Adaptive coding modulation method and device, equipment and storage medium

Abstract

The invention discloses a self-adaptive coding modulation method, device and equipment and a storage medium, and relates to the technical field of communication, the method is based on the time delay characteristic of satellite communication, an ACM scheme based on satellite communication is improved from the two aspects of channel estimation and MCS selection, channel state prediction is added in a channel estimation module, and channel state prediction is carried out on the ACM scheme based on satellite communication. A more accurate channel state value is obtained, the time-varying characteristic and the synchronous time delay characteristic of a satellite communication channel are comprehensively considered, an MCS switching algorithm based on variance correction is improved in the aspect of the MCS switching algorithm, a reference channel state threshold value is corrected by using a correction factor, and the accuracy of the channel state value is improved. According to the method, the MCS mode more conforming to the current communication environment is selected based on the corrected reference channel state threshold value, the satellite communication performance is improved, satellite and link resources can be saved, the system throughput rate and the system performance can be well balanced, and therefore the system has good robustness.

Description

Technical Field

[0001] The present application relates to the field of communication technology and provides an adaptive coding modulation method, device, equipment and storage medium. Background Art

[0002] Adaptive coded modulation (ACM) technology is a communication link control technology. The core of ACM technology is to select the appropriate modulation and coding scheme (MCS) method according to the current channel state. It can effectively combat the shadow slow fading characteristics in broadband satellite mobile communication channels. Different MCS methods are selected according to different channel states or different transmission rate and bit error rate requirements. It can not only ensure the correct transmission of signals, but also make full use of system margins, improve system resource utilization, obtain higher system information throughput, and achieve high-quality and efficient transmission of satellite communication systems.

[0003] At present, the MCS mode switching algorithm used in most ACM technologies is to select the corresponding MCS mode based on the comparison between the signal to interference plus noise ratio (SNR) (referred to as signal to noise ratio) of the received signal and the determined signal to noise ratio threshold. This method is applicable to ordinary ground communication systems, but in satellite communication systems, the channel transmission distance is usually long and the delay of the satellite communication channel is high, that is, the channel state information currently estimated has changed after being transmitted through the feedback channel, resulting in the MCS mode selected by the transmitter being no longer applicable to the current channel environment, resulting in poor transmission performance of the satellite communication system. Summary of the invention

[0004] The embodiments of the present application provide an adaptive coding modulation method, apparatus, device and storage medium for improving the accuracy of the selected MCS mode and improving the transmission performance of the satellite communication system.

[0005] On the one hand, an adaptive coding modulation method is provided, which is applied to a signal receiving end in a satellite communication system, and the method comprises:

[0006] Performing channel state estimation on a signal sent by a signal transmitting end to obtain a first channel state value sequence at a current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal;

[0007] Perform channel state information prediction based on the first channel state value sequence and second channel state value sequences at multiple historical moments to obtain a predicted channel state value sequence at a current moment;

[0008] The predicted channel state value sequence is fed back to the signal transmitting end, so that the signal transmitting end determines a modulation and coding strategy MCS mode based on the predicted channel state value sequence.

[0009] On the one hand, an adaptive coding modulation method is provided, which is applied to a signal transmitting end in a satellite communication system, and the method comprises:

[0010] Receive a predicted channel state value sequence sent by a signal receiving end; wherein the predicted channel state value sequence is predicted by the signal receiving end based on a channel state value sequence obtained by multiple historical channel state estimations;

[0011] Determining a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and a reference channel state threshold;

[0012] After modulating and encoding the signal to be sent based on the target MCS mode, the processed signal is sent to the signal receiving end.

[0013] On the one hand, an adaptive coding modulation device is provided, which is applied to a signal receiving end in a satellite communication system, and the device comprises:

[0014] A channel state estimation unit, configured to perform channel state estimation on a signal sent by a signal transmitting end, and obtain a first channel state value sequence at a current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal;

[0015] A channel state prediction unit, configured to predict channel state information based on the first channel state value sequence and second channel state value sequences at multiple historical moments, to obtain a predicted channel state value sequence at a current moment;

[0016] The sending unit is used to feed back the predicted channel state value sequence to the signal sending end, so that the signal sending end determines the modulation and coding strategy MCS mode based on the predicted channel state value sequence.

[0017] On the one hand, an adaptive coding modulation device is provided, which is applied to a signal transmitting end in a satellite communication system, and the device comprises:

[0018] A receiving unit, configured to receive a predicted channel state value sequence sent by a signal receiving end; wherein the predicted channel state value sequence is predicted by the signal receiving end based on a channel state value sequence obtained by multiple historical channel state estimations;

[0019] An MCS mode selection unit, configured to determine a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and a reference channel state threshold;

[0020] The sending unit is used to modulate and encode the signal to be sent based on the target MCS mode, and then send the processed signal to the signal receiving end.

[0021] On the one hand, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any one of the above methods when executing the computer program.

[0022] In one aspect, a computer storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0023] In one aspect, a computer program product is provided, the computer program product comprising a computer program, the computer program being stored in a computer-readable storage medium. A processor of a computer device reads the computer program from the computer-readable storage medium, and the processor executes the computer program, so that the computer device performs the steps of any of the above methods.

[0024] In the embodiment of the present application, after the receiving end estimates the channel state of the signal sent by the signal transmitting end and obtains the first channel state value sequence at the current moment, the channel state information is predicted based on the first channel state value sequence and the second channel state value sequence at multiple historical moments to obtain the predicted channel state value sequence at the current moment, and then the predicted channel state value sequence is fed back to the signal transmitting end, so that the signal transmitting end determines the modulation and coding strategy MCS method based on the predicted channel state value sequence. That is, the method in the embodiment of the present application takes into account the delay characteristics of the satellite communication channel, and predicts the channel state value based on the channel state values ​​obtained multiple times in history, that is, the current or subsequent channel state values ​​are predicted with reference to the historical channel changes, so that the predicted channel state value sequence is time-effective, so that the MCS method selected by the transmitting end based on this is more accurate and suitable for the current channel state, thereby improving the communication transmission performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0026] Figure 1a and Figure 1b A schematic diagram of the ACM technology used in the related art;

[0027] Figure 2 A schematic diagram of an application scenario provided for an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the architecture of the improved ACM system provided in the embodiment of the present application;

[0029] Figure 4 A schematic diagram of a flow chart of an adaptive coding modulation method provided in an embodiment of the present application;

[0030] Figure 5 A schematic diagram of a prediction process performed by a channel prediction module provided in an embodiment of the present application;

[0031] Figure 6 A channel prediction algorithm modeling block diagram provided in an embodiment of the present application;

[0032] Figure 7 A schematic diagram of another method flow of the adaptive coding modulation method provided in an embodiment of the present application;

[0033] Figure 8a to Figure 8d A schematic diagram of the simulation provided in the embodiment of the present application;

[0034] Fig. 9 A schematic diagram of the structure of an adaptive coding modulation device provided in an embodiment of the present application;

[0035] Fig.10 A schematic diagram of the structure of another adaptive coding modulation device provided in an embodiment of the present application;

[0036] Fig.11 A schematic diagram of the structure of a computer device provided in an embodiment of the present application;

[0037] Fig.12 A schematic diagram of the composition structure of another computer device to which an embodiment of the present application is applied. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described below in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. In the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other arbitrarily. In addition, although the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in an order different from that here.

[0039] The terms "first" and "second" in the embodiments of the present application are used to distinguish different objects, rather than to describe a specific order. In addition, the term "comprising" and any of their variations are intended to cover non-exclusive protection. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices. "Multiple" in the present application can mean at least two, for example, two, three or more, and the embodiments of the present application are not limited.

[0040] To facilitate understanding of the technical solutions provided in the embodiments of the present application, some key terms used in the embodiments of the present application are explained here:

[0041] Auto-Regressive and Moving Average Model (ARIMA) algorithm: is a commonly used local statistical algorithm for time series prediction. The ARIMA algorithm is particularly useful for data sets that can be mapped to stationary time series. The statistical characteristics of stationary time series (such as autocorrelation) are independent of time. Data sets with stationary time series usually contain a combination of signals and noise. The signal may show a sinusoidal oscillation pattern or have a seasonal component. The ARIMA algorithm is like a filter that separates the signal from the noise, and then infers the future signal to make predictions. In other words, the essence of the ARIMA algorithm is to first find out the rules with trends (trend), seasonality (seasonal), and business scenario periodicity (domain cycle) in the data, and extract the regular information from the data layer by layer. The final data is left with irregular information, or noise, which is ideally white noise.

[0042] ACM technology is a newer communication link control technology that can effectively combat the shadow slow fading characteristics in mobile communication channels. It selects different MCS modes according to different channel states or different transmission rate and bit error rate requirements. It can not only ensure the correct transmission of signals, but also make full use of system margins, improve system resource utilization, and obtain higher system information throughput. It has great research significance and value.

[0043] The core of ACM technology is to select the appropriate MCS mode according to the channel state information, and design an effective MCS switching algorithm to ensure that the satellite communication system reasonably selects the MCS mode according to the current channel state. An effective and appropriate MCS mode can achieve high-quality and efficient transmission of the satellite communication system. At present, most ACM switching algorithms compare the signal-to-noise ratio (SNR) of the received data with the determined signal-to-noise ratio threshold value to determine which MCS mode to use.

[0044] See also Figure 1a and Figure 1b As shown, Figure 1a It is a system framework diagram of the ACM system used in the related technology. Figure 1b The figure is a technical flow diagram of the ACM system used in the related art. In the related art, the transmitter of the ACM system is composed of an adaptively adjustable coding module and a modulation module, and the receiver is composed of a signal-to-noise ratio estimation module, a demodulation module, and a decoding module. After the transmitter transmits the signal, the signal reaches the receiver through the satellite broadband mobile communication channel, and the receiver estimates the signal-to-noise ratio of the transmitted signal and transmits it back to the transmitter through the reverse link. The transmitter selects the appropriate MCS mode according to the estimated signal-to-noise ratio through the MCS switching algorithm, and encodes and modulates the next transmitted signal according to the selected MCS mode.

[0045] However, the ACM technology in the related art does not take into account the delay characteristics of the satellite communication channel, that is, the channel state information currently estimated has changed after being transmitted through the feedback channel, that is, the channel state information (CSI) is outdated, which causes the transmitter to incorrectly select the MCS mode, reducing system performance. In addition, in the MCS switching algorithm in the related art, most of the MCS switching algorithms based on variance are directly selected to estimate the reference SNR, and the received data information of the receiving end is not fully utilized to correct the reference SNR, resulting in the low accuracy of the selected MCS mode.

[0046] Based on this, an embodiment of the present application provides an adaptive coding modulation method, in which the receiving end performs channel state estimation for the signal sent by the signal transmitting end, and after obtaining the first channel state value sequence at the current moment, the channel state information is predicted based on the first channel state value sequence and the second channel state value sequence at multiple historical moments to obtain the predicted channel state value sequence at the current moment, and then the predicted channel state value sequence is fed back to the signal transmitting end, so that the signal transmitting end determines the modulation and coding strategy MCS mode based on the predicted channel state value sequence. That is, the method in the embodiment of the present application takes into account the delay characteristics of the satellite communication channel, and predicts the channel state value based on the channel state values ​​obtained multiple times in history, that is, the current or subsequent channel state value is predicted with reference to the historical channel changes, so that the predicted channel state value sequence is time-effective, so that the MCS mode selected by the transmitting end based on this is more accurate.

[0047] In addition, the reference channel state threshold is corrected by adding a reference state correction factor, so that the final reference channel state threshold is more in line with the actual situation and has higher reliability, so that the MCS method determined by the transmitter based on the corrected reference channel state threshold is more in line with the current communication environment, thereby improving the accuracy of the MCS method.

[0048] The following briefly introduces the application scenarios to which the technical solutions of the embodiments of the present application can be applied. It should be noted that the application scenarios introduced below are only used to illustrate the embodiments of the present application and are not limited. In the specific implementation process, the technical solutions provided by the embodiments of the present application can be flexibly applied according to actual needs.

[0049] The solution provided in the embodiment of the present application can be applied to channels with a Gaussian distribution of channel probability, such as satellite broadband channel communication scenarios. Figure 2 As shown, it is a schematic diagram of an application scenario provided by an embodiment of the present application. In this scenario, multiple satellite base stations located on the orbital plane in a satellite communication system and terminals communicating with the satellite base stations may be included, exemplarily including satellite base stations 102_1, satellite base stations 102_2, ..., satellite base stations 102_N in the figure, where N is a positive integer, and the size of N is determined according to specific needs and scenarios in practice. User terminal 101 communicates with other destination user terminals 103 through satellite base stations.

[0050] Compared with ground communication systems, low-orbit satellite mobile communication systems have a wider coverage range, and multiple low-orbit satellites can achieve seamless global coverage. Users have an increasing demand for the new generation of 5G low-orbit satellite mobile communication systems with high transmission rates, wide coverage, and strong flexibility. At present, the development of low-orbit satellite mobile communication systems has evolved from voice and short messages to high-speed broadband services, and multimedia broadband communication services have become the development needs of the next generation of low-orbit satellite mobile communication systems. In future mobile communications, 5G low-orbit satellite mobile communication systems can meet more complex application scenarios and services.

[0051] The user terminal 101 may be, for example, a mobile phone, a tablet computer (PAD), a laptop, a desktop computer, a smart TV, a smart vehicle device, a smart wearable device, or any other device with satellite communication capabilities. In addition to the user terminal 101, a ground base station 104 may also be included, and the ground base station 104 may communicate with other user terminals 103 or other ground base stations through a satellite base station.

[0052] like Figure 3 As shown, it is a schematic diagram of the architecture of an improved ACM system provided by an embodiment of the present application, in which an improved channel estimation module and an improved MCS switching algorithm module are added. Among them, the improved channel estimation module calculates the channel state information CSI based on the locally known pilot sequence and the received pilot sequence, and feeds back the calculated CSI of the current link state. During the feedback process, it is necessary to correct the expired CSI according to the feedback channel state prediction, that is, to perform channel state prediction, and the signal transmitter affects the MCS switching of ACM according to the current CSI. The improved MCS switching algorithm module improves the MCS switching algorithm based on variance correction, and uses the correction factor to correct the reference SNR obtained by the first calculation. The LLR soft information can be used to determine the position information of the uncertain information bit or the error-prone bit, and the reference SNR is further corrected according to the error-prone set. The specific process will be explained in the subsequent process, and will not be described in detail here.

[0053] The following describes the adaptive coding modulation method provided by the exemplary embodiment of the present application in combination with the application scenario and system architecture described above, with reference to the accompanying drawings. It should be noted that the above application scenario is only shown to facilitate understanding of the spirit and principles of the present application, and the implementation of the present application is not limited in this respect.

[0054] See also Figure 4As shown, it is a flow chart of the adaptive coding modulation method provided in the embodiment of the present application. The method can be jointly executed by the signal transmitting end and the channel receiving end. When the signal transmitting end is a satellite, the signal receiving end can be a ground base station or a user terminal. When the signal transmitting end is a ground base station or a user terminal, the signal receiving end can be a satellite. In the embodiment of the present application, the signal transmitting end can also be referred to as a signal source, and the signal receiving end can also be referred to as a signal destination. The specific implementation process of the method is as follows:

[0055] Step 401: The signal sending end sends a signal to the signal receiving end.

[0056] When the signal transmitting end needs to send a signal to the signal receiving end, it can select the MCS mode based on the channel state value sequence previously fed back by the signal receiving end, and then use the MCS mode to perform modulation and coding related processing on the information to be sent to obtain the signal sent to the signal receiving end.

[0057] Exemplarily, after the signal transmitting end generates the binary bit stream required for satellite communication, it encodes and modulates the binary bit stream using the set coding and modulation methods to ensure the reliability of satellite communication transmission. Then, the corresponding filter order is set according to the required sampling rate, and the modulated data is digitally processed by the RF front-end to be converted into the corresponding baseband signal. Finally, the RF module of the signal transmitting end power amplifies the baseband signal from the RF front-end to make it a high-power RF signal that can support long-distance satellite transmission, and then sends it to the signal receiving end.

[0058] Step 402: The signal receiving end performs channel state estimation on the received signal to obtain a first channel state value sequence at the current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal.

[0059] Considering that in the ACM technology of the related art, the signal receiving end directly transmits the channel state information of the channel state estimation back to the signal sending end through the feedback channel, and then the ACM module selects the corresponding MCS according to the feedback channel information and the preset threshold value, which is obviously not suitable for high-latency satellite communication systems. Therefore, in the embodiment of the present application, the estimation of the signal state is improved, and the improved channel estimation module obtains a more accurate channel state value sequence through channel state prediction, that is, the process of the above step 402 can be obtained by Figure 3 Considering that the channel state values ​​obtained multiple times in history can reflect the changes in the channel, the channel state values ​​estimated by the improved channel estimation module based on the channel state values ​​obtained multiple times in history are more accurate, which can improve the problem of channel state value expiration in high-latency scenarios to a certain extent.

[0060] In the embodiment of the present application, the channel state estimation is an estimation of the current channel state, which can be characterized by the channel state information CSI, and the channel state information can contain all channel-related information, so it can be noise variance, channel estimation matrix or SNR, etc. Taking SNR as an example, the channel state estimation can adopt a signal-to-noise ratio estimation algorithm based on no data assistance, or a signal-to-noise ratio estimation algorithm based on data assistance, which is not limited in the embodiment of the present application.

[0061] In actual scenarios, due to the high satellite launch cost in satellite communications and the tight frequency resources in satellite communications, non-data-assisted methods can be used in satellite communication systems to estimate the channel state in satellite communications. For example, the signal-to-noise ratio estimation algorithm based on non-data assistance may include but is limited to the error vector magnitude (EVM) algorithm, the second-order fourth-order moment (M2-M4) signal-to-noise ratio estimation algorithm, the polynomial fitting signal-to-noise ratio estimation algorithm, etc.

[0062] In addition, in order to improve the estimation performance, a data-assisted signal-to-noise ratio estimation algorithm can also be used, such as the data-assisted signal-to-noise ratio estimation algorithm, the maximum likelihood signal-to-noise ratio estimation algorithm, the linear minimum mean squared error (LMMSE) signal-to-noise ratio estimation algorithm, etc. Among them, in terms of performance, the ML signal-to-noise ratio estimation algorithm is closer to the estimated Cramer-Rao lower bound, but its large amount of calculation has higher requirements on the receiver, and it will also need to obtain more data information from the received signal. In actual scenarios, it can be selected according to the actual scenario.

[0063] Next, the process of channel state estimation is introduced by taking the LMMSE channel estimation method to calculate SNR as an example.

[0064] Specifically, after receiving the signal, the signal receiving end extracts the corresponding signal located at the pilot insertion position in the received signal, and then compares it with the locally known pilot sequence to calculate the receiving SNR corresponding to the pilot sequence. Then, when the sending end sends a sequence of x = (x1, x2, ..., x N ), the received sequence is y=(y1,y2,…,y N ), and y=hx+n, where n represents the Gaussian white noise sequence, and h represents the attenuation coefficient associated with the satellite channel.

[0065] According to the MMSE algorithm, the estimation error is orthogonal to the estimation result, so the following relationship exists:

[0066]

[0067] Among them, R h is the autocorrelation matrix of the channel estimate h, E() represents the averaging operation of the results in the brackets, I is the unit matrix, x H The conjugate matrix representing x.

[0068] The core of the LMMSE algorithm is to use the mean to replace the M*M order matrix in the MMSE calculation process. The diagonal of the matrix is ​​the power of the known data (i.e., the pilot sequence), and the data at other positions are the correlation values ​​of the pilot sequence and its own delayed data. The correlation value can approximately satisfy the normal distribution with a mean of 0. The data on the diagonal is the average power of the known data, and the data at other positions is 0, thereby simplifying the MMSE. Therefore, the channel estimation value calculated by LMMSE is:

[0069] h LMMSE =R h (R h +E(1 / x 2 ) -1 δ 2 I) -1 *h LS

[0070] Among them, h LS =y / x represents the channel estimation value based on the least squares (LS) estimation.

[0071] The received SNR can be as follows:

[0072]

[0073] Where P represents the transmit power, represents the system noise variance and can usually be obtained through measurement.

[0074] Step 403: Perform channel state information prediction based on the first channel state value sequence and second channel state value sequences at multiple historical moments to obtain a predicted channel state value sequence at the current moment.

[0075] In the embodiment of the present application, considering that the traditional LMMSE-based channel estimation algorithm has good performance and relatively low computational complexity, it does not consider the problem of channel state information expiration due to large delay in satellite communication environment. On this basis, the embodiment of the present application updates the channel state information in real time without increasing the computational complexity too much, thereby improving the practicality of the system, that is, compared with the traditional LMMSE algorithm, it increases a certain complexity, but it considers the influence of delay expansion on the calculation results of the LMMSE algorithm, so it can improve the timeliness and effectiveness of the channel state information CSI estimated by LMMSE to a certain extent. Among them, in the improved LMMSE algorithm, by adding a channel prediction module at the signal receiving end, the channel state information CSI estimated by the channel maintains real-time and effectiveness in satellite communications with large delays, so that the improved LMMSE algorithm is suitable for satellite communications with large delays.

[0076] Specifically, the prediction process of the channel prediction module can be found in Figure 5 As shown, the following steps are included:

[0077] Step 4031: Perform a stationary process on the first channel state value sequence to obtain a corresponding stationary time series.

[0078] A stationary sequence refers to a sequence without a trend and does not change over time. Therefore, in the embodiment of the present application, the non-stationary sequence is converted into a stationary sequence through the above-mentioned stabilization process, thereby assisting the subsequent process to effectively model the sequence.

[0079] Specifically, if the input CSI time series or SNR time series, that is, the first channel state value sequence mentioned above is not a stationary sequence, it is converted into a stationary sequence through a differential operation. If only one differential operation is required to convert it into a stationary sequence, then d=1. If m differential operations are required, then d=m, where d is the number of differential operations performed when the time series becomes stationary.

[0080] In one possible implementation, whether a stabilization operation is needed can be determined by detecting the characteristics of the time series data. Specifically, the time series data can be visualized and analyzed to observe the data's trends, periodicity, seasonality and other characteristics to determine whether a differential operation is needed, that is, whether a stabilization operation is needed.

[0081] Step 4032: Based on the autocorrelation coefficient and partial autocorrelation coefficient of the stationary time series, determine the autoregressive level and moving average order corresponding to the current moment.

[0082] Specifically, the range of the estimated autoregressive order p and the moving average order q can be estimated based on the autocorrelation function (ACF) and partial autocorrelation function (PACF) images of the time series data. If the time series data is not stationary, a differential operation is required. Based on the stationarity of the time series data and the ACF and PACF images, the range of the differential order d can be preliminarily estimated. Then, based on the above estimated parameter range, the model parameters can be selected and compared using methods such as grid search to find the optimal model parameter combination.

[0083] Step 4033: Using the autoregressive hierarchy and the moving average order as the parameters of the autoregressive integrated moving average model, perform autoregressive integrated moving average processing on the stationary time series corresponding to the first channel state value sequence and the stationary time series of the second channel state value sequences at multiple historical moments to obtain a predicted channel state value sequence.

[0084] Furthermore, after determining the parameter combination, the parameters of the autoregressive integrated moving average model may be assigned values ​​based on the parameters determined above, and then the autoregressive integrated moving average model may be used to realize the prediction of the channel state value.

[0085] In practical applications, an autoregression and moving average model (ARMA), an autoregression integrated moving average model (ARIMA) or a similar model thereof may be used as a channel prediction algorithm to implement channel prediction. Figure 6 As shown, a possible channel prediction algorithm modeling block diagram provided by an embodiment of the present application is shown. Taking ARIMA as an example, it predicts the value of the current time k by using the values ​​of several items before the current time k in the time series and the random error, which includes the following: Figure 6 The three stages shown are:

[0086] (1) Identification stage

[0087] In the identification stage, it is necessary to convert the expired CSI time series data into a stationary series. The conversion formula is as follows:

[0088]

[0089] Among them, B is the hysteresis factor, CSI k represents the expired CSI time series after stabilization, d represents the difference order, and k represents the kth moment.

[0090] (2) Evaluation and testing phase

[0091] In the evaluation and testing phase, it is necessary to use the stationary time series CSI k The number of autocorrelation rows and partial autocorrelation functions are used to calculate the order p of the autoregressive model and the order q of the moving average model.

[0092] In one possible implementation, the MATLAB function autocorr (stationary time series) is used, and the last value outside the threshold is the required q value, and autocorr refers to calculating the autocorrelation function of the time series; the MATLAB function parcorr (stationary time series) is used, and the last value outside the threshold is the required p value, and parcorr is used to calculate the partial autocorrelation coefficient of the time series.

[0093] (3) Application stage

[0094] For stationary time series CSI k When making ARIMA model predictions, the predicted channel state value sequence can be expressed as:

[0095]

[0096] Among them, λ i is the autoregression coefficient, θ i Moving average coefficient, ε k is a white noise sequence, Characterizes the conjugate of the CSI (SNR) sequence at the k-1th moment (i.e., the signal state value sequence obtained by channel estimation), Characterizes the conjugate of the CSI (SNR) sequence at the kpth time.

[0097] In one possible implementation, when applied to a high-orbit satellite channel environment, considering that in a high-orbit satellite channel environment, the satellite channel characteristics approach a Gaussian white channel, the CSI (SNR) sequence can be considered to be a stationary sequence, so there is no need to convert it into a stationary sequence by the difference method. In this case, the difference order d=0 in the ARIMA model.

[0098] Step 404: The signal receiving end feeds back the predicted channel state value sequence to the signal transmitting end.

[0099] Step 405: The signal transmitting end determines a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and the reference channel state threshold.

[0100] Specifically, the reference channel state value is divided into several regions through the reference channel state threshold, each region corresponds to an optimal coding modulation mode, and when the predicted channel state value sequence is within a certain threshold, the corresponding coding modulation mode is selected.

[0101] Step 406: The signal transmitting end performs modulation and coding processing on the signal to be transmitted based on the target MCS mode, and then sends the processed signal to the signal receiving end.

[0102] In an embodiment of the present application, after selecting the target MCS mode, the signal transmitting end can modulate and encode the signal to be transmitted according to the target MCS mode, and then send the processed signal to the signal receiving end. Since the above-mentioned target MCS mode is selected based on the predicted channel state value sequence, it is more in line with the actual channel changes and more accurate. Therefore, the target MCS mode finally selected is more suitable for the current channel state, so that the transmission performance is higher and the system throughput is improved.

[0103] Specifically, the signal transmitter, as a signal source, can encode and modulate the binary stream through the target MCS method after generating the binary bit stream required for satellite communication to ensure the reliability of satellite communication transmission, and then set the corresponding filter order according to the required sampling rate, perform RF front-end digital processing on the modulated data to convert it into the corresponding baseband signal, and finally use the RF module of the signal transmitter to power amplify the baseband data from the RF front end to make it a high-power RF signal that can support long-distance satellite transmission.

[0104] It should be noted that in actual scenarios, the process of predicting the channel state value sequence can actually be performed by the signal transmitter, that is, the signal transmitter can make predictions based on the channel state value sequences received multiple times in the past, and participate in the selection of the MCS method based on the predicted results.

[0105] In the embodiment of the present application, considering that the ACM technology in the related art usually adopts the threshold method, and usually directly selects the reference SNR for comparison in the MCS switching algorithm, the reliability of the reference SNR is not high, resulting in the MCS mode selection not being the most accurate. Based on this, in the embodiment of the present application, a correction factor is added to the MCS switching algorithm to correct the reference SNR, so that the corrected reference SNR has a higher reliability, thereby improving the accuracy of the MCS mode selection. Figure 7 As shown, it is a schematic diagram of another method flow of the adaptive coding modulation method provided in an embodiment of the present application, which may include the following steps:

[0106] Step 701: The channel receiving end determines a reference channel state threshold based on the mean and standard deviation of historical channel state values ​​of N symbols within a code block period.

[0107] In an embodiment of the present application, an MCS switching algorithm based on probability estimation is provided, the core of which is to solve the probability that the received SNR is less than the reference SNR within a code block period. After the optimal probability value is selected through multiple simulations, the reference value is deduced from the probability formula, that is, after the receiving end estimates the SNR through the channel, it will correct the SNR based on the probability, and then send the corrected SNR to the transmitting end. The calculation of the correction factor should be combined with the channel environment.

[0108] In a possible implementation manner, taking the channel state value as SNR as an example, a reference SNR based on variance correction is calculated according to the channel state value predicted by the channel:

[0109] SNR r =μ r -δ r

[0110] Among them, SNR r represents the calculated reference SNR, μ r represents the mean of the estimated SNR, δ r It represents the standard deviation of the received SNR of N symbols within a code block period T. The calculation formula is as follows:

[0111]

[0112]

[0113] It should be noted that the reference channel state threshold may be used continuously after being initially determined, may be changed dynamically in real time, or may be updated periodically.

[0114] Step 702: The channel receiving end determines a reference state correction factor based on historical channel state values ​​obtained through historical estimation.

[0115] In the embodiment of the present application, the reference state correction factor can be determined in combination with the real-time channel estimation situation, and then the reference channel state threshold is corrected to improve the accuracy of MCS mode selection.

[0116] In a possible implementation manner, the historical channel state value obtained by historical estimation may include any of the following:

[0117] (1) a first channel state value sequence, i.e., the most recent channel state estimation based on the signal sent by the channel transmitter;

[0118] (2) Historical channel state values ​​of N symbols in the most recent code block period;

[0119] (3) Historical channel state values ​​of N symbols within a preset period. The preset period can be set based on the needs in the actual scenario and is not limited here.

[0120] Specifically, the calculation of the reference state correction factor may be determined based on the difference between the historical channel state value obtained by historical estimation and the reference channel state threshold.

[0121] In actual application scenarios, the reference state correction factor can be calculated by analyzing the distribution of data points above the reference SNR and below the reference SNR based on the reference SNR calculated above. For points corresponding to data points above the reference SNR, data points above the reference SNR indicate that these points can be correctly decoded at a higher SNR. For points below the reference SNR, data at these locations can only be correctly decoded at a lower SNR than the reference SNR. Therefore, in order to achieve good decoding performance, a correction factor needs to be introduced to ensure that most data points have higher decoding performance as much as possible. The reference state correction factor can be obtained based on the average value of the reference SNR and the actual SNR.

[0122] Specifically, K first-category channel state values ​​greater than the reference channel state threshold and M second-category channel state values ​​less than the reference channel state threshold can be determined from the historical channel state values ​​obtained by historical estimation, and then, based on the average of the differences between the K first-category channel state values ​​and the reference channel state threshold and the average of the differences between the M second-category channel state values ​​and the reference channel state threshold, the reference state correction factor is determined.

[0123] In a possible implementation, the calculation formula of the reference state correction factor may be as follows:

[0124]

[0125] Where K+M≤N, d k The difference between the number of data points above the reference SNR and the reference SNR, d m Indicates the difference between the number of data points below the reference SNR and the reference SNR.

[0126] Based on the above calculation method, when the SNR of most signals is higher than the reference SNR, it means that the reference SNR is too small. Therefore, the value of the reference SNR needs to be increased, that is, the variance subtracted when calculating the reference SNR is reduced. If the reference SNR is larger than the actual SNR of most signals, that is, smaller than the weight of the number of reference signals, the variance subtracted when calculating the reference SNR is increased, so that the reference SNR is more suitable for the current situation.

[0127] In the embodiments of the present application, the values of K and M can be determined in combination with the log-likelihood ratio (LLR) information of the decoding soft information at the receiving end, and the following two methods can be used to determine them:

[0128] In the first method, K first-class channel state values and M second-class channel state values can be determined from the historical channel state values corresponding to all LLR values. In this case, all data points are involved in the correction process of the above reference SNR.

[0129] In the second method, in order to simplify the algorithm, the improved MCS switching algorithm can be further improved in combination with the received soft information. Since when the decoding soft information LLR is close to 0, it indicates that the decoding at this position is uncertain, because the probability of decoding to 0 or 1 is 0.5 each. Therefore, the SNR corresponding to the soft information LLR being 0 can be compared, and the SNR of this part of the data points can be analyzed and compared, and the reference SNR for MCS switching can be calculated more accurately.

[0130] Specifically, based on the maximum likelihood ratio LLR value, the historical channel state values corresponding to the LLR uncertainty bit set can be obtained from the historically estimated historical channel state values, and from the historical channel state values corresponding to the uncertainty bit set, K first-class channel state values and M second-class channel state values can be determined. That is to say, the reference SNR can be corrected only based on the LLR uncertainty bit set, and the LLR uncertainty bit set is the SNR value corresponding to LLR being 0.

[0131] In actual application, the calculated LLR value can be compared with 0. If the LLR is near 0, it means that the probability of decoding to 0 and 1 at this position is equal. Therefore, according to the positions where these LLR values are equal to 0 or close to 0, the corresponding SNR values within the code block period can be selected to calculate the SNR of the MCS switching algorithm. For example, if there are N symbols in a code block period originally, then there are N SNR values corresponding to them. Then, it is necessary to calculate through these N values. The simplification by the LLR value lies in that if the set of SNR values corresponding to the positions where the LLR is close to 0 has only M (M < N), then directly use these M values to calculate the SNR of the MCS, thereby simplifying the operation.

[0132] Step 703: The signal receiving end sends the reference state correction factor to the signal sending end.

[0133] Step 704: The signal sending end corrects the reference channel state threshold based on the reference state correction factor.

[0134] Step 705: The signal sending end determines the target MCS mode based on the corrected reference channel state threshold.

[0135] In a possible implementation manner, the signal receiving end may directly send the obtained reference state correction factor to the signal transmitting end, and then the signal transmitting end corrects the reference channel state threshold and continues the subsequent process.

[0136] Step 706: The signal receiving end corrects the reference channel state threshold based on the reference state correction factor to obtain a corrected reference channel state threshold.

[0137] Step 707: The signal receiving end sends the modified reference channel state threshold to the signal transmitting end.

[0138] Step 708: The signal transmitting end determines the MCS mode based on the modified reference channel state threshold.

[0139] In another possible implementation, the signal receiving end may also correct the reference channel state threshold based on the reference state correction factor, and send the corrected reference channel state threshold to the signal sending end, and then the signal sending end may directly continue the subsequent process based on the corrected reference channel state threshold.

[0140] Specifically, the SNR formula of the modified MCS switching algorithm is as follows:

[0141] SNR r =μ r -(1-α)δ r

[0142] That is, when the actual SNR is neither higher nor lower than the reference SNR calculated for the first time, the final reference SNR calculation result is consistent with the first calculation. When the weight of the signal higher than the reference signal is greater than the weight of the signal lower than the reference signal, the final reference SNR will be greater than the reference SNR calculated for the first time.

[0143] It should be understood that in an actual scenario, the process of steps 703 to 705 and the process of steps 706 to 708 are performed alternatively and can be set according to the needs of the actual scenario and the actual situation.

[0144] To summarize, in the embodiments of the present application, based on the delay characteristics of satellite communication, improvements are made to the ACM scheme based on satellite communication from two aspects: channel estimation and MCS selection. Channel state prediction is added to the channel estimation module, and the time-varying and synchronization delay characteristics of the satellite communication channel are comprehensively considered. In terms of the MCS switching algorithm, the MCS switching algorithm based on variance correction is improved, and the reference SNR obtained by the first calculation is corrected using a correction factor. The LLR soft information can be used to determine the position information of the uncertain information bit or the error-prone bit, and the reference SNR is further corrected according to the error-prone set.

[0145] Specifically, the improved channel estimation scheme adds the satellite channel delay characteristics on the basis of the LMMSE channel estimation algorithm, that is, adds a channel prediction module, and updates and predicts the expired CSI in combination with the actual satellite channel environment, so as to obtain channel state information that is more in line with the current satellite channel environment, making the CSI more accurate, and effectively avoiding the degradation of system performance caused by expired CSI; the improved MCS switching based on variance correction introduces the influence factor α, and the calculation of the influence factor α comprehensively considers the actual SNR and reference SNR characteristics of N symbols within the time period T, and the LLR can also be further updated and calculated according to the soft information of the receiving end. The calculated reference SNR is made more realistic, and the received soft information is fully utilized to further correct the MCS switching, so that the MCS mode is more in line with the satellite communication environment. The algorithm has good performance in channels where the channel probability distribution is a Gaussian distribution, which can ensure that the system bit error rate is not lower than the target bit error rate, and can improve the system throughput performance to a certain extent; a more accurate MCS value is obtained through two iterative calculations, so that the MCS mode that is more in line with the current communication environment is selected, and the satellite communication performance is improved, which can save on-board and link resources and achieve a good balance between system throughput and system performance, so that the system has good robustness.

[0146] In the embodiment of the present application, the improved ACM technology is used for implementation, and the implementation environment is the satellite broadband channel of the Ka band (referring to the radio frequency band of 10.7-12.75GHz). The Ka band is very sensitive to weather. For the satellite system in the geostationary orbit of the band, weather fading is an important factor affecting the transmission quality. In the embodiment of the present application, the data obtained by measuring the propagation characteristics by Olympus is assumed. Assume that the envelope r of the received signal ω , phase r θ , then the envelope and phase both obey the normal distribution, and the corresponding probability density function is as follows:

[0147]

[0148]

[0149] Where μ1 and μ2 represent the mean of the envelope and phase of the received signal, respectively; δ1 and δ2 represent the standard deviation of the envelope and phase of the received signal, respectively.

[0150] See Tables 1 and 2 below, which are weather data obtained by C.Loo through Olympus propagation characteristic measurements, where Table 1 shows the signal envelope measurement parameter values ​​under different weather conditions, and Table 2 shows the signal phase measurement parameter values ​​under different weather conditions.

[0151] weather <![CDATA[Mean μ1]]> <![CDATA[Variance δ1 2 > sunny 0.455 0.00056 Dark Cloud 0.346 0.00272 light rain 0.483 0.00003 thunderstorm 0.436 0.01386 Light Snow 0.5 0.00021 Frost 0.482 0.00062 moderate rain 0.332 0.02

[0152] Table 1

[0153] weather <![CDATA[Mean μ1]]> <![CDATA[Variance δ1 2 > sunny 0.455 0.00056 Dark Cloud 0.346 0.00272 light rain 0.483 0.00003 thunderstorm 0.436 0.01386 Light Snow 0.5 0.00021 Frost 0.482 0.00062 moderate rain 0.332 0.02

[0154] Table 2

[0155] The Ka-band satellite broadband channel modeling used in the present application embodiment is shown in Figure 8a As shown, the received signal can be expressed as follows:

[0156] y(t)=(μ1+δ1*randn(t))*(exp(j(μ2+δ2*randn(t))))+n(t)

[0157] In practical applications, Figure 3 The simulation structure diagram shown in the figure is in MATLAB R2021b.lnk. The simulation environment is as follows: Figure 8b and Figure 8c The steps shown, Figure 8b The steps corresponding to the signal sending end specifically include the following steps:

[0158] Step 801a: The binary bit stream required for satellite communication is coded with a cyclic redundancy check (CRC), and is coded and modulated with a forward error correction (FEC) code according to the currently specified MCS mode.

[0159] Step 802a: The signal passes through the RF front-end signal processing module to form a baseband signal.

[0160] Step 803a: The transmitter module at the signal transmitting end generates a corresponding radio frequency signal and transmits it.

[0161] in, Figure 8c The steps adopted by the signal receiving end specifically include the following steps:

[0162] Step 801b: The receiver at the signal receiving end receives signals with different distortion levels, performs filtering, and obtains data of corresponding sequences.

[0163] Step 802b: The signal receiving end uses the soft information of the filtered pilot sequence and the local pilot sequence information to perform LMMSE channel estimation to obtain CSI. Due to the delay characteristics of the satellite communication environment, the CSI information estimated by LMMSE will expire. Therefore, on this basis, the ARIMA algorithm is added to predict and update the channel state CSI after the delay.

[0164] Step 803b: The signal receiving end corrects and updates the reference SNR in combination with the improved variance-based MCS switching algorithm, and can further correct the reference SNR in combination with the LLR uncertainty bit of the soft information of the decoding end. The correction formula is SNR r =μ r -(1-α)δ r .

[0165] Step 804b: The signal receiving end uses the delay-based LMMSE channel estimation algorithm and the MCS switching algorithm based on the receiving end decoding soft information to more accurately locate and update the action MCS actually used in the end, so that the improved ACM solution has good performance in bit error rate and system throughput.

[0166] Finally, the simulation performance analysis results obtained according to the above simulation process are shown in Figure 8d As shown in the figure, the figure is a simulation comparison of 64 pilot symbols under different conditions in the satellite traffic environment. The simulation conditions are the "clear sky" simulation parameters in Tables 1 and 2. The satellite pilot signal after channel estimation and equalization based on the improved LMMSE algorithm, the pilot signal without channel estimation and equalization, and the local satellite pilot signal are compared and simulated. Among them, "×" represents transmitted symbols, that is, the local satellite pilot signal, "○" represents Equalized symbols, that is, the satellite pilot signal after channel estimation and equalization based on the improved LMMSE algorithm, and "◇" represents Unequalized symbols, that is, the satellite pilot signal without channel estimation and equalization. Figure 8d It can be seen that the pilot signal without channel estimation and equalization is scattered and has a large gap with the local reference signal. The compensation signal after the improved LMMSE channel estimation and equalization is distributed near the local reference signal and is not scattered without channel estimation and equalization, which significantly improves the communication performance.

[0167] See also Fig. 9 Based on the same inventive concept, the embodiment of the present application further provides an adaptive coding modulation device 90, which is applied to a signal receiving end in a satellite communication system. The device includes:

[0168] A channel state estimation unit 901 is used to perform channel state estimation on a signal sent by a signal transmitting end to obtain a first channel state value sequence at a current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal;

[0169] A channel state prediction unit 902 is used to predict channel state information based on the first channel state value sequence and the second channel state value sequence at multiple historical moments to obtain a predicted channel state value sequence at a current moment;

[0170] The sending unit 903 is used to feed back the predicted channel state value sequence to the signal sending end, so that the signal sending end determines the modulation and coding strategy MCS mode based on the predicted channel state value sequence.

[0171] In a possible implementation manner, the device further includes a reference threshold correction unit 904, configured to:

[0172] Determining a reference state correction factor based on a historical channel state value obtained by historical estimation;

[0173] Sending the reference state correction factor to the signal transmitting end, so that the signal transmitting end corrects the reference channel state threshold based on the reference state correction factor, and determines the MCS mode based on the corrected reference channel state threshold; or,

[0174] After the reference channel state threshold is corrected based on the reference state correction factor, the corrected reference channel state threshold is sent to the signal transmitting end, so that the signal transmitting end determines the MCS mode based on the corrected reference channel state threshold.

[0175] In a possible implementation manner, the reference threshold correction unit 904 is further configured to:

[0176] Determine a reference channel state threshold based on a mean and a standard deviation of historical channel state values ​​of N symbols within a code block period;

[0177] A reference state correction factor is determined based on a difference between a historical channel state value obtained by historical estimation and a reference channel state threshold.

[0178] In a possible implementation manner, the reference threshold correction unit 904 is specifically configured to:

[0179] Determine, from the historical channel state values ​​obtained by historical estimation, K first-category channel state values ​​greater than a reference channel state threshold, and M second-category channel state values ​​less than the reference channel state threshold, K+M≤N;

[0180] The reference state correction factor is determined based on an average of differences between K first-category channel state values ​​and a reference channel state threshold, and an average of differences between M second-category channel state values ​​and the reference channel state threshold.

[0181] In a possible implementation manner, the reference threshold correction unit 904 is specifically configured to:

[0182] Based on the maximum likelihood ratio LLR value, from the historical channel state values ​​obtained by historical estimation, the historical channel state values ​​corresponding to the LLR uncertainty bit set are obtained, and from the historical channel state values ​​corresponding to the uncertainty bit set, K first-category channel state values ​​and M second-category channel state values ​​are determined; or,

[0183] K first-category channel state values ​​and M second-category channel state values ​​are determined from the historical channel state values ​​corresponding to all LLR values.

[0184] In a possible implementation manner, the historical channel state value obtained by historical estimation is any one of the following:

[0185] a first channel state value sequence;

[0186] Historical channel state values ​​of N symbols in the most recent code block period;

[0187] Historical channel state values ​​of N symbols within a preset period;

[0188] In a possible implementation manner, the channel state value is SNR.

[0189] In a possible implementation manner, the channel state prediction unit 902 is specifically configured to:

[0190] Performing stationary processing on the first channel state value sequences respectively to obtain corresponding stationary time sequences;

[0191] Based on the autocorrelation coefficient and partial autocorrelation coefficient of the stationary time series, determine the autoregressive hierarchy and moving average order corresponding to the current moment;

[0192] With the autoregressive hierarchy and the moving average order as the parameters of the autoregressive integrated moving average model, the stationary time series corresponding to the first channel state value sequence and the respective stationary time series of the second channel state value sequences at multiple historical moments are processed by autoregressive integrated moving average to obtain a predicted channel state value sequence.

[0193] The device can be used to execute the method executed by the channel receiving end in each embodiment of the present application. Therefore, for the functions that can be implemented by each functional module of the device, reference can be made to the description of the aforementioned embodiments and no further details will be given.

[0194] See also Fig.10 Based on the same inventive concept, the embodiment of the present application further provides an adaptive coding modulation device 100, which is applied to a signal transmitting end in a satellite communication system. The device includes:

[0195] The receiving unit 1001 is configured to receive a predicted channel state value sequence sent by a signal receiving end; wherein the predicted channel state value sequence is predicted by the signal receiving end based on a channel state value sequence obtained by multiple historical channel state estimations;

[0196] The MCS mode selection unit 1002 is used to determine a target MCS mode from a plurality of preset MCS modes based on a predicted channel state value sequence and a reference channel state threshold;

[0197] The sending unit 1003 is used to perform modulation and coding processing on the signal to be sent based on the target MCS mode, and then send the processed signal to the signal receiving end.

[0198] In a possible implementation manner, the device further includes a reference threshold correction unit 1004, configured to:

[0199] receiving a reference state correction factor sent by a signal receiving end, and correcting a reference channel state threshold based on the reference state correction factor to obtain a corrected reference channel state threshold;

[0200] The MCS mode selection unit 1002 is specifically configured to:

[0201] Based on the predicted channel state value sequence and the modified reference channel state threshold, a target MCS mode is determined from a plurality of preset MCS modes.

[0202] The device can be used to execute the method executed by the channel sending end in each embodiment of the present application. Therefore, for the functions that can be implemented by each functional module of the device, reference can be made to the description of the aforementioned embodiments and no further details will be given.

[0203] Through the above-mentioned device, the expired CSI is updated and predicted in combination with the actual satellite channel environment, and the channel state information that is more in line with the current satellite channel environment is obtained to make the CSI more accurate, which can effectively avoid the degradation of system performance caused by expired CSI, and make full use of the received soft information to further correct the MCS switching, so that the MCS is more in line with the satellite communication environment. The algorithm has good performance in the channel whose channel probability distribution is Gaussian distribution, that is, it can ensure that its system bit error rate is not lower than the target bit error rate, and can improve the system throughput performance to a certain extent.

[0204] See also Fig.11 Based on the same technical concept, the present application also provides a computer device. In one embodiment, the computer device can be Figure 2 The base station shown is, for example, a ground base station or a satellite base station, and the computer device is Fig.11 As shown, it includes a memory 1101 , a communication module 1103 and one or more processors 1102 .

[0205] The memory 1101 is used to store computer programs executed by the processor 1102. The memory 1101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and programs required to run the functions of the embodiment of the present application, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0206] The memory 1101 may be a volatile memory, such as a random-access memory (RAM); the memory 1101 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); or the memory 1101 is any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 1101 may be a combination of the above memories.

[0207] The processor 1102 may include one or more central processing units (CPU) or a digital processing unit, etc. The processor 1102 is configured to implement the above-mentioned adaptive coding modulation method when calling the computer program stored in the memory 1101 .

[0208] The communication module 1103 is used to communicate with terminal devices and other base stations.

[0209] The specific connection medium between the memory 1101, the communication module 1103 and the processor 1102 is not limited in the embodiment of the present application. Fig.11 In the embodiment, the memory 1101 and the processor 1102 are connected via a bus 1104. The bus 1104 is connected to the processor 1102 via a bus 1104. Fig.11 The connections between the other components are described with bold lines, which are only for illustration and are not intended to be limiting. The bus 1104 can be divided into an address bus, a data bus, a control bus, etc. For ease of description, Fig.11 The diagram shows that only one thick line is used, but this does not mean that there is only one bus or one type of bus.

[0210] The memory 1101 stores a computer storage medium, which stores computer executable instructions. The computer executable instructions are used to implement the adaptive coding modulation method of the embodiments of the present application. The processor 1102 is used to execute the adaptive coding modulation method of the above-mentioned embodiments.

[0211] In another embodiment, the computer device may also be a terminal device, such as Figure 2 In this embodiment, the structure of the computer device can be as follows: Fig.12 As shown, it includes: a communication component 1210, a memory 1220, a display unit 1230, a camera 1240, a sensor 1250, an audio circuit 1260, a Bluetooth module 1270, a processor 1280 and other components.

[0212] The communication component 1210 is used to communicate with the server. In some embodiments, a circuit wireless fidelity (Wireless Fidelity, WiFi) module may be included. The WiFi module belongs to a short-range wireless transmission technology. The computer device can help the user to send and receive information through the WiFi module.

[0213] The memory 1220 can be used to store software programs and data. The processor 1280 executes various functions and data processing of the terminal device by running the software programs or data stored in the memory 1220. The memory 1220 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. The memory 1220 stores an operating system that enables the terminal device to run. In the present application, the memory 1220 can store an operating system and various application programs, and can also store code for executing the adaptive coding modulation method of the embodiment of the present application.

[0214] The display unit 1230 can also be used to display information input by the user or information provided to the user and a graphical user interface (GUI) of various menus of the terminal device. Specifically, the display unit 1230 may include a display screen 1232 disposed on the front of the terminal device. The display screen 1232 may be configured in the form of a liquid crystal display, a light emitting diode, etc.

[0215] The display unit 1230 can also be used to receive input digital or character information and generate signal input related to user settings and function control of the terminal device. Specifically, the display unit 1230 may include a touch screen 1231 arranged on the front of the terminal device, which can collect user touch operations on or near it, such as clicking a button, dragging a scroll box, etc.

[0216] The touch screen 1231 can be covered on the display screen 1232, or the touch screen 1231 and the display screen 1232 can be integrated to realize the input and output functions of the terminal device, and the integrated display screen can be referred to as a touch display screen. In this application, the display unit 1230 can display the application and the corresponding operation steps.

[0217] The camera 1240 can be used to capture still images, and users can post comments on the images taken by the camera 1240 through the application. There can be one or more cameras 1240. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the processor 1280 to convert it into a digital image signal.

[0218] The terminal device may further include at least one sensor 1250, such as an acceleration sensor 1251, a distance sensor 1252, a fingerprint sensor 1253, and a temperature sensor 1254. The terminal device may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, a light sensor, and a motion sensor.

[0219] The audio circuit 1260, the speaker 1261, and the microphone 1262 can provide an audio interface between the user and the terminal device. The audio circuit 1260 can transmit the electrical signal converted from the received audio data to the speaker 1261, which is converted into a sound signal for output. The terminal device can also be equipped with a volume button for adjusting the volume of the sound signal. On the other hand, the microphone 1262 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1260 and converted into audio data, and then the audio data is output to the communication component 1210 to be sent to, for example, another terminal device, or the audio data is output to the memory 1220 for further processing.

[0220] The Bluetooth module 1270 is used to exchange information with other Bluetooth devices having Bluetooth modules through the Bluetooth protocol. For example, the terminal device can establish a Bluetooth connection with a wearable computer device (such as a smart watch) that also has a Bluetooth module through the Bluetooth module 1270 to exchange data.

[0221] The processor 1280 is the control center of the terminal device. It uses various interfaces and lines to connect various parts of the entire terminal. It executes various functions of the terminal device and processes data by running or executing software programs stored in the memory 1220 and calling data stored in the memory 1220. In some embodiments, the processor 1280 may include one or more processing units; the processor 1280 may also integrate an application processor and a baseband processor, wherein the application processor mainly processes an operating system, a user interface, and an application program, and the baseband processor mainly processes wireless communications. It is understandable that the above-mentioned baseband processor may not be integrated into the processor 1280. In the present application, the processor 1280 can run an operating system, an application program, a user interface display and a touch response, as well as the adaptive coding and modulation method of the embodiment of the present application. In addition, the processor 1280 is coupled to the display unit 1230.

[0222] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores a computer program. When the computer program runs on a computer, the computer executes the steps of the adaptive coding modulation method described above in this specification according to various exemplary embodiments of the present application.

[0223] In some possible implementations, various aspects of the adaptive coding modulation method provided by the present application may also be implemented in the form of a computer program product, which includes a computer program. When the program product is run on a computer device, the computer program is used to enable the computer device to execute the steps of the adaptive coding modulation method according to various exemplary embodiments of the present application described above in this specification. For example, the computer device may execute the steps of each embodiment.

[0224] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0225] The program product of the embodiment of the present application may adopt a portable compact disk read-only memory (CD-ROM) and include a computer program, and can be run on a computer device. However, the program product of the present application is not limited thereto. In the present application, the readable storage medium may be any tangible medium containing or storing a program, and the computer program included therein may be used by or in combination with a command execution system, apparatus or device.

[0226] A readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, wherein a readable computer program is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with a command execution system, apparatus, or device.

[0227] The computer program embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0228] Computer programs for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages.

[0229] It should be noted that, although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0230] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that the operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps.

[0231] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0232] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0233] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An adaptive coding modulation method, characterized in that: Applied to a signal receiving end in a satellite communication system, the method comprises: Performing channel state estimation on a signal sent by a signal transmitting end to obtain a first channel state value sequence at a current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal; Perform channel state information prediction based on the first channel state value sequence and second channel state value sequences at multiple historical moments to obtain a predicted channel state value sequence at a current moment; The predicted channel state value sequence is fed back to the signal transmitting end, so that the signal transmitting end determines a modulation and coding strategy MCS mode based on the predicted channel state value sequence.

2. The method according to claim 1, characterized in that The method further comprises: Determining a reference state correction factor based on a historical channel state value obtained by historical estimation; Sending the reference state correction factor to the signal transmitting end, so that the signal transmitting end corrects the reference channel state threshold based on the reference state correction factor, and determines the MCS mode based on the corrected reference channel state threshold; or, After the reference channel state threshold is corrected based on the reference state correction factor, the corrected reference channel state threshold is sent to the signal transmitting end, so that the signal transmitting end determines the MCS mode based on the corrected reference channel state threshold.

3. The method according to claim 2, characterized in that Before determining the reference state correction factor based on the historical channel state value obtained by historical estimation, the method further includes: Determine the reference channel state threshold based on the mean and standard deviation of historical channel state values ​​of N symbols within a code block period; Then the determination of the reference state correction factor based on the historical channel state value obtained by historical estimation includes: The reference state correction factor is determined based on a difference between a historical channel state value obtained by historical estimation and the reference channel state threshold.

4. The method according to claim 2, characterized in that The determining the reference state correction factor based on the difference between the historical channel state value obtained by historical estimation and the reference channel state threshold value comprises: Determine, from the historical channel state values ​​obtained by historical estimation, K first-category channel state values ​​greater than the reference channel state threshold, and M second-category channel state values ​​less than the reference channel state threshold, K+M≤N; The reference state correction factor is determined based on an average of differences between the K first-category channel state values ​​and the reference channel state threshold, and an average of differences between the M second-category channel state values ​​and the reference channel state threshold.

5. The method according to claim 4, characterized in that Determining, from historical channel state values ​​obtained by historical estimation, K first-category channel state values ​​greater than the reference channel state threshold, and M second-category channel state values ​​less than the reference channel state threshold, comprising: Based on the maximum likelihood ratio (LLR) value, obtaining the historical channel state value corresponding to the LLR uncertainty bit set from the historical channel state value obtained by the historical estimation, and determining the K first-category channel state values ​​and the M second-category channel state values ​​from the historical channel state values ​​corresponding to the uncertainty bit set; or, The K first-category channel state values ​​and the M second-category channel state values ​​are determined from the historical channel state values ​​corresponding to all LLR values.

6. The method according to any one of claims 2 to 5, characterized in that: The historical channel state value obtained by the historical estimation is any one of the following: the first channel state value sequence; Historical channel state values ​​of N symbols in the most recent code block period; Historical channel state values ​​of N symbols within a preset period.

7. The method according to any one of claims 1 to 5, characterized in that: The channel state value is a signal to interference plus noise ratio SNR.

8. The method according to any one of claims 1 to 5, characterized in that: The method of predicting channel state information based on the first channel state value sequence and second channel state value sequences at multiple historical moments to obtain a predicted channel state value sequence at a current moment includes: Performing stationary processing on the first channel state value sequences respectively to obtain corresponding stationary time sequences; Based on the autocorrelation coefficient and partial autocorrelation coefficient of the stationary time series, determining the autoregressive hierarchy and moving average order corresponding to the current moment; The autoregressive hierarchy and the moving average order are used as parameters of the autoregressive integrated moving average model, and autoregressive integrated moving average processing is performed on the stationary time series corresponding to the first channel state value sequence and the stationary time series of the second channel state value sequences at multiple historical moments to obtain the predicted channel state value sequence.

9. An adaptive coding modulation method, characterized in that: Applied to a signal transmitting end in a satellite communication system, the method comprises: Receive a predicted channel state value sequence sent by a signal receiving end; wherein the predicted channel state value sequence is predicted by the signal receiving end based on a channel state value sequence obtained by multiple historical channel state estimations; Determining a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and a reference channel state threshold; After modulating and encoding the signal to be sent based on the target MCS mode, the processed signal is sent to the signal receiving end.

10. The method according to claim 9, characterized in that Before determining a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and the reference channel state threshold, the method further includes: receiving a reference state correction factor sent by the signal receiving end, and correcting the reference channel state threshold based on the reference state correction factor to obtain a corrected reference channel state threshold; Based on the predicted channel state value sequence and the reference channel state threshold, a target MCS mode is determined from a plurality of preset MCS modes, including: Based on the predicted channel state value sequence and the modified reference channel state threshold, a target MCS mode is determined from a plurality of preset MCS modes.

11. An adaptive coding modulation device, characterized in that: Applicable to a signal receiving end in a satellite communication system, the device comprises: A channel state estimation unit, configured to perform channel state estimation on a signal sent by a signal transmitting end, and obtain a first channel state value sequence at a current moment; wherein the first channel state value sequence includes a plurality of channel state values ​​estimated for a plurality of symbols of the signal; A channel state prediction unit, configured to predict channel state information based on the first channel state value sequence and second channel state value sequences at multiple historical moments, to obtain a predicted channel state value sequence at a current moment; The sending unit is used to feed back the predicted channel state value sequence to the signal sending end, so that the signal sending end determines the modulation and coding strategy MCS mode based on the predicted channel state value sequence.

12. An adaptive coding modulation device, characterized in that: Applicable to a signal transmitting end in a satellite communication system, the device comprises: A receiving unit, configured to receive a predicted channel state value sequence sent by a signal receiving end; wherein the predicted channel state value sequence is predicted by the signal receiving end based on a channel state value sequence obtained by multiple historical channel state estimations; An MCS mode selection unit, configured to determine a target MCS mode from a plurality of preset MCS modes based on the predicted channel state value sequence and a reference channel state threshold; The sending unit is used to modulate and encode the signal to be sent based on the target MCS mode, and then send the processed signal to the signal receiving end.

13. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 or 9 to 10 are implemented.

14. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 or 9 to 10 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 or 9 to 10 are implemented.

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