An adaptive variable rate method based on signal-to-noise ratio prediction

Abstract

This invention provides an adaptive variable rate method and apparatus based on signal-to-noise ratio (SNR) prediction, applicable to scattering communication. It addresses the adaptive variable rate requirement in scattering channels with fast fading characteristics, solving the problems of inaccurate SNR information and mapping relationships in determining the transmission symbol rate and coding / modulation scheme. The method includes: calculating an initial predicted SNR value and an SNR prediction error value based on an estimated SNR value, thereby obtaining an accurate predicted SNR value after error compensation; updating a dynamic threshold using the demodulation block error rate; obtaining an equivalent SNR using the predicted SNR value and the dynamic threshold; and determining the symbol rate and coding / modulation scheme by looking up a rate threshold table and a coding / modulation scheme threshold table using the equivalent SNR. This invention guides the transmission symbol rate and coding / modulation scheme with accurate SNR information and mapping relationships, effectively improving the average throughput of scattering communication systems and ensuring communication reliability.

Description

Technical Field

[0001] This invention relates to the field of scattering communication technology, and in particular to an adaptive variable rate method based on signal-to-noise ratio prediction in scattering communication. Background Technology

[0002] Tropospheric scattering communication is a beyond-line-of-sight wireless communication method that utilizes the forward scattering transmission effect of radio waves by inhomogeneities in the atmospheric medium. As a beyond-line-of-sight communication method used in both military and civilian communications, tropospheric scattering communication has advantages such as long single-hop range, strong obstacle-crossing capability, good mobility, high cost-effectiveness, and strong anti-interference and anti-interception capabilities.

[0003] After transmission through the scattering channel, the amplitude of the received signal changes within seconds or even less; this characteristic is known as the fast fading characteristic of tropospheric scattering communication. To address this time-varying characteristic of fast fading, adaptive variable rate (AVR) technology can be used to improve the average throughput of the system while ensuring transmission reliability. AVR technology involves changing the symbol rate and the coding and modulation scheme. The basic idea is to feed back the channel information calculated at the receiver to the transmitter, which then determines the symbol rate and coding and modulation scheme based on the channel characteristics. When the channel quality is good, a higher symbol rate, higher coding rate, and higher order modulation scheme can be used to increase the data transmission rate; when the channel quality is poor, a lower symbol rate, lower coding rate, and lower order modulation scheme can be used to ensure communication reliability.

[0004] However, the channel information fed back to the transmitter is the result of an estimate from the previous moment, which is delayed relative to the current channel information. In fast fading scattering communication scenarios, due to rapid channel changes, the channel information fed back to the transmitter can no longer reflect the current channel state. To solve this problem, it is necessary to predict changes in channel information. Existing technologies suffer from inaccurate channel information prediction and inaccurate mapping relationships between channel information and symbol rate and coding / modulation schemes. How to accurately predict channel information in fast fading scattering channels and provide accurate mapping relationships for adaptive variable rate schemes is a problem that needs to be solved. Summary of the Invention

[0005] This invention addresses the adaptive variable rate problem in scattering channels by providing an adaptive variable rate method based on signal-to-noise ratio prediction. This method can accurately predict changes in channel fading and guide the transmission symbol rate and coding modulation scheme in real time based on channel information. It can effectively improve the average throughput of the scattering communication system and ensure the reliability of communication, thus solving the problem that existing adaptive variable rate methods do not fully and accurately utilize the changes in scattering channel characteristics.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An adaptive variable rate method based on signal-to-noise ratio prediction, applied to both master and slave stations, includes the following steps:

[0008] The initial predicted value of the signal-to-noise ratio (SNR) and the SNR prediction error are calculated based on the SNR estimate. The initial predicted value of the SNR is calculated as follows:

[0009] according to Signal-to-noise ratio information at any moment Calculate the current Predicted signal-to-noise ratio at time:

[0010]

[0011] Wherein, the vector represents the signal-to-noise ratio information of the previous time step. and the vector representing the tap coefficients of the previous time step All contain the first and elements from previous moments indivual, The number of taps represents the adaptive filter taps, and the superscript T indicates matrix transpose.

[0012] According to the current The signal-to-noise ratio at time 1 and the error in calculating the signal-to-noise ratio at the current time are given by the predicted value.

[0013] Update the tap coefficients of the signal-to-noise ratio prediction term:

[0014]

[0015] in, , The forgetting factor has a value range of 1. ;

[0016] ], The initial value is , It is the identity matrix. The initial parameter has a range of values. ;

[0017] The signal-to-noise ratio prediction error is calculated as follows:

[0018] according to Signal-to-noise ratio prediction error information at time step Calculate the current Error prediction value at time:

[0019]

[0020] in, for The tap coefficient of the error compensation term at time;

[0021] Based on the error at the current time and error prediction value Calculate the error at the current time:

[0022]

[0023] Update the tap coefficients of the error compensation term using the error of the error:

[0024]

[0025] in, To determine the convergence step size, the range of values ​​is: ;

[0026] The final predicted signal-to-noise ratio (SNR) value is obtained based on the initial SNR prediction value and the SNR prediction error value; the final predicted SNR value is calculated as follows:

[0027]

[0028] in, This is the initial predicted value for the signal-to-noise ratio. This is the error compensation term;

[0029] While obtaining the final predicted value of the signal-to-noise ratio from the received signal, the received signal is demodulated to obtain the block error rate. The dynamic threshold is updated by the block error rate, and the equivalent signal-to-noise ratio is obtained by using the final predicted value of the signal-to-noise ratio and the dynamic threshold.

[0030] The symbol rate level is determined by looking up the rate threshold table using the equivalent signal-to-noise ratio.

[0031] The coding and modulation mode level is determined by looking up the threshold table of the coding and modulation mode at the current rate using the equivalent signal-to-noise ratio.

[0032] In one possible implementation, the dynamic threshold is updated using the block error rate, specifically as follows:

[0033] The error rate was statistically analyzed within the observation period;

[0034] If the block error rate exceeds the preset threshold, the dynamic threshold is incremented by one; if the block error rate does not exceed the preset threshold, the dynamic threshold is decremented by one.

[0035] In one possible implementation, the equivalent signal-to-noise ratio (SNR) is obtained using the predicted SNR value and a dynamic threshold, specifically as follows:

[0036] The equivalent signal-to-noise ratio of the master station is equal to the predicted signal-to-noise ratio of the master station minus the dynamic threshold of the master station, and the equivalent signal-to-noise ratio of the slave station is equal to the predicted signal-to-noise ratio of the slave station minus the dynamic threshold of the slave station.

[0037] The smaller of the two values ​​is selected as the equivalent signal-to-noise ratio.

[0038] The beneficial effects of this invention are as follows:

[0039] 1. This invention predicts the signal-to-noise ratio (SNR) at the next moment by using the current SNR to represent channel information, thus providing a basis for the transmission symbol rate and coding / modulation scheme. The SNR prediction includes calculating the initial SNR prediction value and the SNR prediction error value. Error compensation avoids the problem of inaccurate channel information prediction in the prior art.

[0040] 2. While obtaining the predicted signal-to-noise ratio (SNR), this invention updates the dynamic threshold in real time through the block error rate. The predicted SNR and the dynamic threshold are used to determine the final transmitted symbol rate and coding / modulation scheme. The introduction of the dynamic threshold avoids the problem of inaccurate mapping relationship between channel information and symbol rate and coding / modulation scheme in the background technology. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of an adaptive variable rate system based on signal-to-noise ratio prediction provided by an embodiment of the present invention;

[0043] Figure 2 This is a flowchart illustrating the implementation of a signal-to-noise ratio prediction method provided in an embodiment of the present invention;

[0044] Figure 3 This is a flowchart illustrating the implementation of an adaptive variable rate method based on signal-to-noise ratio prediction, provided in an embodiment of the present invention.

[0045] Figure 4 This is a flowchart illustrating the implementation of another adaptive variable rate method based on signal-to-noise ratio prediction provided in this embodiment of the invention.

[0046] Figure 5 This is a schematic diagram of the structure of an adaptive variable rate device based on signal-to-noise ratio prediction provided in an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Detailed Implementation

[0048] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, specific embodiments will be described below in conjunction with the accompanying drawings.

[0050] Figure 1 A schematic diagram of an adaptive variable rate system based on signal-to-noise ratio prediction provided in an embodiment of the present invention is shown below:

[0051] The transmitting end first determines the symbol rate and coding / modulation scheme of the transmitted signal, and then performs frame assembly. After transmission through the channel, the receiving end demodulates the signal and estimates the signal-to-noise ratio (SNR), finally calculating the block error rate (BOR) using the demodulated information. The receiving end uses the estimated SNR to predict the SNR for the next time step and informs the transmitting end of the predicted SNR in real time. The transmitting end then determines the symbol rate and coding / modulation scheme based on the predicted SNR and the statistically calculated BOR.

[0052] Figure 2 A flowchart illustrating the implementation of a signal-to-noise ratio prediction method according to an embodiment of the present invention is shown below:

[0053] Step S201: Calculate the predicted signal-to-noise ratio (SNR) for the current time based on the SNR of the previous time step.

[0054] In one possible implementation, the signal-to-noise ratio is predicted using an adaptive filtering algorithm. According to Signal-to-noise ratio information at any moment Calculate the current Predicted signal-to-noise ratio at time: Wherein represents the vector representing the signal-to-noise ratio information of the previous time step. and the vector representing the tap coefficients of the previous time step All contain the first and elements from previous moments indivual, This represents the number of taps in the adaptive filter.

[0055] Step S202: Calculate the error of the signal-to-noise ratio at the current time based on the signal-to-noise ratio at the current time and the predicted value.

[0056] In one possible implementation, the input signal-to-noise ratio at the current moment is The calculation method is as follows: .

[0057] Step S203: Update the tap coefficients of the signal-to-noise ratio prediction term.

[0058] Thus, the initial predicted signal-to-noise ratio is obtained.

[0059] In one possible implementation, the signal-to-noise ratio prediction term tap coefficients are updated as follows: .in , The forgetting factor has a value range of 1. . ]. Initialize to , It is the identity matrix. The initial parameter has a range of values. .

[0060] Step S204: Calculate the error prediction value for the current moment based on the error information from the previous moment.

[0061] In one possible implementation, according to Error information at time Calculate the current The method for calculating the error prediction value at time 1 is as follows: .

[0062] Step S205: Calculate the error at the current time based on the error at the current time and the predicted value.

[0063] In one possible implementation, the error at the current time... Compared with the predicted value Calculate the error between them: .

[0064] Step S206: Update the tap coefficients of the error compensation term.

[0065] In one possible implementation, the error compensation term tap coefficients are updated as follows: .in To determine the convergence step size, the range of values ​​is: .

[0066] This gives us the signal-to-noise ratio prediction error.

[0067] Step S207: Calculate the final predicted signal-to-noise ratio.

[0068] In one possible implementation, the calculation method is as follows:

[0069]

[0070] in, This is the initial predicted value for the signal-to-noise ratio. This is the error compensation term.

[0071] The aforementioned signal-to-noise ratio (SNR) prediction method for scatter communication has advantages such as fast convergence, strong tracking ability, and small prediction error at inflection points during the initial SNR prediction. However, it suffers from prediction delay. Adding error compensation effectively solves this prediction delay problem, thus providing accurate SNR prediction information for the selection of symbol rate and coding / modulation scheme.

[0072] In the adaptive variable rate method based on signal-to-noise ratio prediction, the variable rate includes changing the symbol rate and changing the coding and modulation scheme. First, the symbol rate to be transmitted is determined, and then the coding and modulation scheme to be transmitted is determined.

[0073] Figure 3 A flowchart illustrating the implementation of an adaptive variable rate method based on signal-to-noise ratio prediction, provided in this embodiment of the invention, is detailed below:

[0074] The master station equipment and the slave station equipment calculate their respective final predicted signal-to-noise ratio and block error rate. The block error rate is used to update the dynamic threshold.

[0075] In one possible implementation, the block error rate is statistically analyzed during the observation period. If the block error rate exceeds a preset threshold, the dynamic threshold is incremented by one; if the block error rate does not exceed the preset threshold, the dynamic threshold is decremented by one.

[0076] After the dynamic threshold is updated in real time, the equivalent signal-to-noise ratio of the master station is equal to the final predicted value of the master station signal-to-noise ratio minus the master station dynamic threshold, and the equivalent signal-to-noise ratio of the slave station is equal to the final predicted value of the slave station signal-to-noise ratio minus the slave station dynamic threshold. The smaller value of the two is selected as the equivalent signal-to-noise ratio.

[0077] According to the equivalent signal-to-noise ratio (SNR), if the equivalent SNR is greater than the highest modulation and coding scheme level in the current operating rate, then the symbol rate is increased; if the equivalent SNR is less than the lowest modulation and coding scheme level in the current operating rate, then the symbol rate is decreased; otherwise, the symbol rate remains unchanged.

[0078] Figure 4 The following is a detailed flowchart of another adaptive variable rate method based on signal-to-noise ratio prediction provided in an embodiment of the present invention:

[0079] The master station equipment and the slave station equipment calculate their respective final predicted signal-to-noise ratio and block error rate. The block error rate is used to update the dynamic threshold.

[0080] In one possible implementation, the block error rate is statistically analyzed during the observation period. If the block error rate exceeds a preset threshold, the dynamic threshold is incremented by one; if the block error rate does not exceed the preset threshold, the dynamic threshold is decremented by one.

[0081] After the dynamic threshold is updated in real time, the equivalent signal-to-noise ratio of the master station is equal to the final predicted value of the master station signal-to-noise ratio minus the master station dynamic threshold, and the equivalent signal-to-noise ratio of the slave station is equal to the final predicted value of the slave station signal-to-noise ratio minus the slave station dynamic threshold. The smaller value of the two is selected as the equivalent signal-to-noise ratio.

[0082] The coding and modulation scheme is determined by looking up the threshold table of coding and modulation schemes at the current rate based on the equivalent signal-to-noise ratio.

[0083] The aforementioned adaptive variable rate method based on signal-to-noise ratio (SNR) prediction addresses the rapid changes in the SNR of the received signal caused by the fast fading characteristics of the scattering channel. Different SNR thresholds correspond to different transmission symbol rates and coding / modulation schemes. However, existing technologies suffer from inaccurate SNR prediction and inaccurate mapping relationships between SNR and symbol rate / coding / modulation schemes. By combining initial SNR prediction with error compensation, this method can accurately predict SNR changes and accurately guide the transmission symbol rate and coding / modulation scheme in real time based on SNR information and dynamic thresholds. This effectively improves the average throughput of the scattering communication system while ensuring communication reliability.

[0084] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0085] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0086] Figure 5 The diagram illustrates a structural schematic of an adaptive variable rate device based on signal-to-noise ratio prediction according to an embodiment of the present invention. For ease of explanation, only the parts relevant to the embodiment of the present invention are shown, and are described in detail below:

[0087] like Figure 5 As shown, an adaptive variable rate device based on signal-to-noise ratio prediction includes: a signal-to-noise ratio prediction module 501 and a symbol rate and coding modulation method determination module 502.

[0088] The signal-to-noise ratio prediction module 501 is used to obtain the final predicted value of the signal-to-noise ratio.

[0089] The signal-to-noise ratio (SNR) prediction module 501 includes an initial SNR prediction unit and a prediction error compensation unit. The initial SNR prediction unit calculates the predicted SNR value for the current time based on the SNR of the previous time step, and calculates the error of the SNR at the current time step based on the current SNR and the predicted value, using this error to update the tap coefficients. The prediction error compensation unit calculates the predicted error value for the current time step based on the error information of the previous time step, and calculates the error of the error at the current time step based on the error at the current time step and the predicted value, using this error to update the tap coefficients. After obtaining the SNR prediction error, compensation is performed based on the initial SNR prediction value.

[0090] Symbol rate and coding modulation mode determination module 502 is used to determine symbol rate and coding modulation mode.

[0091] The symbol rate and coding / modulation scheme determination module 502 includes an equivalent signal-to-noise ratio (SNR) determination unit, a symbol rate determination unit, and a coding / modulation scheme determination unit. The equivalent SNR determination unit calculates the block error rate (BER) for both the master and slave devices and updates the dynamic threshold using the BER. After real-time updates to the dynamic threshold, the master station's equivalent SNR equals the final predicted value of the master station's SNR minus the master station's dynamic threshold, and the slave station's equivalent SNR equals the final predicted value of the slave station's SNR minus the slave station's dynamic threshold. The smaller of the two values ​​is selected as the equivalent SNR. The symbol rate determination unit looks up the rate threshold table based on the equivalent SNR. If the equivalent SNR is greater than the highest modulation / coding scheme level at the current operating rate, the symbol rate is increased; if the equivalent SNR is less than the lowest modulation / coding scheme level at the current operating rate, the symbol rate is decreased; otherwise, the symbol rate remains unchanged. The coding / modulation scheme determination unit looks up the coding / modulation scheme threshold table at the current rate based on the equivalent SNR to determine the coding / modulation scheme.

[0092] The aforementioned adaptive variable rate device based on signal-to-noise ratio (SNR) prediction, through initial SNR prediction combined with error compensation, achieves fast convergence of the predicted SNR value, strong tracking capability, and effectively avoids prediction delay, thus accurately predicting the SNR. Based on the predicted SNR information and real-time adjustment of the dynamic threshold, it can accurately guide the transmitted symbol rate and coding / modulation method, effectively improving the average throughput of the scatter communication system while ensuring communication reliability.

[0093] Figure 6 This is a schematic diagram of a terminal provided in an embodiment of the present invention. Figure 6 As shown, the terminal 6 in this embodiment includes: a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60. When the processor 60 executes the computer program 62, it implements the steps in the various embodiments of the adaptive variable rate method based on signal-to-noise ratio prediction described above, for example... Figure 2 Steps S201 to S207 are shown. Alternatively, when the processor 60 executes the computer program 62, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 5 The functions of modules / units 501 to 502 shown.

[0094] For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 62 in the terminal 6. For example, the computer program 62 can be divided into... Figure 5 Modules / units 501 to 502 are shown.

[0095] The terminal 6 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 6 may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of terminal 6 and does not constitute a limitation on terminal 6. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0096] The processor 60 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0097] The memory 61 can be an internal storage unit of the terminal 6, such as a hard disk or memory of the terminal 6. The memory 61 can also be an external storage device of the terminal 6, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 6. Furthermore, the memory 61 can include both internal storage units and external storage devices of the terminal 6. The memory 61 is used to store the computer program and other programs and data required by the terminal. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0098] In summary, this invention addresses the adaptive variable rate requirement in scattering channels with fast fading characteristics, solving the problems of inaccurate signal-to-noise ratio (SNR) information and mapping relationships in determining the transmission symbol rate and coding / modulation scheme. By using accurate SNR information and mapping relationships to guide the transmission symbol rate and coding / modulation scheme, this invention can effectively improve the average throughput of scattering communication systems and ensure communication reliability.

[0099] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0100] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0101] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0102] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0103] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0104] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0105] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the above-described adaptive variable rate method embodiments based on signal-to-noise ratio prediction. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0106] The above-described 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method of adaptive rate based on signal-to-noise ratio prediction, applied to a master station and a slave station, characterized in that, The method comprises the following steps: According to the signal-to-noise ratio estimation value, a signal-to-noise ratio initial prediction value and a signal-to-noise ratio prediction error value are obtained; the signal-to-noise ratio initial prediction value is calculated in the following way: According to Signal-to-noise ratio information at the time The current Signal-to-noise ratio prediction value at the time wherein the vector s represents the signal-to-noise ratio information of the previous time and the vector h represents the tap coefficients of the previous time each contain the elements of the previous and the previous time time, T denotes the matrix transpose; According to the current The error of the current time SNR is calculated according to the current time SNR and the predicted value. The signal-to-noise ratio prediction item tap coefficient is updated: wherein , is a forgetting factor, taking values in the range ; The initial value is is a unit matrix, is an initial parameter, and the value range is ;​​ The signal-to-noise ratio prediction error value is calculated in the following way: According to signal-to-noise ratio prediction error information calculating a current error prediction value for the time instant: wherein, is error compensation term tap coefficients for the time instant error at the current time and the error prediction value , calculate the error of the error at the current time: The error compensation item tap coefficient is updated by using the error of the error: wherein is the convergence step size, having a value in the range ; According to the signal-to-noise ratio initial prediction value and the signal-to-noise ratio prediction error value, a signal-to-noise ratio final prediction value is obtained; the signal-to-noise ratio final prediction value is calculated in the following way: wherein is the initial prediction of the signal-to-noise ratio, is an error compensation term; While obtaining the signal-to-noise ratio final prediction value from the received signal, the received signal is demodulated to obtain a block error rate, the dynamic threshold is updated by the block error rate, and an equivalent signal-to-noise ratio is obtained by using the signal-to-noise ratio final prediction value and the dynamic threshold; The symbol rate level is determined by looking up the rate threshold table according to the equivalent signal-to-noise ratio; The coding modulation mode level is determined by looking up the coding modulation mode threshold table according to the equivalent signal-to-noise ratio.

2. A method for predicting adaptive variable rate based on signal-to-noise ratio as claimed in claim 1 wherein, The dynamic threshold is updated by the block error rate in the following way: The block error rate is counted in the observation period; If the block error rate exceeds the preset threshold, the dynamic threshold is increased by one; if the block error rate does not exceed the preset threshold, the dynamic threshold is decreased by one.

3. The method of claim 1, wherein the step of predicting the SNR comprises the step of: predicting the SNR based on a plurality of SNR prediction algorithms. The equivalent signal-to-noise ratio is obtained by using the signal-to-noise ratio final prediction value and the dynamic threshold in the following way: The master station equivalent signal-to-noise ratio is equal to the master station signal-to-noise ratio final prediction value minus the master station dynamic threshold, and the slave station equivalent signal-to-noise ratio is equal to the slave station signal-to-noise ratio final prediction value minus the slave station dynamic threshold; The smaller one is selected as the equivalent signal-to-noise ratio.

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