic transmission priority, a collaborative scheduling strategy is generated to perform anti-interference transmission control on the transmitter corresponding to the communication link. An adaptive filter located at the front end of the baseband demodulator in the signal receiving link is used to receive a mixed signal through the signal receiving link. The mixed signal contains corresponding interference noise and a target communication signal carrying the service data. The filter is used to extract the target communication signal from the mixed signal. The cooperative scheduler is also used to monitor the real-time communication performance indicators of the target communication signal, adjust the transmission parameters of the transmitter based on the real-time communication performance indicators, and feed the real-time communication performance indicators back to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter.

[0008] An automobile includes a system for processing the wireless communication signals.

[0009] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the wireless communication signal processing method described in any of the preceding claims.

[0010] In one of the solutions provided in this application, a complete perception-decision-execution-feedback closed-loop control mechanism is constructed through the above steps. In this mechanism, this embodiment creatively transcends the protocol layer, intervening at the baseband demodulator front end of the signal receiving link, and using an adaptive filter for underlying physical layer signal processing. In the pre-processing stage of baseband demodulation, the system uses a mixed signal containing the target communication signal and interference noise as input for filtering, and continuously monitors the real-time communication performance indicators after demodulation, feeding these indicators back to the adaptive filter. The system can dynamically adjust the digital filtering operator parameters of the adaptive filter. This mechanism, which combines front-end scheduling and filtering based on interference level and dynamic transmission priority with back-end parameter dynamic updates, enables the system to exhibit anti-interference capabilities exceeding those of existing fixed hardware or pure protocol layer scheduling schemes when dealing with complex multipath echoes in vehicles, thereby ensuring the purity of the target signal and the robustness of the system. Attached Figure Description

[0011] Figure 1 This is a schematic diagram of a wireless communication signal processing system according to an embodiment of this application; Figure 2 This is a flowchart illustrating a method for processing wireless communication signals according to an embodiment of this application; Figure 3 This is another flowchart illustrating a method for processing wireless communication signals provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating how the method provided in the embodiments of this application responds to and resolves a real-world interference event. Detailed Implementation

[0012] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0013] Terminology Explanation To enable those skilled in the art to better understand the technical solutions of the embodiments of this application, before describing the embodiments of this application in detail, some core technical terms involved in the embodiments of this application will first be explained and described: 1. Co-channel interference and adjacent-channel interference Co-channel interference refers to the mutual interference phenomenon caused by overlapping operating channel frequencies between different wireless communication devices operating in the same frequency band (such as the 2.4GHz industrial, scientific, and medical frequency band shared by WiFi and Bluetooth in this application). In the vehicle-mounted scenario of this application, it mainly manifests as a collision between the fixed channel of WiFi and the frequency-hopping channel of Bluetooth in the air.

[0014] Adjacent channel interference: refers to interference caused by signals in adjacent or similar channels, due to energy leakage in the transmitter spectrum or insufficient out-of-band suppression in the receiver, resulting in energy falling into the passband of the receiver in the same channel.

[0015] 2. Multipath effect Multipath effect refers to the physical phenomenon where radio waves encounter obstacles during transmission, causing reflection, refraction, or scattering, resulting in signals reaching the receiving antenna through multiple different paths with varying time delays. In the confined space of a car filled with metallic reflectors, multipath effect can easily lead to signal fading or severe self-interference (echoes).

[0016] 3. Channel occupancy rate A key indicator for measuring the congestion level of a wireless physical channel is defined as the proportion of time within a set observation time window during which the channel is covered by radio frequency signal energy (i.e., the signal strength exceeds a specific threshold and is judged to be in a "busy" state).

[0017] 4. Dynamic transmission priority Unlike traditional fixed priorities in device hardware or software, the dynamic transmission priority in this application refers to the system dynamically assigning a transmission permission level to the communication link based on the current service's requirements for real-time performance, latency sensitivity, and security by analyzing the transmitted data packets or service streams in real time. For example, an ongoing Bluetooth voice call might be temporarily given a higher priority than a background WiFi file download.

[0018] 5. Time Division Multiplexing (TDR) scheduling A wireless resource allocation strategy. When multiple services cause time-domain conflicts, the scheduler divides the channel into different time slices (transmission windows) on the time axis by issuing control signaling (such as hold or sleep signals) and interleaves them to services of different priorities, thereby completely isolating interference sources in the time domain and preventing signals from being transmitted simultaneously.

[0019] 6. Adaptive Filter A digital signal processing device deployed at the front end of the baseband demodulator in the receiver. It can automatically adjust its internal structural parameters (such as digital filter operator parameters / tap weight coefficients) according to the received mixed signal using a specific recursive algorithm, thereby constructing an inverse filter with the opposite characteristics to the interference in real time to filter out residual interference and extract the clean signal.

[0020] 7. Least Mean Square (LMS) and Recursive Least Squares (RLS) Least Mean Square (LMS) algorithm: An adaptive iterative algorithm that aims to minimize the mean square value of the error signal (the difference between the desired signal and the filter output). It is characterized by low computational cost and ease of implementation.

[0021] Recursive Least Squares (RLS) algorithm: An adaptive algorithm based on the least squares criterion. Compared with the LMS algorithm, it converges faster in complex environments and can adapt more quickly to rapid changes in channel conditions. In this application, it is used to drive the adaptive filter to perform iterative calculation of weight coefficients.

[0022] like Figure 1 As shown, in order to enable those skilled in the art to better understand the technical solutions of the embodiments of this application, before describing the embodiments of this application in detail, the relationship between the modules of the overall system is explained by taking one of the wireless communication signal processing systems applied in an automotive application scenario as an example.

[0023] according to Figure 1 The text identifiers and data flow within the system, and the overall system's operational mechanism can be comprised of the following four core components: 1. External environment and interference threats like Figure 1 As shown at the top, the wireless communication signal processing system operates in a real external environment, specifically the complex electromagnetic environment inside the vehicle and the 2.4GHz band interference source. This objectively existing harsh environment directly causes co-channel interference to the WiFi and Bluetooth modules below, which is the physical prerequisite for activating the anti-interference mechanism of this processing system.

[0024] 2. Two-way input of perception and demand To counter interference, a sensing network that aggregates front-end data is deployed within the system: On one hand, the spectrum sensing unit is responsible for detecting the external environment in real time and continuously transmitting real-time spectrum data to the central coordinating scheduler. On the other hand, the WiFi and Bluetooth modules, while running their own services (corresponding to the status / service type in the diagram), proactively report their status and needs to the coordinating scheduler. These two data streams (objective environment and subjective needs) converge at the central processing unit, providing a comprehensive basis for subsequent strategy generation.

[0025] 3. Core Scheduling and Active Execution After synthesizing the above data, the coordinating scheduler, acting as the decision-making brain, sends specific scheduling instructions (time slot / power / channel) to the underlying WiFi and Bluetooth modules. Upon receiving the instructions, these two communication modules transmit signals outward in a strictly controlled manner, thereby actively avoiding possible spatial or temporal collisions at the physical transmission end.

[0026] 4. Filtering, purification, and closed-loop feedback When the system receives a noisy signal over the air interface, the noisy signal directly enters the adaptive filter in the lower left corner. Simultaneously, the coordinating scheduler sends configuration parameters to this filter. After hardware-level purification, the filter outputs a purified signal and transmits it to the main control unit / application layer for final service use. After evaluating the final signal quality, the main control unit / application layer sends the generated communication quality feedback (bit error rate, throughput, etc.) back to the coordinating scheduler.

[0027] Based on the overall overview of the system described above, the following describes various embodiments provided in the embodiments of this application based on the system.

[0028] Example 1 In one embodiment, such as Figure 2 As shown, a method for processing wireless communication signals is provided, which is applied to a wireless communication signal processing system. The method includes the following steps: S110: Obtain channel status data of the wireless environment, as well as service data on the corresponding communication link.

[0029] At the beginning of system operation, the system uses the underlying spectrum sensing unit to scan the wireless frequency band (e.g., 2.4GHz band) of the wireless environment (for example, an in-vehicle wireless environment) in real time, monitoring the signal strength, noise floor, and occupancy of each channel, thereby obtaining channel status data reflecting the current electromagnetic environment. At the same time, the system synchronously acquires the specific service data information carried on the WiFi and Bluetooth links that are currently communicating.

[0030] S120. Determine the level of interference based on channel state data, and parse the service data to determine the dynamic transmission priority of the corresponding communication link.

[0031] After acquiring the data, the system can accurately assess channel quality and determine the level of interference in the current environment based on the obtained channel state data. Furthermore, the system identifies service types by parsing running service data. For example, it can dynamically prioritize services on different communication links according to their real-time and security requirements. For instance, it can set high priority for Bluetooth voice calls and low priority for latency-insensitive services such as file downloads.

[0032] S130. Based on the level of interference and dynamic transmission priority, a collaborative scheduling strategy is generated to perform anti-interference transmission control on the transmitter corresponding to the communication link.

[0033] Based on the aforementioned interference assessment and priority determination results, the collaborative scheduler dynamically selects and generates specific collaborative scheduling strategies. Subsequently, the collaborative scheduler translates these strategies into instructions and issues them to directly control the transmission behavior of transmitters such as WiFi modules or Bluetooth modules, thereby proactively implementing anti-interference transmission control at the source and rationally allocating transmission resources.

[0034] S140. Receive a mixed signal through the signal receiving link. The mixed signal contains corresponding interference noise and the target communication signal carrying service data.

[0035] At the signal receiving end, due to the complexity and multipath effect within the terminal space (in-vehicle environment), the receiving antenna often does not capture a perfect single signal. What is usually received through the signal receiving link is a mixed signal, which contains both residual interference noise that was not completely eliminated in the previous steps and the useful target communication signal that actually carries service data.

[0036] S150. The mixed signal is filtered using an adaptive filter located at the front end of the baseband demodulator in the signal receiving link in order to extract the target communication signal from the mixed signal.

[0037] To further purify the signal, an adaptive filter is deployed before the demodulation module in the receiving link (i.e., the baseband demodulator front end). This filter takes the aforementioned mixed signal as input and performs filtering by constructing a response with characteristics opposite to the interference signal in real time, thereby suppressing residual interference to the maximum extent and extracting the clean target communication signal from the mixed signal.

[0038] S160. Monitor the real-time communication performance indicators of the target communication signal, adjust the transmission parameters of the transmitter based on the real-time communication performance indicators, and feed the real-time communication performance indicators back to the adaptive filter to dynamically adjust the digital filter operator parameters of the adaptive filter.

[0039] The system continuously monitors real-time communication performance metrics. Performance data is fed back to the cooperative scheduler and adaptive filter in real time: the cooperative scheduler can fine-tune the transmission parameter strategies such as time or power at the transmitter, while the adaptive filter can also dynamically adjust the operator parameters (such as step size factor) of its internal algorithm based on these feedback metrics to maintain optimal convergence and filtering in complex environments.

[0040] As can be seen, in this embodiment, a complete perception-decision-execution-feedback closed-loop control mechanism is constructed through the above steps. Intervention is made at the baseband demodulator front end of the signal receiving link, utilizing an adaptive filter for underlying physical layer signal processing. In the pre-processing stage of baseband demodulation, the system uses a mixed signal containing the target communication signal and interference noise as input for filtering. Furthermore, by continuously monitoring the real-time communication performance indicators after demodulation and feeding these indicators back to the adaptive filter, the system can dynamically adjust the digital filtering operator parameters of the adaptive filter, forming a dynamic digital inverse filter that evolves in real time according to the actual degree of clutter degradation. This mechanism, which combines front-end scheduling and filtering based on interference levels and dynamic transmission priorities with dynamic back-end parameter updates, enables the system to exhibit anti-interference capabilities exceeding existing fixed hardware or pure protocol-level scheduling schemes when dealing with complex multipath echoes in vehicles, fundamentally ensuring the purity of the target signal and the robustness of the system.

[0041] In one embodiment, the process is further refined to include the following steps before determining the level of interference: S210. Analyze channel state data to extract customized signal features.

[0042] After acquiring basic channel state data through the spectrum sensing unit, the system does not merely stop at monitoring surface energy indicators such as the signal strength (RSSI) of each channel center. Instead, the system goes deeper, performing in-depth analysis of complex data such as the noise floor of the entire channel. Its key objective is to extract underlying signal characteristics that can characterize a specific communication protocol.

[0043] S220 performs feature pattern matching on specially customized signal characteristics to identify wireless LAN beacon frame characteristics or Bluetooth frequency hopping sequence characteristics.

[0044] After successfully extracting the underlying features, the system invokes its internal feature pattern matching algorithm to perform detailed comparison and analysis of the captured signal features. Through this standard-level matching, the system can accurately identify signal types that conform to specific protocol specifications from mixed radio waves, such as directly identifying the beacon frame features of wireless local area networks (WiFi) or the frequency hopping sequence features unique to Bluetooth devices.

[0045] S230. Determine the target interference source based on the identified wireless LAN beacon frame characteristics or Bluetooth frequency hopping sequence characteristics, and update the channel state data based on the target interference source.

[0046] Based on the fingerprint information such as beacon frames or frequency hopping sequences successfully identified in the previous step, the system can accurately determine whether there is co-channel or adjacent-channel interference causing collisions in the current channel, and further pinpoint whether this interference originates from other electronic devices inside the vehicle or from the external environment (such as roadside access points encountered during vehicle movement). After clarifying the attributes of these specific target interference sources, the system supplements the identity and type information of the interference source, thereby updating and enriching the original channel state data, upgrading it from simple physical measurements to comprehensive data containing semantic information.

[0047] As can be seen, this embodiment further defines the deep feature extraction and identification mechanism for interference sources. In traditional channel sensing technology, the system can usually only roughly determine whether the channel is busy based on energy level (such as RSSI), which means the system only knows that there is interference, but does not know what the interference is. This embodiment, by forcibly introducing feature pattern matching to identify WiFi beacon frames or Bluetooth frequency hopping sequences before determining the interference level, realizes the system's processing from blindly sensing energy to accurately identifying the communication mode. This enables the system to effectively distinguish whether the interference source is an internally controllable device or an external uncontrollable base station, thus providing indispensable high-dimensional data support for subsequently formulating more refined and targeted collaborative scheduling strategies (such as choosing time avoidance or physical channel switching).

[0048] In one embodiment, the specific computational dimensions and evaluation mechanism of the multi-dimensional evaluation level model in quantifying channel quality are further refined. Specifically, in step S120, determining the interference level based on the channel state data includes the following steps: S310. Input the signal-to-noise ratio, bit error rate, and channel occupancy rate from the channel state data into the pre-constructed multi-dimensional evaluation level model.

[0049] In complex communication environments such as vehicular networks, a dynamic channel quality assessment model is established to achieve accurate evaluation. The system inputs three core parameters sensed by the front end into this model for analysis: the first dimension is the real-time signal-to-noise ratio (SNR), the most direct indicator of channel quality, reflecting the clarity of the useful signal relative to background noise; the second dimension is the channel busy ratio, the proportion of time the channel is covered by signal energy, directly reflecting the congestion level of the physical channel; and the third dimension is historical data on the bit error rate (BER) or packet error rate (PER), objectively reflecting the channel's true reliability when actually carrying data streams at the data link layer.

[0050] S320. Quantify and score the channel quality corresponding to the communication link using a multi-dimensional evaluation level model, and determine the interference level based on the quantitative scoring results. Use the interference level as the interference severity.

[0051] After receiving the parameters from the aforementioned cross-layer (physical layer and link layer), the multi-dimensional evaluation level model performs a fusion analysis, combining multiple parameters to quantify and score the signal. This scoring mechanism comprehensively considers signal clarity, channel congestion, and the accuracy of historical transmissions to arrive at an interference level. For example, through the fusion analysis of these parameters, the system can accurately label each channel as "excellent," "mildly congested," "severely interfered," or "unavailable." This determined classification will directly serve as the core interference level input for the system's subsequent collaborative scheduler.

[0052] As can be seen, this embodiment further defines a multi-dimensional channel quality quantification assessment and grading mechanism. In particular, the combination of features—"inputting signal-to-noise ratio, bit error rate, and channel occupancy rate into a multi-dimensional assessment grading model for quantification and scoring to determine the level of interference"—greatly improves the accuracy of the assessment.

[0053] In existing technologies, judgments are typically made simply based on signal strength. However, in a train carriage with strong metallic reflections, a channel may measure a very high signal strength, but this energy could very well originate from undemodulated co-channel noise or external interference sources. This embodiment, by fusing real-time signal-to-noise ratio, the proportion of time the channel is covered by energy (channel occupancy), and historical bit error rate performance, can directly penetrate the surface and reveal the channel's true data carrying capacity. This multi-dimensional fusion analysis completely avoids the fatal flaw of misjudging high-intensity interference as a high-quality idle channel, rather than simply judging based on signal strength, thus greatly improving the accuracy and practicality of interference assessment. By outputting highly granular status markers such as "excellent," "mild congestion," and "severe interference," the subsequent scheduling module not only knows that the environment is chaotic but also accurately grasps how chaotic it is, thereby enabling it to calmly and accurately match corresponding time-division multiplexing or channel switching tiered anti-interference strategies.

[0054] In one embodiment, the execution steps of how the collaborative scheduler dynamically generates specific anti-interference strategies through logical judgment when facing different interference environments and service requirements are further refined. In step S130, that is, generating a collaborative scheduling strategy based on the interference level and the dynamic transmission priority, the following steps are included: S410. Using the level of interference as the criterion for triggering decision branches, traverse the preset decision tree to determine the strategy branch corresponding to the level of interference.

[0055] After outputting accurate quantification results, the collaborative scheduler incorporates them into the system's internal decision tree structure. The system uses the interference severity level (e.g., light, medium, heavy interference) obtained from previous steps as the core criterion for triggering specific decision branches. Through top-down traversal and logical judgment within the pre-defined decision tree, the system can accurately identify the conflict scenario in the current network environment and directly guide the system to the policy branch corresponding to that specific interference severity level.

[0056] S420. Under the determined strategy branch, the target strategy is called from the preset hybrid strategy according to the dynamic transmission priority, and the target strategy is used as the cooperative scheduling strategy; wherein, the hybrid strategy includes parallel transmission strategy, time division multiplexing scheduling strategy, and power or channel adaptive adjustment strategy.

[0057] like Figure 3 As shown, unlike traditional systems that passively execute single actions, the collaborative scheduler in this embodiment employs a hybrid scheduling strategy. After entering a specific strategy branch, the system further combines the dynamic transmission priority of the service and dynamically selects and invokes the most suitable target strategy from multiple strategies (A / B / C): Strategy A (Parallel Transmission): If the branch is determined to be "light interference, no business conflict", the system directly calls the parallel transmission strategy (default state). In this state, the system considers it an ideal state, with both sides sending and receiving normally, and no additional scheduling overhead.

[0058] Strategy B (Time Division Multiplexing Scheduling): If the decision branch is "interference, with time sequence conflicts", the system invokes the time division multiplexing scheduling strategy, that is, allocating time slots according to priority. This strategy aims to resolve moderate conflicts, and its core is time arbitration.

[0059] Strategy C (Power / Channel Adaptation): If the branch is determined to be "heavy interference, or critical services need to be guaranteed" (i.e., core services involving extremely high dynamic transmission priority), the system invokes the power / channel adaptation strategy to perform operations such as reducing the power of one party or switching channels. This strategy aims to resolve severe conflicts or guarantee high-priority services, and its core is spatial (channel) or power adjustment.

[0060] As can be seen, this embodiment further defines the specific logic of the cooperative scheduler's generation strategy. In the complex and ever-changing in-vehicle environment, a single, fixed anti-interference strategy often faces a dilemma: either over-scheduling and wasting channel resources during minor interference, or being powerless during severe congestion. This embodiment, by constructing a decision tree structure encompassing strategies A, B, and C, achieves scenario-based and tiered configuration of scheduling strategies. It can dynamically and intelligently select the lowest-cost and most effective anti-interference means based on the actual severity of interference and the urgency of the service. This not only avoids unnecessary scheduling overhead but also ensures that the system can decisively employ powerful measures to guarantee the smooth operation of critical services even in extremely harsh electromagnetic environments, endowing the entire system with extremely high intelligence and scenario adaptability.

[0061] In one embodiment, for cases where the target policy being invoked includes a Time Division Multiplexing (TDR) scheduling policy, the specific steps for the system to perform time resource allocation and signaling control are further refined. Specifically, if the target policy being invoked includes the TDR scheduling policy, the process of generating the cooperative scheduling policy further includes the following steps: S510. If the first dynamic transmission priority corresponding to the first communication link is higher than the second dynamic transmission priority corresponding to the second communication link, then control signaling is generated in a specific timing sequence.

[0062] In real-world communication scenarios, service requirements often exhibit different priorities. For example, the first communication link might handle services involving active calls, such as Bluetooth voice calls, while the second communication link might handle background tasks like downloading large files via WiFi. The system, through an initial priority determination mechanism, assigns Bluetooth voice the highest priority (i.e., the first dynamic transmission priority) and file downloads a lower priority (i.e., the second dynamic transmission priority). When a high-priority service (such as Bluetooth audio) truly needs to send data, the scheduler will proactively generate a brief hold signal as control signaling at the specific timing of this impending transmission.

[0063] S520: Send control signaling to the transmitter corresponding to the second communication link to control the transmitter corresponding to the second communication link to enter a predefined sleep state.

[0064] After generating control signaling, the coordinating scheduler immediately sends a brief hold signal to the communication modules (such as WiFi modules) where low-priority services reside. WiFi modules receiving this signal are instructed to temporarily reduce their power or enter a brief sleep state. This mandatory control mechanism causes low-priority transmitters to proactively postpone sending their own data packets, thereby achieving service avoidance on the timeline.

[0065] S530. During the sleep state, a transmission window is allocated to the transmitter corresponding to the first communication link so that the transmitter corresponding to the first communication link can transmit data.

[0066] During the period when the transmitter of the second communication link is suppressed and enters a brief dormant state, the system effectively creates a conflict-free transmission window for high-priority services. Essentially, the coordinator utilizes this extremely valuable time slice to clear a green channel for the transmitter of the first communication link (i.e., the Bluetooth module). This allows voice data packets from devices such as Bluetooth headsets to be prioritized and transmitted without interruption at millisecond speeds. Simultaneously, during the transmission gaps of this high-priority service or when low-priority services can be transmitted, the dormant WiFi module resumes operation and transmits data at full power.

[0067] It is evident that in the complex environment of a train carriage, simple packet transmission arbitration mechanisms often fail to effectively distinguish the priorities of data packets, easily leading to unnecessary delays for high-priority data. This embodiment, through the aforementioned signaling generation-forced sleep-window allocation process, enables system resources to intelligently prioritize services with higher real-time requirements. It not only temporarily suspends the preemption of low-priority services, creating an absolutely clean green channel for critical voice services and eliminating the pain point of audio interruptions, but also ensures that low-priority services can quickly recover during transmission gaps, maximizing the utilization of channel resources in the time dimension.

[0068] In one embodiment, the underlying signal processing mechanism and core algorithm steps of the adaptive filter are further refined to address how the system extracts a clean and useful signal from a complex mixed signal. Specifically, step S150 includes the following steps: S610. The filter weight coefficients of the adaptive filter are calculated iteratively using the least mean square algorithm or the recursive least squares algorithm.

[0069] In the back-end processing stage of the signal receiving link, when the system receives a mixed signal containing both useful signal and interference noise as input, the filter's operation is actually a continuous iterative optimization process. Adaptive filters deployed before the demodulation module in the receiving link typically employ digital filtering algorithms based on Least Mean Square (LMS) or Recursive Least Squares (RLS). The system continuously performs iterative calculations using these algorithms, adjusting the core parameters within the filter—the tap weight coefficients (filter weight coefficients)—in real time. The goal is to minimize the mean square error between the filter's output signal and the desired clean signal.

[0070] S620: Based on the iteratively updated filter weight coefficients, generate the corresponding inverse response signal for the interference noise in the mixed signal.

[0071] As the internal parameters of the filter (tap weight coefficients) iterate and converge, the system uses these adjustable filter coefficients to construct an inverse filter in real time that is completely opposite to the characteristics of the interference signal. This means that, for example, when the received Bluetooth signal is mixed with residual WiFi interference, the filter can quickly learn this mixed state and generate an inverse response signal (i.e., an inverse response signal) that can effectively cancel out the characteristics of that specific WiFi signal.

[0072] S630. The inverse response signal and the mixed signal are used to perform signal reconstruction and cancellation processing to extract the target communication signal.

[0073] After generating a targeted inverse response signal, the system applies it directly to the original mixed input signal. Through this physical or digital signal reconstruction and cancellation process, specific interference characteristics in the mixed signal are canceled out by the inverse response signal. Thus, the system can extract the clean, pure Bluetooth target communication signal from the noisy mixed signal to the maximum extent possible, providing a high-quality input source for subsequent baseband demodulation.

[0074] It is evident that in the complex environment of a train carriage, relying solely on coordinated scheduling at the communication protocol level (preliminary steps) at the transmitting end is often insufficient to completely eliminate all signal collisions. Furthermore, it cannot overcome inherent channel defects caused by multi-metal reflectors within the carriage (such as self-echo interference due to multipath effects). This embodiment, by introducing adaptive filtering based on the LMS / RLS algorithm at the receiving end, effectively suppresses residual interference that the preliminary scheduling strategy failed to address through real-time iterative updates of the filter weight coefficients and signal cancellation. This passive anti-interference mechanism, serving as a post-processing measure, further purifies the communication signal, significantly reduces the system's bit error rate, and thus substantially improves the overall robustness of the communication system and the accuracy of data transmission.

[0075] In one embodiment, the specific quantitative calculation and parameter fine-tuning steps of the feedback adjustment mechanism are further refined to address how the system constructs a closed loop to continuously optimize the scheduling strategy and filtering effect. Specifically, step S160 includes the following steps: S710. The adjustment compensation factor is calculated using real-time communication performance indicators, wherein the real-time communication performance indicators include at least one of real-time throughput, bit error rate, packet error rate, communication link delay, and signal-to-noise ratio improvement.

[0076] After performing scheduling and filtering, the system continuously monitors key performance indicators to evaluate the effectiveness of these steps. The system acquires real-time communication performance indicators of the target communication signal, including but not limited to: real-time throughput, bit error rate / packet error rate, communication link latency, and the improvement in signal-to-noise ratio. By mathematically evaluating the degree to which these indicators deviate from the expected target, the system can calculate an adjustment compensation factor to characterize the current state deviation.

[0077] S720: Adjust the transmission parameters of the transmitter by adjusting the compensation factor.

[0078] The calculated performance data (and adjustment compensation factors) are fed back to the forward coordinating scheduler in real time. The coordinating scheduler uses these factors to automatically fine-tune the macroscopic transmission parameters at the transmitting end. For example, if feedback data shows that after enabling a certain time-division multiplexing strategy, the Bluetooth audio latency meets the standard but the WiFi throughput drops too drastically, the scheduler will automatically fine-tune the time slot allocation ratio (i.e., transmission parameters) to ensure audio quality while maximizing download efficiency.

[0079] S730: The adjustment compensation factor is input to the adaptive filter at the front end of the baseband demodulator. The convergence step size of the adaptive filter is dynamically adjusted by adjusting the adjustment compensation factor in order to update the digital filter operator parameters.

[0080] Simultaneously, this adjustment compensation factor is also synchronously input into the adaptive filter located at the back end of the receiving link. The adaptive filter adjusts the step size factor (i.e., convergence step size) of its underlying algorithm based on the latest performance feedback (such as bit error rate), thereby updating the digital filter operator parameters. By dynamically changing this step size parameter, the filter can achieve the optimal balance between convergence speed in the face of sudden interference and steady-state error in a steady-state environment.

[0081] In traditional static anti-interference mechanisms, the set thresholds or parameters are often unchanging, failing to cope with dynamic transient changes in the in-vehicle environment (such as passenger boarding and alighting, equipment additions and removals). This embodiment maximizes the throughput of low-priority services (downloads) without sacrificing the quality of high-priority services (audio), achieving a dynamic optimal balance. The adaptive filter departs from a fixed convergence curve, adjusting its step size factor in real time according to the degree of environmental degradation, enabling it to respond quickly to sudden interference changes and provide fine filtering during stable conditions. Through this continuous monitoring-evaluation-adjustment closed loop, the entire system transforms from a static rule enforcer into a dynamically optimized system capable of adapting to different vehicle environments and user habits, endowing the system with strong intelligence and robustness.

[0082] by Figure 4 For example, Figure 4Through a typical in-vehicle business scenario, the implementation effect of dynamic priority management and time-division multiplexing in the embodiment is vividly illustrated. Figure 4 The video, arranged chronologically, shows the actions and interactions of four entities—a mobile phone, a coordinator, a WiFi module, and a speaker—under a specific interference event.

[0083] When a mobile phone, acting as the source, initiates a high-priority Bluetooth voice request, the system (coordinating scheduler) instantly intervenes, proactively sending suppression instructions (i.e., delaying transmission or suspending activity as described in the embodiment) to the WiFi module that might cause co-channel interference. Even more intelligently, such as... Figure 4 As shown, once the call ends, the system releases the restrictions, and the Wi-Fi data download task resumes seamlessly, thereby maximizing the use of channel resources.

[0084] Figure 4 Using a sequence diagram, the actual interception and release process of dynamic priority is vividly illustrated through four main entities: the mobile phone (Bluetooth), the coordinating scheduler, the in-vehicle WiFi module, and the in-vehicle audio system. The process begins with the mobile phone sending an "Event: Initiate high-priority Bluetooth voice call request" to the system. Upon receiving this, the coordinating scheduler quickly executes two actions: "1. Sensing the current channel status" and "2. Determining the service type as 'Bluetooth voice' (highest priority)." Subsequently, the scheduler enters the yellow-highlighted decision box: "Decision: Execute the 'priority protection' strategy." The coordinating scheduler decisively issues an "Instruction: Temporarily reduce transmission power (or suspend high-bandwidth transmission)" to the in-vehicle WiFi module. Only after receiving confirmation from the WiFi module that "Power has been adjusted / Transmission has been paused" does the coordinating scheduler issue a "Permission: Bluetooth can transmit at full speed / stably" to the mobile phone. This scheduling allows the mobile phone to output a stable, low-latency audio stream to the in-vehicle audio system, and the audio system successfully plays clear, uninterrupted call audio. After the entire yellow "Call Period..." ends, the mobile phone sends an "Event: Bluetooth voice call ended." At this point, the coordinating scheduler immediately sends a "command: restore normal power / transmission" to the WiFi module, and after receiving confirmation that it has been restored, it releases the channel resources, forming a perfect service loop.

[0085] Example 2 In one embodiment, see Figure 3 As shown, in order to provide a complete and systematic explanation of the entire technical solution of the present invention, a comprehensive embodiment of a wireless communication signal processing method is provided. This embodiment integrates the technical features of the aforementioned stages and demonstrates in detail the complete closed-loop execution process of the system from front-end perception to back-end feedback. The specific execution steps of this method are as follows: S1, Spectrum Sensing and Interference Assessment.

[0086] The spectrum sensing unit acquires channel sensing data. It scans wireless frequency bands (such as channels 1-13 of the IEEE 802.11 standard and the 79 frequency hopping points of Bluetooth in the 2.4 GHz band) at a higher scanning frequency than conventional communication data rates (e.g., 10-100 times per second) to obtain the wireless environment characteristics of each communication channel (such as signal strength, noise floor, and channel occupancy), thus acquiring channel sensing data. Furthermore, it not only monitors the signal strength (RSSI) at the center of each channel, but more importantly, analyzes the noise floor of the entire channel, extracts customized signal features (such as identifying WLAN beacon frame features or Bluetooth frequency hopping sequence features), and determines whether there is co-channel or adjacent-channel interference from the in-vehicle or external environment. To achieve accurate evaluation, the system utilizes a dynamic multi-dimensional evaluation level model to perform fusion analysis and quantitative scoring of real-time signal-to-noise ratio (SNR), noise floor, channel occupancy, and historical packet error rate / bit error rate from the channel sensing data. The quality level of each communication channel is determined based on the quantitative scoring results (such as marking the channel as "excellent", "mild congestion", "severe interference" or other interference levels), thereby obtaining accurate spectrum sensing results, replacing the blind judgment based on simple signal strength.

[0087] S2. Communication requirements analysis and priority determination.

[0088] The system parses service characteristic information to determine the service type and its dynamic transmission priority. By parsing protocol fields, port numbers, or priority markers in the packet header, or by interacting with the application processor to obtain service flow identifiers, it acquires real-time service characteristic information on the communication links between the WiFi and Bluetooth modules. For example, it identifies that the Bluetooth link is conducting a synchronous connection-oriented (SCO) audio call, or that the WiFi link is conducting a TCP large file download. Based on this identification, as an example, a preset priority policy library that supports dynamic adjustment can be queried to determine the first and second service types. This policy library, with the core principles of ensuring real-time performance, ensuring security, and optimizing user experience, assigns a dynamic transmission priority to the first service type, such as Bluetooth voice calls involving active communication, higher than the second service type, which is less sensitive to latency, such as background file synchronization and large file downloads. This dynamic determination allows the system to make informed choices when resource conflicts occur.

[0089] S30, Cooperative scheduling strategy generation and execution.

[0090] The collaborative scheduler generates collaborative scheduling instructions based on spectrum sensing results and transmission priorities, and executes anti-interference scheduling strategies. Based on spectrum sensing results (i.e., interference levels) and the dynamic transmission priorities of each service type, the collaborative scheduler dynamically selects and generates specific collaborative scheduling instructions from a preset hybrid strategy. The hybrid strategy employed by the scheduler specifically includes: time-division multiplexing scheduling, power adaptive adjustment, and dynamic channel switching. Specifically, the collaborative scheduler prioritizes time-division multiplexing scheduling; when the high-priority first service type needs to transmit data, the scheduler generates control signaling (a brief hold signal) at a specific timing and sends it to the transmitter corresponding to the second service type, controlling it to enter a predefined sleep state and postpone transmission, thereby allocating a conflict-free transmission window for the first service type. When dealing with adjacent channel interference or scenarios requiring coexistence, power adaptive adjustment is enabled, dynamically fine-tuning the transmission parameters (transmit power) of the WiFi or Bluetooth module transmitter to just meet communication quality requirements, thereby reducing the interference range to the other party. When the spectrum sensing results show that the current channel quality is consistently poor and the aforementioned strategies are ineffective, dynamic channel switching is triggered as a last resort. This involves controlling the WiFi module to switch to the optimal idle channel or guiding the Bluetooth module to adjust the adaptive frequency hopping sequence to completely avoid interference sources.

[0091] As an example, such as Figure 3 As shown, the scheduler acts as a distribution diamond node here, dividing the path into three distinct policy paths based on the level of interference: Scenario 1: When "interference is minor and there is no business conflict", the system takes the left branch and executes "Policy A: Normal transmission (no scheduling overhead)".

[0092] Scenario 2: When there is "interference and time domain conflict", the system takes the intermediate branch and executes "Strategy B: Time Division Multiplexing Scheduling (send hold signal to low priority service)".

[0093] Scenario 3: When "interference is severe or critical services need to be guaranteed", the system will take the right branch and execute the most powerful "strategy C: power adaptive adjustment or channel dynamic switching".

[0094] S40, adaptive filtering at the signal receiver.

[0095] An adaptive filter filters the received mixed signal to extract the target communication signal. At the signal receiving end, a mixed signal containing the target communication signal (useful signal) and interference noise (interference signal) is received through the signal receiving link. To suppress residual interference and inherent channel defects (such as multipath effects) that were not completely eliminated by the preceding scheduling steps, the system uses an adaptive filter (such as a digital filter based on the least mean square (LMS) or recursive least squares (RLS) algorithm located at the front end of the baseband demodulator in the signal receiving link for back-end filtering. The filter operates as a continuous iterative optimization process: it takes the received mixed signal as input, iteratively calculates and adjusts its internal filter weight coefficients (tap weight coefficients) through the algorithm, and constructs an inverse response signal ("=inverse filter") with characteristics opposite to the interference signal in real time. Through reconstruction and cancellation processing, the mean square error between the filter's output signal and the desired clean signal is minimized, thereby maximizing the extraction of clean Bluetooth or WiFi signals and significantly reducing the bit error rate.

[0096] S50, Strategy Effect Feedback and Optimization.

[0097] The system monitors real-time communication performance metrics and dynamically adjusts scheduling parameters and digital filtering operator parameters. It continuously monitors key real-time communication performance metrics of the target communication signal (including but not limited to real-time throughput, bit error rate / packet error rate, communication link latency, and signal-to-noise ratio improvement) to evaluate the effectiveness of steps S3 and S4. These real-time communication performance metrics (or the adjustment compensation factors calculated from them) are fed back in a closed-loop manner in real time: on the one hand, they are fed back to the cooperative scheduler to fine-tune the transmission parameters at the transmitter (e.g., automatically fine-tuning the time slot allocation ratio when latency meets the target but throughput drops drastically, balancing efficiency and quality); on the other hand, they are fed back to the adaptive filter to dynamically adjust its algorithm's convergence step size and other digital filtering operator parameters to achieve the best balance between convergence speed and steady-state error. Through this continuous monitoring-evaluation-adjustment closed loop, the entire system can transform from a static rule enforcer into a dynamically optimized system that adapts to the complex environments and usage habits of different vehicles.

[0098] In one embodiment, see further. Figure 1 As shown, this application provides a wireless communication signal processing system. This system architecture tightly integrates low-level hardware processing with high-level protocol scheduling, and is primarily applicable to the complex electromagnetic environment inside automobiles, fundamentally solving the mutual interference problem caused by the coexistence of WiFi and Bluetooth. The specific component composition and working principle of this system are explained below.

[0099] The spectrum sensing unit, acting as a pioneer probe for the system to perceive the external electromagnetic environment, is specifically designed to acquire channel state data of the wireless environment (e.g., scanning the 2.4 GHz band to obtain signal strength, noise floor, etc.). It provides the entire system with objective, low-level electromagnetic environment data input.

[0100] The cooperative scheduler (state assessment and active control) is the core decision-making brain of this processing system. It acquires service data on the corresponding communication link, determines the interference level based on channel state data, and parses the service data to determine the dynamic transmission priority of the corresponding communication link. After completing the two-way assessment of the environment and services, the cooperative scheduler generates a cooperative scheduling strategy based on the interference level and dynamic transmission priority to perform anti-interference transmission control on the corresponding transmitter of the communication link (such as performing time-division multiplexing or power adjustment to avoid signal collisions at the source).

[0101] The adaptive filter, located at the front end of the baseband demodulator in the signal receiving link, forms the system's underlying physical defense at the receiving end. When the air interface signal reaches the receiving end after complex reflections inside the vehicle, this adaptive filter receives the mixed signal through the signal receiving link. This mixed signal inevitably contains corresponding interference noise and the target communication signal carrying service data. This filter intervenes directly at the front end of the baseband demodulator, performing low-level physical layer filtering on the mixed signal to powerfully clean and extract the pure target communication signal.

[0102] The collaborative scheduler (with closed-loop monitoring and bidirectional feedback adjustment) plays a crucial role in enabling the system to adapt and evolve. It also serves as a central quality control and joint debugging mechanism. Furthermore, it monitors real-time communication performance metrics of the target communication signal (such as bit error rate and throughput). Based on these real-time performance metrics, the scheduler performs cross-layer bidirectional adjustment: on one hand, it adjusts the transmission parameters at the transmitter (protocol layer / MAC layer fine-tuning); on the other hand, it accurately feeds back the real-time communication performance metrics to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter (physical layer underlying algorithm fine-tuning).

[0103] Existing communication system hardware architectures often completely separate MAC layer protocol scheduling from underlying baseband signal processing, resulting in pure MAC layer scheduling offering only temporary solutions when facing complex multipath environments. This system architecture creatively extends the control authority of the cooperative scheduler, enabling it to form a direct architecture-level linkage with the adaptive filter located in front of the baseband demodulator.

[0104] Furthermore, the system not only performs passive filtering at the front end but also establishes a hard-wired feedback channel. The coordinating scheduler directly feeds back communication performance indicators to the front-end filter to adjust its operator parameters, making the receiver hardware essentially a self-evolving dynamic digital inverse filter. This closed-loop hardware architecture, combining front-end physical layer filtering with dynamic parameter updates from the central scheduler, endows the system with resistance to multipath echoes and co-channel collisions far exceeding that of traditional fixed hardware, fundamentally ensuring the purity of the target signal and the system's extremely high robustness.

[0105] In one embodiment, a vehicle is provided. This embodiment aims to practically implement the aforementioned wireless communication signal processing system within the extremely unique physical space of a vehicle cabin, fundamentally solving the complex electromagnetic interference problem within the vehicle. The internal system integration and specific operating scenarios of this vehicle are explained below: Addressing the Complex Wireless Environment in the Automotive Cockpit and its System Integration: Modern automobiles have become highly integrated intelligent mobile spaces, typically containing multiple WiFi hotspots (e.g., in-vehicle entertainment systems, passenger device sharing hotspots) and numerous Bluetooth devices (e.g., mobile phone connectivity, car audio systems, tire pressure monitoring systems). The vast majority of these devices operate primarily in the congested 2.4GHz frequency band. Furthermore, the car cabin is a small, enclosed space filled with numerous metallic reflectors, making it highly susceptible to severe co-channel collisions between the fixed channels of WiFi and the frequency-hopping channels of Bluetooth, as well as multipath effects caused by metallic reflections. Therefore, the vehicle incorporates a comprehensive wireless communication signal processing system, serving as the central brain for the vehicle's wireless resources.

[0106] For example, in a real driving scenario, when the system's data acquisition and evaluation module detects the status of the in-vehicle wireless network and identifies a service request, the system will perform an integrated anti-interference operation combining hardware and software within the cabin.

[0107] When the driver initiates a Bluetooth audio call (high priority) while the passenger is downloading a large file using the vehicle's Wi-Fi (low priority), the car's scheduling module proactively intervenes, instructing the Wi-Fi transmitter to briefly go into sleep mode or temporarily reduce its transmission power to make way for the Bluetooth voice call, thereby reducing signal collisions at the source. At the receiving end, to address the unavoidable multipath reflection noise from the vehicle's metal components, the adaptive filter in the car's receiver link continuously purifies the received mixed signal, removing residual interference and extracting the pure target audio or data signal.

[0108] In existing automobiles, interference between devices often manifests as intermittent or noisy Bluetooth audio, reduced WiFi data transmission rates, and video streaming stuttering, severely impacting the driving experience and the reliability of in-vehicle equipment. This embodiment deeply integrates a closed-loop system that combines active avoidance and passive suppression into the vehicle architecture. It not only readily handles dynamic changes in the in-vehicle environment (such as passenger entry and exit, and the addition or removal of devices), but also completely eliminates the complex electromagnetic interference problem within the cabin. Ultimately, this integration provides drivers and passengers with uninterrupted in-vehicle audio, smooth high-definition video streaming, and extremely low-latency vehicle-to-everything (V2X) connectivity, significantly improving the driving comfort, convenience, and intelligence of modern automobiles.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a wireless communication signal processing method described in any embodiment of this application.

[0110] In this application, "multiple" refers to two or more.

[0111] In this application, unless otherwise expressly defined, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0112] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0113] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0114] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0115] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method of processing a wireless communication signal, the method comprising: include: Acquire channel status data of the wireless environment, as well as service data on the corresponding communication link; The interference level is determined based on the channel state data, and the service data is parsed to determine the dynamic transmission priority corresponding to the communication link; Based on the interference level and the dynamic transmission priority, a collaborative scheduling strategy is generated to perform anti-interference transmission control on the transmitter corresponding to the communication link. The mixed signal is received through a signal receiving link. The mixed signal includes corresponding interference noise and the target communication signal carrying the service data. An adaptive filter located at the front end of the baseband demodulator in the signal receiving link is used to filter the mixed signal in order to extract the target communication signal from the mixed signal. The system monitors the real-time communication performance indicators of the target communication signal, adjusts the transmission parameters of the transmitting end based on the real-time communication performance indicators, and feeds the real-time communication performance indicators back to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter.

2. The method of claim 1, wherein Before determining the interference level based on the channel state data, the method further includes: Analyze the channel state data to extract specific signal features; Feature pattern matching is performed on the specially customized signal features to identify wireless LAN beacon frame features or Bluetooth frequency hopping sequence features; The target interference source is determined based on the identified features of the wireless LAN beacon frame or the Bluetooth frequency hopping sequence, and the channel state data is updated based on the target interference source.

3. The method of claim 1, wherein Determining the interference level based on the channel state data includes: The signal-to-noise ratio, bit error rate, and channel occupancy rate from the channel state data are input into a pre-constructed multi-dimensional evaluation level model. The channel quality corresponding to the communication link is quantitatively scored using the multidimensional evaluation level model, and the interference level is determined based on the quantitative scoring results. The interference level is then used as the interference severity.

4. The method of claim 3, wherein The step of generating a cooperative scheduling strategy based on the interference level and the dynamic transmission priority includes: The interference level is used as the criterion for triggering decision branches. The system is traversed in a preset decision tree to determine the strategy branch corresponding to the interference level. Under the determined strategy branch, the target strategy is called from the preset hybrid strategy according to the dynamic transmission priority, and the target strategy is used as the cooperative scheduling strategy. The hybrid strategy includes a parallel transmission strategy, a time-division multiplexing scheduling strategy, and a power or channel adaptive adjustment strategy.

5. The method of claim 4, wherein If the target policy being invoked includes the time-division multiplexing scheduling policy, the generation of the cooperative scheduling policy further includes: If the first dynamic transmission priority corresponding to the first communication link is higher than the second dynamic transmission priority corresponding to the second communication link, then control signaling is generated in a specific timing sequence. The control signaling is sent to the transmitter corresponding to the second communication link to control the transmitter corresponding to the second communication link to enter a predefined sleep state; During the sleep state, a transmission window is allocated to the transmitter corresponding to the first communication link so that the transmitter corresponding to the first communication link can transmit data.

6. The method of claim 1, wherein The filtering process of the mixed signal to extract the target communication signal from the mixed signal includes: The filter weight coefficients of the adaptive filter are iteratively calculated using the least mean square algorithm or the recursive least squares algorithm. Based on the iteratively updated filter weight coefficients, a corresponding inverse response signal is generated for the interference noise in the mixed signal; The target communication signal is extracted by performing signal reconstruction and cancellation processing using the inverse response signal and the mixed signal.

7. The method of claim 1, wherein The step of adjusting the transmission parameters of the transmitting end based on the real-time communication performance indicators and feeding back the real-time communication performance indicators to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter includes: The adjustment compensation factor is calculated using the real-time communication performance indicators, wherein the real-time communication performance indicators include at least one of real-time throughput, bit error rate, packet error rate, communication link delay, and signal-to-noise ratio improvement. The transmission parameters of the transmitting end are adjusted using the aforementioned adjustment compensation factor; The adjustment compensation factor is input to the adaptive filter at the front end of the baseband demodulator, and the convergence step size of the adaptive filter is dynamically adjusted using the adjustment compensation factor to update the parameters of the digital filter operator.

8. A system for processing a wireless communication signal, the system comprising: include: The spectrum sensing unit is used to acquire channel state data of the wireless environment; A collaborative scheduler is used to acquire service data on the corresponding communication link, determine the interference level based on the channel state data, and parse the service data to determine the dynamic transmission priority of the corresponding communication link; based on the interference level and the dynamic transmission priority, a collaborative scheduling strategy is generated to perform anti-interference transmission control on the transmitter corresponding to the communication link. An adaptive filter located at the front end of the baseband demodulator in the signal receiving link is used to receive a mixed signal through the signal receiving link. The mixed signal contains corresponding interference noise and a target communication signal carrying the service data. The filter is used to extract the target communication signal from the mixed signal. The cooperative scheduler is also used to monitor the real-time communication performance indicators of the target communication signal, adjust the transmission parameters of the transmitter based on the real-time communication performance indicators, and feed the real-time communication performance indicators back to the adaptive filter to dynamically adjust the digital filtering operator parameters of the adaptive filter.

9. An automobile characterized by comprising: The wireless communication signal processing system as described in claim 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the wireless communication signal processing method according to any one of claims 1 to 7.

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