An Adaptive Modulation Data Transmission Method and System Based on Bit Error Rate Statistics

Abstract

This invention belongs to the field of digital data transmission technology, specifically relating to an adaptive modulation data transmission method and system based on bit error rate (BER) statistics. The method includes: recording the bit error interval length between adjacent erroneous bits to obtain a set of bit error interval lengths; sorting the set of bit error interval lengths to obtain an ordered bit error interval sequence, and calculating a bit error clustering index using weighted summation; constructing background noise retention weights using the bit error clustering index, and calculating the effective background bit error rate (BER) in conjunction with the physical statistical BER; determining the target modulation scheme based on the effective background BER, and then transmitting the data. This invention can distinguish from random background noise or burst transient interference in the statistical domain. When transient interference occurs, it restores the effective background BER by stripping burst components, avoiding throughput loss caused by blindly downsampling modulation, and improving the link transmission efficiency and reliability in complex channels.

Description

Technical Field

[0001] This invention relates to the field of digital data transmission technology. More specifically, this invention relates to an adaptive modulation data transmission method and system based on bit error rate statistics. Background Technology

[0002] In high-speed digital communication systems, adaptive modulation techniques are typically used to ensure the reliability of data transmission. This involves switching between different modulation orders based on the real-time state of the channel. In actual data transmission, the channel environment exhibits highly dynamic characteristics: on the one hand, due to background noise, uniformly distributed random errors will appear in the data stream; on the other hand, due to transient interference, short-duration and high-density burst errors will be mixed into the data stream.

[0003] Existing adaptive transmission control strategies mainly rely on the average bit error rate within a fixed period to assess link quality. The system calculates the ratio of the total number of bit errors per unit time to the total number of transmitted bits and compares it with a preset threshold. When the average bit error rate exceeds the threshold, it triggers a down-order operation of the modulation scheme, thereby reducing the transmission rate to achieve higher anti-interference capability.

[0004] However, this evaluation method based on average bit error rate has significant limitations when dealing with mixed interference scenarios. The average bit error rate essentially only reflects the statistical density of errors in quantity, but completely loses the distribution characteristics of errors on the time axis. As a result, the system cannot effectively distinguish whether the current bit error is caused by continuously deteriorating background noise or by occasional transient interference. In practical applications, when encountering short-term pulse interference or beam agility, although the physical bit error rate increases sharply in a short period of time, the background physical conditions of the channel do not deteriorate substantially. The channel still has the ability to carry high-order modulated data streams during the interference gap. If the system blindly performs modulation order reduction based solely on the average bit error rate, it will cause unnecessary throughput loss in data transmission rate during non-continuous interference, which will seriously restrict data transmission efficiency. Summary of the Invention

[0005] To address the technical problem that existing methods, relying solely on average bit error rate, cannot distinguish between random bit errors and sudden interference, leading to blind down-modulation under transient interference and causing unnecessary throughput loss, this invention provides solutions in the following aspects.

[0006] In a first aspect, the present invention provides an adaptive modulation data transmission method based on bit error rate statistics, comprising:

[0007] The process involves monitoring erroneous bits during data transmission, recording the error interval length between adjacent erroneous bits, and using a sliding statistical window to obtain the current set of error interval lengths. The elements in this set are then sorted in ascending order to obtain an ordered error interval sequence. Based on this sequence, different weighting coefficients are assigned to error interval lengths of varying values, and a weighted sum is calculated to determine the error clustering index, which characterizes the error distribution pattern. The weighting coefficients decrease as the error interval length increases. The physical statistical bit error rate (PBER) is calculated based on the total number of error events and the total number of transmitted bits within the sliding statistical window. A background noise retention weight is constructed using the error clustering index, and this weight is used to correct the PBER, yielding an effective background bit error rate. Finally, the target modulation scheme is determined based on the effective background bit error rate, and data transmission is performed.

[0008] This invention records and sorts the intervals between adjacent error bits during data transmission, and uses differentiated weighted calculations to obtain an error clustering index that characterizes the error distribution pattern. This transforms the transient interference characteristics of the physical layer into statistical domain distribution characteristics, effectively distinguishing between random background noise and sudden transient interference. Based on this, the invention uses the error clustering index to construct a background noise retention weight to correct the physical statistical bit error rate, eliminating sudden interference components to restore the true effective background bit error rate. The target modulation scheme is then determined based on this effective background bit error rate for data transmission. This allows the system to avoid blindly performing modulation scheme downgrading operations when facing non-sustained sudden interference, maximizing throughput loss while ensuring link reliability, and improving the transmission efficiency and adaptive performance of the communication system under complex dynamic channels.

[0009] Preferably, the sliding statistics window always contains a fixed number of recently occurring error events.

[0010] Preferably, the weighting coefficient is ,in This indicates the total number of error events within the current sliding statistics window. This represents the index number in the ordered error interval sequence.

[0011] Preferably, the error clustering index satisfies the expression: In the formula, This indicates the current error rate clustering index; This indicates the total number of transmitted bits covered by the current sliding statistics window; Represents the first bit in the ordered bit error interval sequence. Small error interval length.

[0012] This invention assigns differentiated weighting coefficients to different error interval lengths in an ordered error interval sequence, assigning larger weighting coefficients to shorter error interval lengths and smaller weighting coefficients to longer error interval lengths. This allows the calculated error clustering index to keenly capture the distribution characteristics of errors on the time axis, amplify the clustered error characteristics caused by burst interference, and keep the statistical value of random errors stable under large sample conditions. Thus, it can accurately distinguish between random errors and burst interference patterns without needing to know the prior channel model.

[0013] Preferably, the step of calculating the physical statistical bit error rate based on the total number of bit error events and the total number of transmitted bits within the sliding statistical window includes: taking the ratio between the total number of bit error events and the total number of transmitted bits within the sliding statistical window as the physical statistical bit error rate.

[0014] This invention calculates the ratio of the total number of bit error events within a sliding statistical window to the total number of transmitted bits, thereby enabling real-time acquisition of the physical statistical bit error rate, which reflects the current total bit error level of the channel. This ensures an accurate original total baseline for subsequent stripping of burst interference components, guaranteeing the integrity and logical closed loop of data processing.

[0015] Preferably, the background noise retention weight satisfies the expression: ;in, Indicates the weighting of background noise; This indicates the current error rate clustering index; This indicates the reference threshold for determining a sudden event; Indicates the discrimination sensitivity coefficient; This represents an exponential function with the natural constant e as its base.

[0016] This invention utilizes the bit error clustering index to construct a background noise retention weight and establishes a nonlinear mapping relationship between the background noise retention weight and the bit error distribution pattern. When sudden interference occurs in the channel, causing the bit error clustering index to increase, the background noise retention weight can rapidly and nonlinearly decay. When the channel is in a stable random bit error state, the background noise retention weight remains at a high level, thereby realizing adaptive weighted adjustment for different channel states and providing a sensitive control coefficient for filtering out sudden interference bit error components from mixed bit errors.

[0017] Preferably, the step of correcting the physical statistical bit error rate using the background noise retention weight to obtain the effective background bit error rate includes: using the product between the background noise retention weight and the physical statistical bit error rate as the effective background bit error rate.

[0018] This invention obtains the effective background bit error rate by multiplying the background noise retention weight by the physical statistical bit error rate, thereby achieving mathematical masking and elimination of burst interference components in the physical bit error rate in the statistical domain. It can restore the true bit error level that is only related to the channel background from the fluctuating total bit error rate, ensuring that the adaptive modulation strategy only responds to continuous channel fading rather than transient interference. This ensures link reliability while avoiding unnecessary speed reduction due to misjudgment, maximizing the effective throughput of data transmission.

[0019] Preferably, the step of determining the target modulation scheme based on the effective background bit error rate and performing data transmission includes: when the effective background bit error rate does not exceed a preset modulation switching threshold, determining the target modulation scheme as a high-order modulation scheme and performing data transmission using the high-order modulation scheme; when the effective background bit error rate exceeds the preset modulation switching threshold, determining the target modulation scheme as a downgraded modulation scheme and performing data transmission using the downgraded modulation scheme.

[0020] Preferably, the step of monitoring bit errors during data transmission and recording the bit error interval length between adjacent bit errors includes: acquiring and parsing the received digital data stream, comparing it bit by bit with the received digital data stream using locally stored standard reference data; and, in response to detecting a bit error, starting a counter to record the number of correct bits between the bit error and the previous bit error as the bit error interval length.

[0021] Secondly, the present invention provides an adaptive modulation data transmission system based on bit error rate statistics, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the aforementioned adaptive modulation data transmission method based on bit error rate statistics is implemented.

[0022] By adopting the above technical solution, a computer program is generated from the above-mentioned adaptive modulation data transmission method based on bit error rate statistics and stored in the memory so that it can be loaded and executed by the processor. In this way, a terminal device can be made based on the memory and the processor for convenient use.

[0023] The beneficial effects of this invention are as follows: By monitoring the bit errors during data transmission and recording the interval length between adjacent bit errors, this invention uses a weighted error clustering index to transform the transient interference characteristics of the physical layer into the distribution characteristics of the statistical domain. This allows for a more accurate distinction between bit errors caused by random background noise and those caused by sudden transient interference. Furthermore, this invention uses the error clustering index to construct a background noise retention weight to correct the physical statistical bit error rate, removing the sudden interference component to restore the effective background bit error rate that only reflects the continuous background quality of the channel. Based on this, the target modulation scheme for data transmission is determined, enabling the system to maintain high-order modulation without unnecessary down-order operations when encountering non-sustained sudden interference. This ensures link transmission reliability while effectively avoiding throughput loss and improving data transmission efficiency and adaptability in complex channel environments. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating an adaptive modulation data transmission method based on bit error rate statistics according to the present invention;

[0025] Figure 2 This is a schematic diagram illustrating the changes in the error clustering index;

[0026] Figure 3 This is a diagram comparing the physical statistical bit error rate with the effective background bit error rate.

[0027] Figure 4 This diagram illustrates a comparison between the modulation strategies of this invention, which are based on effective background bit error rate, and those of traditional methods, which are based on physical statistical bit error rate. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0030] This invention discloses an adaptive modulation data transmission method based on bit error rate statistics, referring to... Figure 1 This includes steps S1-S3:

[0031] S1. Monitor the bit errors during data transmission, record the bit error interval length between adjacent bit errors, and use a sliding statistical window to obtain the set of bit error interval lengths at the current moment.

[0032] It should be noted that, in order to distinguish between random bit errors caused by Gaussian white noise or rain attenuation and burst bit errors caused by phased array agility or interference, this invention transforms the transient interference characteristics of the physical layer into the distribution characteristics of the statistical domain by recording the number of bits between adjacent bit errors, thus providing standardized input data for subsequent steps.

[0033] Specifically, the received digital data stream is acquired and parsed, and a bit-by-bit comparison is performed between the received digital data stream and locally stored standard reference data or a preset sequence. Whenever an erroneous bit is detected, a counting logic is activated to record the number of correct bits between the erroneous bit and the previous erroneous bit, thereby obtaining the error interval length.

[0034] A sliding statistics window is set up so that it always contains the K most recent bit error events. The set of bit error interval lengths within the current sliding statistics window is obtained, where K represents the total number of bit error events within the sliding statistics window. In this embodiment, it is set to... In other embodiments, implementers may choose an integer between 100 and 1000 depending on the actual implementation situation.

[0035] Furthermore, in order to eliminate the interference of the chronological order of bit errors on the distribution pattern analysis, the elements in the bit error interval length set are arranged in ascending order of numerical value to obtain an ordered bit error interval sequence.

[0036] S2. Sort the elements in the set of bit error interval lengths in ascending order to obtain an ordered bit error interval sequence. Based on the ordered bit error interval sequence, assign differentiated weight coefficients to bit error interval lengths with different values ​​and perform weighted summation to calculate the bit error clustering index, which characterizes the bit error distribution pattern. The weight coefficients decrease as the bit error interval length increases.

[0037] It should be noted that there is a clear physical mapping relationship between the error distribution characteristics in the channel and the type of channel interference. When the channel is affected by Gaussian white noise or rain attenuation, the occurrence of error events is random and independent, conforming to the characteristics of a Poisson process. At this time, the interval length between adjacent errors statistically follows an exponential distribution, and its distribution pattern is relatively stable. When the channel is affected by phased array agility or pulse interference, the errors exhibit dense cluster bursts and long periods of silence, and the error interval length shows extreme polarization, with a very high degree of uneven distribution. Therefore, this invention constructs an error clustering index to achieve automatic identification of the error distribution pattern and establishes a benchmark that can adaptively distinguish between random errors and burst errors.

[0038] Specifically, the current error clustering index is obtained based on the ordered error interval sequence:

[0039]

[0040] In the formula, This represents the current error rate clustering index, which theoretically ranges from 0 to 1. This indicates the total number of error events within the current sliding statistics window; This indicates the total number of transmitted bits covered by the current sliding statistics window; Indicates the index number in the ordered error interval sequence; Represents the first bit in the ordered bit error interval sequence. Small error interval length.

[0041] In the formula, The weighting coefficient is the error interval length. This invention utilizes the weighting coefficient... Different weighting is applied to different bit error interval lengths, assigning larger weights to bit error intervals with smaller values ​​and smaller weights to bit error intervals with larger values. This makes the bit error clustering index highly sensitive to the distribution pattern of short intervals. That is, when a large number of bit errors are concentrated in extremely small intervals, the weighted summation term... This significantly reduces the error rate, thereby driving up the error clustering index.

[0042] When the channel is affected by Gaussian white noise or rain attenuation, the bit error events are independent, and the bit error interval length follows an exponential distribution. Mathematically, when a specific linearly decreasing weight is applied to an ordered sequence of bit error intervals following an exponential distribution and then weighted and summed, under the large sample limit, i.e., the window size... When the sum is sufficiently large, the expected value of its weighted sum asymptotically approaches half the product of the total interval length and the total number of events, i.e. Therefore, the weighted sum is divided by the normalization factor. Subsequently, its expected value approaches 0.5, thus making the final calculated bit error rate clustering index... The error clustering index tends to be close to 0.5. This characteristic stems from the memorylessness of the exponential distribution and the approximate uniform distribution of its order statistics in large samples. It can serve as a natural benchmark for distinguishing between random and burst errors without manual calibration. When the channel is affected by phased array agility or impulse interference, errors become highly clustered, and the distribution of error interval lengths becomes extremely uneven. The ordered error interval sequence contains a large number of error interval lengths close to 0 and a very small number of huge error interval lengths, which leads to a smaller weighted summation result, thus significantly increasing the error clustering index and making it approach 1. It should be noted that the error clustering index is scale-invariant. Regardless of the magnitude of the bit error rate, as long as the distribution pattern remains unchanged, the error clustering index remains stable and can accurately measure the degree of burst interference.

[0043] For example, Figure 2The diagram illustrates the changes in the bit error rate clustering index. When the channel is in the rain attenuation phase, the bit error rate clustering index remains stable around 0.5. When the channel encounters sudden interference, the bit error rate clustering index rapidly jumps and exceeds the threshold of 0.6. The bit error rate clustering index is highly sensitive to the bit error distribution pattern.

[0044] S3. Calculate the physical statistical bit error rate (PBER) based on the total number of bit error events and the total number of transmitted bits within the sliding statistical window. Construct a background noise retention weight using the bit error clustering index. Use the background noise retention weight to correct the PBER and obtain the effective background bit error rate. Determine the target modulation scheme based on the effective background bit error rate and perform data transmission.

[0045] It should be noted that the device's modulation and demodulation functions support multiple modes, including DBPSK and DQPSK. The fundamental goal of adaptive modulation is to adapt to the continuous background quality of the channel, i.e., the degree of rain attenuation, rather than to cope with transient interference. Although burst interference increases the total number of bit errors, it does not change the background noise floor of the channel. If the bit error clustering index is very high, it indicates that the current bit errors are mainly caused by transient interference, and the background signal-to-noise ratio of the channel has not actually deteriorated. Therefore, this invention uses the bit error clustering index to construct a nonlinear mask to remove the burst component from the physical bit error rate and restore the effective background bit error rate, thereby avoiding the erroneous switching of the modulation mode from high-order (such as DQPSK) to low-order (such as DBPSK) due to transient interference.

[0046] Specifically, the physical statistical bit error rate is calculated based on the total number of bit error events and the total number of transmitted bits within the sliding statistical window:

[0047]

[0048] In the formula, This represents the current physical statistical bit error rate; This indicates the total number of error events within the current sliding statistics window; This indicates the total number of transmitted bits covered by the current sliding statistics window.

[0049] Furthermore, background noise retention weights are constructed based on the bit error rate clustering index, and the effective background bit error rate is calculated by combining the physical statistical bit error rate:

[0050]

[0051]

[0052] in, This represents the current effective background bit error rate, which excludes burst interference features. This represents the current physical statistical bit error rate; This represents the background noise retention weight, with a value ranging from 0 to 1; This indicates the current error rate clustering index; This represents the burst detection reference threshold, used to determine whether the current channel is in a burst interference state. In this embodiment, it is set to... The logic behind this setting is that the theoretical value of the error clustering index of random errors is 0.5. In order to tolerate a certain statistical fluctuation, a value slightly larger than the theoretical value is taken as the threshold. In other embodiments, implementers can set this value between 0.55 and 0.65 according to the tolerance for randomness. This represents the discrimination sensitivity coefficient, used to adjust the sensitivity of the background noise retention weight to changes in the bit error rate clustering exponent. In this embodiment, it is set to... In other embodiments, implementers can set this value between 8 and 15 according to the actual implementation situation. When the smoothness of the decision result is required to be high, a smaller discrimination sensitivity coefficient can be set, such as 8. When the truncation response speed of sudden interference is required to be high, a larger discrimination sensitivity coefficient can be set, such as 15. This represents an exponential function with the natural constant e as its base.

[0053] When the error accumulation index Less than or equal to the sudden detection reference threshold For example, the error clustering index A value close to 0.5 corresponds to rain attenuation or Gaussian white noise scenarios. If it is a negative value, then Approaching 0, background noise retains weight. When the value approaches 1, it indicates that the current physical statistical bit error rate is mainly composed of background noise, which the system treats as an effective component and retains. At this point, the effective background bit error rate is... Approximately equal to the physical statistical bit error rate When the error rate clustering index Greater than the reference threshold for sudden detection For example, the error clustering index A value close to 0.9 corresponds to phased array agility or pulse interference scenarios. It is a large positive number. The weighting increases rapidly, allowing background noise to be preserved. It rapidly approaches 0, thus causing the effective background bit error rate to approach 0.

[0054] Furthermore, in response to the effective background bit error rate not exceeding a preset modulation switching threshold, the system determines that the current bit error is non-persistent interference, identifies the target modulation scheme as a higher-order modulation scheme, and uses the higher-order modulation scheme for data transmission; in response to the effective background bit error rate exceeding the preset modulation switching threshold, the system identifies the target modulation scheme as a downgraded modulation scheme, and uses the downgraded modulation scheme for data transmission. The modulation switching threshold is set by the implementers according to actual communication quality requirements, for example, 0.005.

[0055] For example, Figure 3 This is a diagram comparing the physical statistical bit error rate with the effective background bit error rate. Figure 4 This diagram illustrates a comparison between the modulation strategies of this invention based on effective background bit error rate and those of traditional methods based on physical statistical bit error rate. It can be seen that, compared to traditional methods, this invention recovers a significant throughput gain during interference.

[0056] This invention also discloses an adaptive modulation data transmission system based on bit error rate statistics, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement an adaptive modulation data transmission method based on bit error rate statistics according to this invention.

[0057] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

Claims

1. An adaptive modulation data transmission method based on bit error rate statistics, characterized in that, include: Monitor the bit errors during data transmission, record the bit error interval length between adjacent bit errors, and use a sliding statistical window to obtain the set of bit error interval lengths at the current moment. The elements in the set of bit error interval lengths are sorted in ascending order to obtain an ordered bit error interval sequence. Based on the ordered bit error interval sequence, different weight coefficients are assigned to bit error interval lengths of different values ​​and weighted summation is performed to calculate the bit error clustering index, which characterizes the bit error distribution pattern. The weight coefficients decrease as the bit error interval length increases. The physical statistical bit error rate is calculated based on the total number of bit error events and the total number of transmitted bits within the sliding statistical window. Background noise retention weights are constructed using the bit error clustering index, and the physical statistical bit error rate is corrected using these background noise retention weights to obtain the effective background bit error rate. The target modulation scheme is determined based on the effective background bit error rate, and data transmission is then performed. The weighting coefficient is ,in This indicates the total number of error events within the current sliding statistics window. Indicates the index number in the ordered error interval sequence; Error clustering index satisfies the expression: ; This indicates the current error rate clustering index; This indicates the total number of transmitted bits covered by the current sliding statistics window; Represents the first bit in the ordered bit error interval sequence. Small error interval length; The background noise retention weight satisfies the expression: ; Indicates the weighting of background noise; This indicates the reference threshold for determining a sudden event; Indicates the discrimination sensitivity coefficient; This represents an exponential function with the natural constant e as its base.

2. The adaptive modulation data transmission method based on bit error rate statistics according to claim 1, characterized in that, The sliding statistics window always contains a fixed number of recently occurring error events.

3. The adaptive modulation data transmission method based on bit error rate statistics according to claim 1, characterized in that, The calculation of the physical statistical bit error rate based on the total number of bit error events and the total number of transmitted bits within the sliding statistical window includes: The ratio of the total number of bit error events within the sliding statistical window to the total number of transmitted bits is used as the physical statistical bit error rate.

4. The adaptive modulation data transmission method based on bit error rate statistics according to claim 1, characterized in that, The step of correcting the physical statistical bit error rate using background noise preservation weights to obtain an effective background bit error rate includes: The effective background bit error rate is the product of the background noise retention weight and the physical statistical bit error rate.

5. The adaptive modulation data transmission method based on bit error rate statistics according to claim 1, characterized in that, The step of determining the target modulation scheme based on the effective background bit error rate and performing data transmission includes: When the effective background bit error rate does not exceed the preset modulation switching threshold, the target modulation mode is determined to be a higher-order modulation mode, and data transmission is performed using the higher-order modulation mode; when the effective background bit error rate exceeds the preset modulation switching threshold, the target modulation mode is determined to be a downgraded modulation mode, and data transmission is performed using the downgraded modulation mode.

6. The adaptive modulation data transmission method based on bit error rate statistics according to claim 1, characterized in that, The monitoring of bit errors during data transmission, including recording the error interval length between adjacent bit errors, includes: The system acquires and parses the received digital data stream, and performs a bit-by-bit comparison with the received digital data stream using the locally stored standard reference data. In response to the detection of a bit error, a counter is started to record the number of correct bits between the bit error and the previous bit error as the bit error interval length.

7. An adaptive modulation data transmission system based on bit error rate statistics, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement an adaptive modulation data transmission method based on bit error rate statistics according to any one of claims 1-6.

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