Spread spectrum method for duplex intercom

Abstract

The application relates to the technical field of adaptive cross-layer resource scheduling in wireless communication, in particular to a spread spectrum method of a duplex intercom, which comprises the following steps: collecting an instantaneous signal-to-interference-and-noise ratio (SINR) and a time variation rate of the instantaneous SINR in real time; analyzing a result of embedded AI voice keyword recognition or a received physical button signal, quantifying the result into a semantic priority factor, jointly evaluating the instantaneous SINR and the time variation rate, and constructing a composite channel risk factor; coupling the semantic priority factor and the composite channel risk factor based on preset system reference redundancy and priority sensitivity weight, cross-layer calculating a dynamic redundancy configuration index; and presetting a low-latency threshold and a high-robustness threshold, comparing the dynamic redundancy configuration index with the thresholds in real time, adaptively selecting and executing a low-latency mode, a high-robustness mode or a balanced mode, and realizing closed-loop control of physical layer configuration; and the application constructs a complete execution closed loop and realizes optimal configuration of communication resources.

Description

Technical Field

[0001] This invention relates to the field of adaptive cross-layer resource scheduling technology in wireless communication, specifically to a spread spectrum method for a full-duplex walkie-talkie. Background Technology

[0002] In real-time communication systems such as full-duplex walkie-talkies, there is a fundamental contradiction between communication robustness and real-time performance. Robustness requires adding redundancy and complex error correction coding to resist channel interference, but this increases processing and transmission latency. Real-time performance, on the other hand, requires simplifying the processing flow to achieve the lowest latency, but this sacrifices the communication's anti-interference capability.

[0003] Existing communication methods typically adjust transmission strategies based on the physical layer channel state. For example, when a channel quality degradation is detected, the system adaptively increases the strength of error correction coding and data redundancy to ensure data transmission success. This adjustment mechanism relies entirely on physical channel parameters and cannot perceive the actual needs of application layer services. Whether it is an urgent voice command or ordinary background data, the system processes it using the same standard. This means that in poor channel conditions, the increased transmission latency to ensure robustness may delay the delivery of critical commands. Conversely, if redundancy is reduced to ensure low latency, important data packets may be lost when the channel suddenly deteriorates. Therefore, the core problem of existing technologies is the lack of a cross-layer decision-making mechanism that can integrate application layer task priorities and physical layer channel states. The system cannot dynamically and intelligently weigh and decide whether to prioritize real-time performance or robustness based on the semantic importance of the transmitted information. As a result, it cannot ensure the delivery of urgent commands with minimal delay while also ensuring the transmission reliability of ordinary data under poor channel conditions. This constitutes a serious technical bottleneck in mission-critical communication scenarios. Summary of the Invention

[0004] To solve the above-mentioned technical problems, the present invention provides a spread spectrum method for a full-duplex walkie-talkie. Specifically, the technical solution of the present invention includes:

[0005] S1. Real-time acquisition of instantaneous signal-to-interference-plus-noise ratio (SINR) and its rate of change over time; and acquisition of results from embedded AI voice keyword recognition or received physical button signals;

[0006] S2. Analyze the results of embedded AI voice keyword recognition or the received physical button signals, quantify them into semantic priority factors; and combine them with preset signal-to-interference-plus-noise ratio thresholds, preset reference rate of change thresholds, and dynamic risk sensitivity weights to jointly evaluate the instantaneous signal-to-interference-plus-noise ratio and the time rate of change, and construct a composite channel risk factor.

[0007] S3. Based on the preset system baseline redundancy and priority sensitivity weights, the semantic priority factor and composite channel risk factor are coupled to calculate the dynamic redundancy configuration index across layers; and the preset low latency threshold and high robustness threshold are used to compare the dynamic redundancy configuration index with the threshold in real time, so as to adaptively select and execute the low latency mode, high robustness mode, or balanced mode to realize the closed-loop control of physical layer configuration.

[0008] Preferably, the acquisition of the time change rate includes: capturing the instantaneous signal-to-interference-plus-noise ratio (SINR) of continuous time sampling points through the physical layer channel state information feedback mechanism, and performing differential calculation on the continuous sampling values ​​of the instantaneous SINR to obtain the time change rate.

[0009] Preferably, the semantic priority factor quantified in S2 includes: a preset rule, whereby when an emergency instruction is identified, the semantic priority factor is set to a high priority value; and when ordinary data is identified, the semantic priority factor is set to a low priority value.

[0010] Preferably, the construction of the composite channel risk factor includes:

[0011] Calculate the static risk component, which represents how close the instantaneous signal-to-interference-plus-noise ratio (SIR) is to the SIR threshold;

[0012] The dynamic risk component is calculated. The dynamic risk component is used to quantify the rate of channel degradation, and the dynamic risk component takes a positive value only when the rate of change of time is negative.

[0013] The static risk component is added to the dynamic risk component to obtain the composite channel risk factor.

[0014] Preferably, the calculation of dynamic risk components includes:

[0015] Divide the rate of change over time by a preset reference rate of change threshold to obtain the normalized rate of change of signal-to-interference-plus-noise ratio.

[0016] When the normalized signal-to-interference-plus-noise ratio change rate is negative, the result is multiplied by the dynamic risk sensitivity weight and used as the dynamic risk component.

[0017] When the normalized rate of change of signal-to-interference-plus-noise ratio is greater than or equal to zero, the dynamic risk component is zero.

[0018] Preferably, the calculation logic for the dynamic redundancy configuration index includes:

[0019] Multiply the system baseline redundancy by the composite channel risk factor to reflect the redundancy increment necessary for channel degradation;

[0020] Furthermore, by utilizing semantic priority factors and priority sensitivity weights, a timeliness control item is constructed, which is used to apply a veto capability to the redundancy increment.

[0021] Among them, the value of the priority sensitivity weight is constrained to ensure that when a high-priority task is triggered, the timeliness control item is forced to lower the dynamic redundancy configuration index to a level close to zero.

[0022] Preferably, the sources of the low latency threshold and the high robustness threshold are: the actual processing latency and corresponding anti-interference threshold required by obtaining the various discrete physical layer configuration sets supported by the walkie-talkie hardware through offline calibration, and the engineering boundary values ​​set accordingly.

[0023] Preferably, adaptive selection and execution includes:

[0024] When the dynamic redundancy configuration index is less than or equal to the low latency threshold, the low latency mode is triggered.

[0025] The low-latency mode uses the simplest modulation and coding scheme and reduces the redundancy of error correction codes to ensure the lowest possible end-to-end latency of instructions.

[0026] Preferably, adaptive selection and execution also includes:

[0027] When the dynamic redundancy configuration index is greater than or equal to the high robustness threshold, the high robustness mode is triggered.

[0028] The high robustness mode employs a full suite of spread spectrum technologies and highly redundant error correction coding, at the cost of increased signal processing latency, to maximize the robustness of data transmission.

[0029] Preferably, adaptive selection and execution also includes:

[0030] When the dynamic redundancy configuration index is greater than the low latency threshold and less than the high robustness threshold, the balancing mode is triggered.

[0031] The balanced mode uses a baseline configuration and adopts a preset static balance between latency and robustness.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention constructs a joint closed-loop control system that crosses the application layer task and the physical layer channel, which solves the technical contradiction between robustness and real-time performance in emergency communication. It can intelligently switch strategies according to the urgency of the task and the channel state, realizing a leap from passively adapting to the channel to actively ensuring the task.

[0034] 2. This invention introduces the time rate of change of signal-to-interference-plus-noise ratio to construct a composite channel risk factor containing static and dynamic components. When the channel quality is still acceptable but deteriorates rapidly, the system can predict the risk in advance through the rapidly increasing dynamic risk component and switch the configuration in advance, which greatly improves the continuity and reliability of communication.

[0035] 3. The core advantage of this invention lies in its innovative cross-layer veto logic, which quantifies application layer emergency commands into high-priority factors and gives them absolute priority over physical layer configuration. When an emergency task is issued, the system will force a switch to low-latency mode, ensuring the real-time performance of the command even in poor channel conditions.

[0036] 4. This invention constructs a complete execution closed loop. The system adaptively switches between three modes—low latency, high robustness, and balanced—based on dynamic redundancy configuration indicators. In emergencies, it forces low latency; in poor channel conditions, it automatically enhances robustness; and in normal conditions, it maintains balance, thereby achieving optimal configuration of communication resources. Attached Figure Description

[0037] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0038] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0040] Example 1:

[0041] Please see Figure 1 A spread spectrum method for a full-duplex walkie-talkie, comprising:

[0042] S1. Real-time acquisition of instantaneous signal-to-interference-plus-noise ratio (SINR) and its rate of change over time; and acquisition of results from embedded AI voice keyword recognition or received physical button signals;

[0043] S2. Analyze the results of embedded AI voice keyword recognition or the received physical button signals, quantify them into semantic priority factors; and combine them with preset signal-to-interference-plus-noise ratio thresholds, preset reference rate of change thresholds, and dynamic risk sensitivity weights to jointly evaluate the instantaneous signal-to-interference-plus-noise ratio and the time rate of change, and construct a composite channel risk factor.

[0044] S3. Based on the preset system baseline redundancy and priority sensitivity weights, the semantic priority factor and composite channel risk factor are coupled to calculate the dynamic redundancy configuration index across layers; and the preset low latency threshold and high robustness threshold are used to compare the dynamic redundancy configuration index with the threshold in real time, so as to adaptively select and execute the low latency mode, high robustness mode, or balanced mode to realize the closed-loop control of physical layer configuration.

[0045] This embodiment provides a spread spectrum method for a full-duplex walkie-talkie. This method constructs a complete and self-consistent technical closed loop, and its specific steps are as follows:

[0046] In step S1, dual-channel concurrent information acquisition is performed; at the physical layer, the system acquires the instantaneous signal-to-interference-plus-noise ratio in real time. Furthermore, by differentiating the continuously sampled values, the time rate of change of the instantaneous signal-to-interference-plus-noise ratio is obtained. The rate of change is used to characterize the dynamic stability of the channel; at the application layer, the system concurrently monitors the data stream and obtains the context information of the task through the results of embedded AI voice keyword recognition or the received physical key signals.

[0047] In step S2, dual-path parallel state quantization is performed; the system processes two types of inputs in parallel: the system parses the results of embedded AI voice keyword recognition or the received physical button signals, and quantizes them into semantic priority factors. This factor is a numerical representation of the timeliness requirements of application layer tasks; the system performs a joint evaluation of channel quality, which combines a preset signal-to-interference-plus-noise ratio (SINR) threshold. This represents the minimum threshold for understandable communication, a preset reference rate of change threshold. This represents the typical channel collapse rate and the dynamic risk sensitivity weight. The instantaneous signal-to-interference-plus-noise ratio acquired by S1 and the rate of change over time A joint assessment is conducted; the innovation of this assessment lies in that it not only considers the current channel quality, but also predicts the future collapse rate of the channel, constructing a composite channel risk factor. ;

[0048] In step S3, cross-layer solution and closed-loop configuration are performed; based on the system's preset baseline redundancy. This represents the hardware-calibrated balanced configuration point and priority sensitivity weight. The system will use the semantic priority factor obtained from S2. Composite channel risk factor Perform coupled calculations to obtain dynamic redundancy configuration indices across layers. ;this Metrics serve as the sole control hub connecting application layer requirements with physical layer states; in parallel, the system presets low-latency thresholds. and high robustness threshold The system will calculate in real time The metric is compared to these two thresholds in real time to adaptively select and execute one of three preset modes: low latency mode ( Highly robust mode ), or balanced mode ( Through this selection and execution of actions, the present invention achieves closed-loop control of physical layer configuration;

[0049] This invention constructs a system that integrates application-layer semantic priority. Combined risk with predictable physical layer Cross-layer configuration metrics This drives the closed-loop switching of the physical layer mode, solving the binary opposition between real-time performance and robustness in traditional communication. It enables walkie-talkies to automatically enhance the robustness of data transmission to combat channel degradation in non-emergency situations. More importantly, when an emergency command is detected, it can force a switch to low-latency mode regardless of how bad the channel conditions are, ensuring that the highest priority commands are delivered with the lowest delay. This achieves an intelligent leap in communication strategy from adapting to the channel to ensuring mission performance.

[0050] Example 2:

[0051] The acquisition of the time change rate includes: capturing the instantaneous signal-to-interference-plus-noise ratio (SINR) of continuous time sampling points through the physical layer channel state information feedback mechanism, and performing differential calculation on the continuous sampling values ​​of the instantaneous SINR to obtain the time change rate.

[0052] This embodiment specifies the method for obtaining the time change rate; this acquisition process relies on the underlying physical layer channel state information (CSI) feedback mechanism, which allows upper-layer applications to access key indicators of the communication link in real time; during operation, the system uses the aforementioned mechanism to capture the instantaneous signal-to-interference-plus-noise ratio (SINR) of continuous time sampling points. Subsequently, the system performs differential calculations on these continuously sampled instantaneous signal-to-interference-plus-noise ratio (SINR) values ​​to estimate the instantaneous derivative, thereby obtaining the rate of change over time. In one feasible implementation, it can be approximated by a first-order difference: ;

[0053] This specific implementation method ensures that It is not merely an abstract mathematical symbol, but a real-time data stream with a clear physical origin and operability; by utilizing the CSI feedback mechanism and differential computation, this invention obtains more than just a static snapshot of the channel. Rather, it is the dynamic trend of the channel. This enables predictive dynamic risk assessment in composite channel risk factors. This is a key technological prerequisite that greatly enhances the system's ability to predict channel collapse.

[0054] Example 3:

[0055] In S2, the semantic priority factor is quantified as follows: a preset rule is used to set the semantic priority factor to a high priority value when it is identified as an urgent instruction, and to set the semantic priority factor to a low priority value when it is identified as ordinary data.

[0056] This embodiment specifically defines the process of quantizing into semantic priority factors in step S2; this quantization process is performed based on a set of preset rules; these rules define the process from semantic recognition results to numerical factors. The mapping relationship is as follows: when the system recognizes an emergency command via AI voice or physical button, the system sets a semantic priority factor. For high-priority settings, the preferred value is a normalized maximum value. When identified as ordinary data, the system sets a semantic priority factor. For low-priority settings, the preferred value is... ;

[0057] This quantization rule transforms the fuzzy, qualitative task context of the application layer into a standardized, discrete, and normalized scalar. This transformation is a necessary step to achieve cross-layer coupled computation, enabling the semantic requirements of the application layer. It can be used as a specific mathematical variable to participate in the dynamic redundancy configuration index in the subsequent step three. In the formula calculation, the upper-layer business requirements can accurately and quantitatively affect the underlying physical configuration.

[0058] Example 4:

[0059] The construction of the composite channel risk factor includes:

[0060] Calculate the static risk component, which represents how close the instantaneous signal-to-interference-plus-noise ratio (SIR) is to the SIR threshold;

[0061] The dynamic risk component is calculated. The dynamic risk component is used to quantify the rate of channel degradation, and the dynamic risk component takes a positive value only when the rate of change of time is negative.

[0062] The static risk component is added to the dynamic risk component to obtain the composite channel risk factor.

[0063] This embodiment specifies the construction logic of the composite channel risk factor; this factor The aim is to simultaneously quantify the static and dynamic risks of a link; its construction process includes:

[0064] Solving static risk components The physical meaning of this component lies in representing the instantaneous signal-to-interference-plus-noise ratio. Compared to the signal-to-interference-plus-noise ratio threshold The degree of proximity; in this embodiment, the calculation formula is: ; wherein, the The source is the data collected in step S1, the... This is the system default value;

[0065] Solving for dynamic risk components This component is a key predictor used to quantify the rate of channel degradation; it only applies when the time rate of change... When the value is negative, the dynamic risk component Only positive values ​​are taken; if the channel is stable or improved ( ),but It must be 0 to degenerate the model into a purely static risk model;

[0066] The static risk component With the dynamic risk component Summing yields the composite channel risk factor. ;

[0067] This embodiment decouples risk into and Two components enable predictive risk assessment; most existing technologies rely solely on... It can only react when the channel is already below the threshold; this invention introduces This allows the system to operate while the channel is still above the threshold, i.e. However, it is declining rapidly, that is... hour, This could also lead to a surge in risk earlier than expected, resulting in a higher overall risk. The surge in traffic allows the system to switch to a highly robust mode before the channel completely collapses, greatly improving the continuity and reliability of communication.

[0068] Example 5:

[0069] The calculation of dynamic risk components includes:

[0070] Divide the rate of change over time by a preset reference rate of change threshold to obtain the normalized rate of change of signal-to-interference-plus-noise ratio.

[0071] When the normalized signal-to-interference-plus-noise ratio change rate is negative, the result is multiplied by the dynamic risk sensitivity weight and used as the dynamic risk component.

[0072] When the normalized rate of change of signal-to-interference-plus-noise ratio is greater than or equal to zero, the dynamic risk component is zero.

[0073] This embodiment further addresses the dynamic risk component. The calculation process was specified in detail, meaning that it only applies to calculations performed within the specified timeframes. hour The specific solution process is as follows:

[0074] Perform normalization: change the time-varying rate of change of S1. Divide by the preset reference rate of change threshold The normalized rate of change of signal-to-interference-plus-noise ratio is obtained, denoted as . ;in It is an engineering parameter that has been experimentally calibrated and represents the typical channel collapse rate;

[0075] Application of nonlinear functions and weighting: This embodiment uses... The function ensures that only negative changes are activated; when the normalized signal-to-interference-plus-noise ratio changes... When it is a negative value, that is ,lead to The system will return the positive result, i.e. Multiply by dynamic risk sensitivity weight The product result is used as a dynamic risk component. ;

[0076] Execute truncation logic: When the normalized signal-to-interference-plus-noise ratio changes... When greater than or equal to zero, that is , ,at this time The function output is 0, resulting in a dynamic risk component. It is zero;

[0077] The complete calculation model for this dynamic risk component is constructed as follows: In this formula, the input variables are... It originates from step S1, and and All of these are system-preset calibration parameters; among them They are constrained to be constants greater than zero to ensure the robustness of the computation;

[0078] This calculation method is achieved through... functions and Weights, which can not only capture channel degradation, but also... The sensitivity of the weighted adjustment system to the crash rate, and through... The threshold is normalized to eliminate the influence of dimensions, thus making... This serves as a robust and controllable risk prediction indicator, ensuring the success of the composite risk model. Stability and configurability in engineering applications.

[0079] Example 6:

[0080] The calculation logic for the dynamic redundancy configuration metric includes:

[0081] Multiply the system baseline redundancy by the composite channel risk factor to reflect the redundancy increment necessary for channel degradation;

[0082] Furthermore, by utilizing semantic priority factors and priority sensitivity weights, a timeliness control item is constructed, which is used to apply a veto capability to the redundancy increment.

[0083] Among them, the value of the priority sensitivity weight is constrained to ensure that when a high-priority task is triggered, the timeliness control item is forced to lower the dynamic redundancy configuration index to a level close to zero.

[0084] This embodiment addresses dynamic redundancy configuration indicators. The solution logic has been specifically defined; this solution model aims to meet the timeliness requirements of the application layer. Channel risks at the physical layer Nonlinear multiplication coupling is performed; in this embodiment, the solution formula is constructed as follows: In this formula, the input variables are... and It originates from the solution result of step S2, and and These are the system's preset calibration parameters;

[0085] System baseline redundancy Composite channel risk factor Multiplication (i.e.) The physical meaning of this product term lies in its reflection of the redundancy increment necessary for channel degradation; when channel risk... As the value increases, this value also increases, requiring the system to improve its redundancy configuration level.

[0086] Using semantic priority factors and priority sensitivity weight A timeliness control item is constructed; in this embodiment, the control item is specifically manifested as follows: At this time, the timeliness control term is used to adjust the above-mentioned redundancy increment ( ) to exercise veto power;

[0087] Among them, priority sensitivity weight The value is constrained to meet a timeliness veto condition, meaning it must be ensured that the event occurs when a high-priority task is triggered. Timeliness control item Able to forcibly lower dynamic redundancy configuration indicators To reach a level close to zero; to achieve this, The value of must satisfy the constraints. For example, if the specification defines ,but Must be set to ,when The value is precisely set to When the timeliness control item is zero, the dynamic redundancy configuration index can be adjusted. Forced to be set to zero;

[0088] This solution employs an innovative multiplication veto mechanism; when an emergency instruction is issued, that is... At that time, control items Approaching 0, making the overall index Forced to be pulled down to near 0, regardless of channel risk. How high; this ensures the system enforces a low-latency mode; while transmitting ordinary data, that is When the value of this control item is close to 1, then... The value will be mainly determined by The decision enables the system to automatically adapt to channel risks; this logic mathematically grants application layer tasks absolute priority and veto power over physical layer configurations in emergency situations.

[0089] Example 7:

[0090] The low latency threshold and high robustness threshold are derived from the actual processing latency and corresponding anti-interference threshold required by the walkie-talkie hardware to obtain the various discrete physical layer configuration sets supported by the walkie-talkie hardware through offline calibration, and the engineering boundary values ​​set accordingly.

[0091] This embodiment addresses the low latency threshold. and high robustness threshold The source of these two thresholds is explained in detail; these two thresholds satisfy... These are the engineering boundaries that define the three working modes. They are not arbitrary but have a solid physical foundation. They are obtained through offline calibration, which uses a variety of discrete physical layer configurations supported by the walkie-talkie hardware, such as different QPSK / 16QAM combinations, different FEC error correction coding rates, different interleaving depths or spreading factor combinations, and the required actual processing delay and corresponding anti-interference thresholds—that is, the lowest tolerable latency under a specific configuration. Based on this, engineering boundary values ​​are set; for example, all configuration sets with latency below 50ms may be classified as low latency, with their corresponding highest... The calculated value is calibrated as All configuration sets with an anti-interference threshold exceeding 5dB are classified as highly robust, with a corresponding minimum... The calculated value is calibrated as ;

[0092] This method ensures The decision output of the indicators can be precisely mapped to the specific, executable physical layer configuration set actually supported by the walkie-talkie hardware; it transforms abstract, continuous control indicators. Connecting with discrete, real-world hardware capabilities ensures the feasibility and effectiveness of the entire closed-loop control system, avoiding a disconnect between decision-making and execution.

[0093] Example 8:

[0094] Adaptively select and execute including:

[0095] When the dynamic redundancy configuration index is less than or equal to the low latency threshold, the low latency mode is triggered.

[0096] The low-latency mode uses the simplest modulation and coding scheme and reduces the redundancy of error correction codes to ensure the lowest possible end-to-end latency of instructions.

[0097] This embodiment specifically defines the first strategy in the adaptive selection and execution logic:

[0098] When the dynamic redundancy configuration index calculated by the system is less than or equal to the low latency threshold, i.e. The system triggers low-latency mode, a condition typically caused by a high-priority instruction. Forced to be suppressed to Below, this decision is related to channel risk. Size is irrelevant; in this mode, the physical layer is immediately reconstructed, the simplest modulation and coding scheme is adopted and the redundancy of the error correction code is turned off or greatly reduced, while time-consuming algorithms such as set union are disabled.

[0099] By sacrificing all sacrificial channel robustness, it reduces processing latency to the lowest level supported by physical hardware, thereby ensuring the lowest end-to-end latency of instructions; this has absolute business value for instructions such as emergency evacuation, guaranteeing their absolute real-time performance.

[0100] Example 9:

[0101] Adaptive selection and execution also include:

[0102] When the dynamic redundancy configuration index is greater than or equal to the high robustness threshold, the high robustness mode is triggered.

[0103] The high robustness mode employs a full suite of spread spectrum technologies and highly redundant error correction coding, at the cost of increased signal processing latency, to maximize the robustness of data transmission.

[0104] This embodiment further defines a second strategy in the adaptive selection and execution logic:

[0105] When the dynamic redundancy configuration index calculated by the system is greater than or equal to the high robustness threshold, that is... The system triggers a high-robustness mode; this condition is typically triggered by both low-priority tasks and extremely poor channel conditions, leading to... The calculated value exceeds the high robustness threshold. In this mode, the physical layer configuration is switched, enabling the full set of spread spectrum technology and the highly redundant strong error correction coding StrongFEC, as well as a complex set of aggregation and merging algorithms.

[0106] This strategy sacrifices signal processing latency for the strongest anti-interference and error correction capabilities, thereby maximizing the robustness of data transmission; this is crucial for ensuring that low-priority but high-importance data packets can still be received completely and accurately under poor channel conditions.

[0107] Example 10:

[0108] Adaptive selection and execution also include:

[0109] When the dynamic redundancy configuration index is greater than the low latency threshold and less than the high robustness threshold, the balancing mode is triggered.

[0110] The balanced mode uses a baseline configuration and adopts a preset static balance between latency and robustness.

[0111] This embodiment further defines and adaptively selects and executes a third strategy in the logic, thus constituting a complete three-mode switching logic:

[0112] When the dynamic redundancy configuration index calculated by the system is greater than the low latency threshold and less than the high robustness threshold, that is... The system triggers a balanced mode; this condition corresponds to normal communication scenarios, such as non-urgent tasks where channel risk is controllable; in this mode, the system adopts a baseline configuration, which corresponds to... Near the horizontal level, a pre-defined static balance is achieved between latency and robustness;

[0113] The addition of this strategy enables the entire adaptive selection logic to be fully closed-loop; it covers a broad intermediate zone beyond extreme real-time and extreme robustness, namely, normal communication scenarios; this avoids the system frequently oscillating between the two extreme configurations under non-extreme conditions, providing a stable and balanced normal working state, and ensuring that the system has the optimal response strategy in all scenarios.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A spread spectrum method for a full-duplex walkie-talkie, characterized in that, The specific steps include: S1. Real-time acquisition of instantaneous signal-to-interference-plus-noise ratio (SINR) and its rate of change over time; and acquisition of results from embedded AI voice keyword recognition or received physical button signals; S2. Analyze the results of embedded AI voice keyword recognition or the received physical button signals, quantify them into semantic priority factors; and combine them with a preset signal-to-interference-plus-noise ratio (SIR) threshold, a preset reference rate of change threshold, and a dynamic risk sensitivity weight to jointly evaluate the instantaneous SIR and the rate of change over time, constructing a composite channel risk factor; the construction of the composite channel risk factor includes: Calculate the static risk component, which represents how close the instantaneous signal-to-interference-plus-noise ratio (SIR) is to the SIR threshold; The dynamic risk component is calculated. The dynamic risk component is used to quantify the rate of channel degradation, and the dynamic risk component takes a positive value only when the rate of change of time is negative. Adding the static risk component to the dynamic risk component yields the composite channel risk factor; dynamic risk component The calculation formula is: in, For dynamic risk sensitivity weights; This is a function to find the maximum value. The normalized rate of change of signal-to-interference-plus-noise ratio; The instantaneous rate of change of the signal-to-interference-plus-noise ratio (SINR); The preset reference rate of change threshold, Constrained to be a constant greater than zero; S3. Based on preset system baseline redundancy and priority sensitivity weights, and coupling semantic priority factors and composite channel risk factors, a dynamic redundancy configuration index is calculated across layers. Low-latency thresholds and high-robustness thresholds are preset, and the dynamic redundancy configuration index is compared with these thresholds in real time to adaptively select and execute low-latency mode, high-robustness mode, or balanced mode, achieving closed-loop control of the physical layer configuration. The calculation formula for the dynamic redundancy configuration index is: System baseline redundancy Composite channel risk factor Multiply to reflect the redundancy increments necessary for channel degradation; And utilize semantic priority factors and priority sensitivity weight Construct timeliness control items, which are used to control the redundancy increment ( ) to exercise veto power; Among them, priority sensitivity weight The value is constrained to ensure that when a high-priority task is triggered, the timeliness control item forcibly lowers the dynamic redundancy configuration index to a level close to zero, and the constraint condition must be met. .

2. The spread spectrum method for a full-duplex walkie-talkie according to claim 1, characterized in that, The acquisition of the time change rate includes: capturing the instantaneous signal-to-interference-plus-noise ratio (SINR) of continuous time sampling points through the physical layer channel state information feedback mechanism, and performing differential calculation on the continuous sampling values ​​of the instantaneous SINR to obtain the time change rate.

3. The spread spectrum method for a full-duplex walkie-talkie according to claim 1, characterized in that, In S2, the semantic priority factor is quantified as follows: a preset rule is used to set the semantic priority factor to a high priority value when it is identified as an urgent instruction, and to set the semantic priority factor to a low priority value when it is identified as ordinary data.

4. The spread spectrum method for a full-duplex walkie-talkie according to claim 1, characterized in that, The calculation of dynamic risk components includes: Divide the rate of change over time by a preset reference rate of change threshold to obtain the normalized rate of change of signal-to-interference-plus-noise ratio. When the normalized signal-to-interference-plus-noise ratio change rate is negative, the result is multiplied by the dynamic risk sensitivity weight and used as the dynamic risk component. When the normalized rate of change of signal-to-interference-plus-noise ratio is greater than or equal to zero, the dynamic risk component is zero.

5. The spread spectrum method for a full-duplex walkie-talkie according to claim 1, characterized in that, The low latency threshold and high robustness threshold are derived from the actual processing latency and corresponding anti-interference threshold required by the walkie-talkie hardware to obtain the various discrete physical layer configuration sets supported by the walkie-talkie hardware through offline calibration, and the engineering boundary values ​​set accordingly.

6. The spread spectrum method for a full-duplex walkie-talkie according to claim 1, characterized in that, Adaptively select and execute including: When the dynamic redundancy configuration index is less than or equal to the low latency threshold, the low latency mode is triggered. The low-latency mode uses the simplest modulation and coding scheme and reduces the redundancy of error correction codes to ensure the lowest possible end-to-end latency of instructions.

7. The spread spectrum method for a full-duplex walkie-talkie according to claim 6, characterized in that, Adaptive selection and execution also include: When the dynamic redundancy configuration index is greater than or equal to the high robustness threshold, the high robustness mode is triggered. The high robustness mode employs a full suite of spread spectrum technologies and highly redundant error correction coding, at the cost of increased signal processing latency, to maximize the robustness of data transmission.

8. The spread spectrum method for a full-duplex walkie-talkie according to claim 7, characterized in that, Adaptive selection and execution also include: When the dynamic redundancy configuration index is greater than the low latency threshold and less than the high robustness threshold, the balancing mode is triggered. The balanced mode uses a baseline configuration and adopts a preset static balance between latency and robustness.

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