Method and apparatus for adaptive modulation and coding based on channel quality and traffic awareness

Abstract

The application provides a channel quality and service sensing based adaptive coding modulation method and device, and relates to the technical field of wireless communication link adaptation. In the method, a channel quality parameter and a service parameter are acquired; the channel quality parameter is predicted to obtain an initial prediction value of the channel quality and estimate a prediction confidence interval; in the case that the interval width exceeds a preset switching threshold, a hierarchical superposition transmission mode is switched to, the number of superposition layers corresponding to the hierarchical superposition transmission mode is determined according to the interval width, a modulation and coding scheme with the number of layers equal to the number of superposition layers is arranged between the lower limit and the upper limit according to a preset gear interval; in the hierarchical superposition transmission mode, the total power is allocated between the basic layer and the enhanced layer to obtain an initial power allocation result; the initial power allocation result is corrected according to the service parameter to obtain a corrected power allocation result, and the basic layer and the enhanced layer corresponding to the power correction allocation are superposed and then transmitted.

Description

Technical Field

[0001] This application relates to the field of adaptive wireless communication link technology, specifically to an adaptive coding and modulation method and device based on channel quality and service awareness. Background Technology

[0002] With the rapid development of wireless communication services, wireless links need to carry an increasing variety of data services with increasingly diverse requirements in time-varying, multipath-fading channel environments. To maximize transmission efficiency with limited spectrum and power resources, adaptive coding and modulation techniques are widely used. The basic principle is as follows: the receiver measures the channel quality and feeds the results back to the transmitter. The transmitter then selects the appropriate modulation and coding scheme based on this. When the channel quality is good, higher-order modulation and higher code rates are used to increase the transmission rate; when the channel quality is poor, lower-order modulation and lower code rates are used to ensure transmission reliability.

[0003] In related technologies, adaptive coding and modulation schemes typically involve the receiver periodically measuring channel quality parameters and feeding them back to the transmitter. The transmitter then compares the measured channel quality parameters with a pre-set threshold table, selects a single modulation and coding scheme that matches the current channel quality, encodes and modulates the data to be transmitted, and sends it. This approach can achieve good results in scenarios where the channel quality is relatively stable.

[0004] However, since channel quality measurement, feedback, and transmission processing all require time, the channel quality parameters used by the transmitter to select a modulation and coding scheme are often measurements from a past moment. When the channel is in a rapidly changing state, there may be a significant deviation between the channel quality parameters used by the transmitter and the actual channel quality at the time of data transmission, leading to a mismatch between the selected modulation and coding scheme and the actual channel conditions. If the selected scheme has a high order, the receiver will be unable to demodulate correctly due to channel degradation, resulting in transmission failure and retransmission overhead; if the selected scheme has a low order, the available transmission capacity will be wasted when the channel is actually good, reducing transmission efficiency. Summary of the Invention

[0005] This application provides an adaptive coding and modulation method and device based on channel quality and service awareness, which can ensure the transmission quality of services with high reliability requirements and improve the overall transmission efficiency in scenarios with rapidly changing channels and different service needs.

[0006] The technical solution of this application embodiment is as follows: In a first aspect, embodiments of this application provide an adaptive coding and modulation method based on channel quality and service awareness, the method comprising: The receiver periodically measures the channel quality parameters of the wireless communication link and the transmitter obtains the service parameters of the service to be transmitted. The channel quality parameters are predicted to obtain initial predicted values ​​of the channel quality, and the prediction confidence interval of the initial predicted values ​​is estimated. The prediction confidence interval is the width of the interval divided by the upper and lower bounds. When the interval width exceeds a preset switching threshold, the system switches to a layered overlay transmission mode. The number of overlay layers corresponding to the layered overlay transmission mode is determined according to the interval width. A modulation and coding scheme with a number of layers equal to the number of overlay layers is formed between the lower boundary and the upper boundary according to a preset gear interval. The modulation and coding scheme corresponding to the lower boundary constitutes the layer protection layer, and the modulation and coding scheme above the layer protection layer constitutes the enhancement layer. In the layered overlay transmission mode, the total power is distributed between the bottom layer and the enhancement layer, so that the power proportion of the bottom layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer is distributed in descending order from low to high according to the layer sequence to obtain the initial power allocation result; The initial power allocation result is corrected according to the service parameters to obtain the corrected power allocation result. The corresponding protection layer and enhancement layer after power correction are superimposed and then sent.

[0007] In the above technical solution, the channel quality parameters fed back from the receiver are predicted, and their prediction confidence interval is estimated. The width of the prediction confidence interval characterizes the uncertainty of the channel quality prediction. When the interval width exceeds the switching threshold, i.e., when the channel prediction uncertainty is large, the system switches to a layered overlay transmission mode. The number of overlay layers is determined according to the interval width, and corresponding modulation and coding schemes are arranged so that different modulation and coding schemes constitute the base layer and the enhancement layer, respectively. This ensures basic transmission reliability with the base layer and strives for higher transmission efficiency with the enhancement layer. Furthermore, when allocating power between the base layer and the enhancement layer, the power proportion of the base layer increases with the increase of the interval width, and the initial power allocation result is corrected in combination with the service parameters of the service to be transmitted, so that the power allocation is adapted to both the uncertainty of channel prediction and the differentiated needs of services. Thus, in scenarios where rapid channel changes lead to outdated feedback and the needs of the services to be transmitted vary, the system can balance transmission reliability and transmission efficiency, achieving the effect of improving overall transmission efficiency while ensuring the transmission quality of services with high reliability requirements.

[0008] Secondly, embodiments of this application provide an adaptive coding and modulation apparatus based on channel quality and service awareness, the apparatus comprising: The parameter acquisition module is used to acquire the channel quality parameters of the wireless communication link periodically measured by the receiver and the service parameters of the service to be transmitted acquired by the transmitter. The prediction estimation module is used to predict the channel quality parameters, obtain an initial predicted value of the channel quality, and estimate the prediction confidence interval of the initial predicted value, wherein the prediction confidence interval is the width of the interval divided by an upper bound and a lower bound. The mode determination module is used to switch to a layered overlay transmission mode when the interval width exceeds a preset switching threshold. It determines the number of overlay layers corresponding to the layered overlay transmission mode according to the interval width, and forms a modulation and coding scheme with a number of layers equal to the number of overlay layers arranged at preset gear intervals between the lower boundary and the upper boundary. The modulation and coding scheme corresponding to the lower boundary constitutes the layer protection layer, and the modulation and coding scheme above the layer protection layer constitutes the enhancement layer. The power allocation module is used to allocate the total power between the bottom layer and the enhancement layer in the layered overlay transmission mode, so that the power ratio of the bottom layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer is allocated in descending order from low to high according to the layer sequence to obtain the initial power allocation result. The power adjustment and transmission module is used to correct the initial power allocation result according to the service parameters, obtain the corrected power allocation result, and then superimpose the corresponding protection layer and the enhancement layer after power correction and transmission.

[0009] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, a user interface, a communication bus, and a network interface. The processor, the memory, the user interface, and the network interface are respectively connected to the communication bus. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any one of the first aspects.

[0010] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed, perform the method described in any one of the methods provided in the first aspect above.

[0011] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By predicting the channel quality parameters fed back from the receiver and estimating their prediction confidence interval, the width of the prediction confidence interval characterizes the uncertainty of channel quality prediction. When the interval width exceeds the switching threshold, indicating significant channel prediction uncertainty, the system switches to a layered overlay transmission mode. The number of overlay layers and the corresponding modulation and coding schemes are determined based on the interval width, allowing different modulation and coding schemes to form a base layer and an enhancement layer, respectively. This ensures basic transmission reliability through the base layer and strives for higher transmission efficiency through the enhancement layer. Furthermore, when allocating power between the base layer and the enhancement layer, the power proportion of the base layer increases with the interval width, and the initial power allocation result is corrected based on the service parameters of the service to be transmitted, ensuring that the power allocation adapts to both channel prediction uncertainty and differentiated service requirements. Therefore, in scenarios where rapid channel changes lead to outdated feedback and the requirements of the services to be transmitted vary, the system can balance transmission reliability and transmission efficiency, achieving the effect of improving overall transmission efficiency while ensuring the transmission quality of services with high reliability requirements. Therefore, it effectively solves the problem in related technologies where the selected modulation and coding scheme does not match the real channel due to outdated channel quality feedback, making it difficult to balance transmission reliability and transmission efficiency. Attached Figure Description

[0012] Figure 1 This is a flowchart illustrating an embodiment of the adaptive coding and modulation method based on channel quality and service awareness provided in this application. Figure 2 This is a schematic diagram of a layered superimposed signal of an adaptive coding and modulation method based on channel quality and service awareness provided in one embodiment of this application; Figure 3 This is a schematic diagram of the module structure of an adaptive coding and modulation apparatus based on channel quality and service awareness provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0014] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0015] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0016] In related technologies, adaptive coding and modulation schemes typically rely solely on channel quality, failing to adequately consider the differentiated requirements of various services in terms of latency and reliability. Applying the same processing method to different services under the same channel conditions makes it difficult to balance ensuring transmission quality for services with high reliability requirements with meeting the efficiency requirements of services prioritizing throughput, leading to a mismatch between resource allocation and service demands.

[0017] Based on this, embodiments of this application provide an adaptive coding and modulation method, apparatus, electronic device, and readable storage medium based on channel quality and service awareness. This adaptive coding and modulation method predicts the channel quality parameters fed back from the receiver and estimates their prediction confidence interval. The width of the prediction confidence interval characterizes the degree of uncertainty in channel quality prediction. When the interval width exceeds a switching threshold, i.e., when the channel prediction uncertainty is large, the method switches to a layered overlay transmission mode. The number of overlay layers is determined according to the interval width, and corresponding modulation and coding schemes are arranged, so that different modulation and coding schemes constitute a base layer and an enhancement layer, respectively. This ensures basic transmission reliability through the base layer and strives for higher transmission efficiency through the enhancement layer. Furthermore, when allocating power between the base layer and the enhancement layer, the power proportion of the base layer increases with the increase of the interval width, and the initial power allocation result is corrected in conjunction with the service parameters of the service to be transmitted, so that the power allocation is adapted to both channel prediction uncertainty and differentiated service requirements. Therefore, in scenarios where rapid channel changes lead to outdated feedback and the needs of services to be transmitted vary, it can balance transmission reliability and transmission efficiency, achieving the effect of improving overall transmission efficiency while ensuring the transmission quality of services with high reliability requirements.

[0018] It should be noted that this adaptive coding and modulation method based on channel quality and service awareness is applied to the transmitter in a wireless communication system and is suitable for high-dynamic scenarios such as inter-satellite communication, UAV telemetry and control, and high-speed railway communication. In these scenarios, the propagation environment of the wireless channel changes rapidly with the relative motion between the transmitter and receiver. There is a time difference between the measured value of the channel quality parameters and the actual channel quality at the actual time of data transmission. This can lead to a mismatch between the selected modulation and coding scheme and the actual channel conditions when selecting a scheme based on outdated feedback information. This application addresses this issue by introducing channel quality prediction and prediction confidence interval estimation, a hierarchical overlay transmission mode, and a service-aware power allocation correction mechanism. This approach balances transmission reliability and efficiency in scenarios where rapid channel changes lead to outdated feedback and the needs of the transmitted services vary.

[0019] The technical solutions provided in the embodiments of this application will be further described below with reference to the accompanying drawings.

[0020] Reference Figure 1 , Figure 1 This is a flowchart illustrating the adaptive coding and modulation method based on channel quality and service awareness provided in this application embodiment. The adaptive coding and modulation method based on channel quality and service awareness is applied to an adaptive coding and modulation device based on channel quality and service awareness, and is located at the transmitting end. The method is executed by a processor in an electronic device or a readable storage medium. The adaptive coding and modulation method based on channel quality and service awareness includes steps S100, S200, S300, S400, and S500.

[0021] Step S100: Obtain the channel quality parameters of the wireless communication link periodically measured by the receiver and the service parameters of the service to be transmitted obtained by the transmitter.

[0022] In one embodiment, channel quality parameters refer to parameter values ​​obtained by the receiver after quantizing and measuring the propagation characteristics of the current wireless communication link at a preset period. These parameters characterize the transmission quality of the wireless channel between the transmitter and receiver at a certain moment. Channel quality parameters include, but are not limited to, one or more combinations of the following metrics: Signal-to-Noise Ratio (SNR), representing the ratio of the useful signal power to the noise power received by the receiver, expressed in decibels (dB); Carrier-to-Interference Ratio (C / I), representing the ratio of the useful carrier signal power to the interference signal power at the receiver, reflecting the channel quality in the presence of co-channel or adjacent-channel interference; Reference Signal Received Power (RSRP), representing the average value of the reference signal power measured by the receiver on a specific resource particle, expressed in dBm, used to measure the large-scale path loss and coverage quality of the channel; and Block Error Rate (BLER), representing the percentage of erroneously received transmission blocks by the receiver within a certain observation period, directly reflecting the transmission reliability of the current modulation and coding scheme under actual channel conditions. The aforementioned channel quality parameters characterize channel transmission capabilities from different dimensions. In practical applications, a single parameter or a combination of multiple parameters can be selected for channel quality assessment based on the scenario requirements.

[0023] The transmitter sends a known reference signal (also called a pilot signal) to the receiver at preset time intervals. After receiving the reference signal, the receiver performs correlation operations between the received reference signal and the known reference signal sequence stored locally to estimate various parameters of the channel. Then, based on the ratio of the power of the useful signal component to the power of the noise component in the received signal, the SNR value at the current moment is calculated. At the same time, the C / I value is calculated based on the ratio of the reference signal power to the detected interference signal power. The RSRP value is calculated by linearly averaging the received power on the resource particle where the reference signal is located. The BLER value is obtained by statistically calculating the decoding results of the transport block within the most recent statistical window. The receiving end repeats the above process according to a preset measurement period (e.g., a measurement is performed every 5 milliseconds) to obtain a series of channel quality parameter measurement values ​​arranged in chronological order. In this embodiment, these are uniformly represented in the form of Channel Quality Indicator (CQI) as {CQI(t1), CQI(t2), ..., CQI(tn)}, where CQI(ti) represents the comprehensive channel quality level value corresponding to the i-th measurement time ti. This level value is obtained by converting the original measurement values ​​such as SNR, C / I, RSRP, and BLER into a unified integer level index (e.g., CQI levels from 0 to 15) according to a preset mapping relationship. N represents the number of measurement samples that have been accumulated so far. The receiving end transmits the measured CQI sequence to the transmitting end through the feedback channel.

[0024] The pre-defined mapping relationship refers to the rules established and stored at the receiver by the system beforehand, used to convert the original channel quality metrics (physical quantities such as SNR, C / I, RSRP, BLER, etc.) into a unified integer level index (CQI level). Since the original channel quality metrics are continuous physical quantities with different dimensions and value ranges, directly using multiple heterogeneous physical quantities as inputs for subsequent channel prediction and MCS selection increases processing complexity and lacks a unified comparison benchmark. The pre-defined mapping relationship converts these heterogeneous original measurements into a unified-scale integer level index (e.g., 16 CQI levels from 0 to 15), so that subsequent steps can process them in a unified numerical form. Specifically, through link-level simulation or actual link testing, various channel conditions are traversed in a controlled channel environment, recording the correspondence between the original channel quality metrics under each channel condition and the highest transmission rate that can be supported under that channel condition (i.e., the spectral efficiency corresponding to the MCS scheme with the highest spectral efficiency under the target BLER constraint). Based on this correspondence, the entire channel quality range is divided into several consecutive intervals, each interval corresponding to an integer level index value. Channel conditions within the same interval can support the same MCS scheme. The corresponding index value is obtained using the interval in which the channel quality parameter is located, for subsequent calculations.

[0025] Service parameters refer to the set of parameters obtained by the transmitting end from the upper-layer protocol stack or service scheduling module, describing the service quality requirements of the data service to be transmitted. These parameters characterize the differentiated requirements of different service types in terms of latency and reliability. Service parameters include at least two dimensions: latency sensitivity level and bit error rate requirement level. The latency sensitivity level is a quantitative classification index of the tolerance of the service to be transmitted for end-to-end transmission latency. It uses a discrete level classification method, dividing latency sensitivity into multiple levels (e.g., high, medium, and low, corresponding to values ​​of 1.0, 0.5, and 0.2 respectively). A higher level indicates a more sensitive service to latency and a lower intolerance for increases in transmission latency (e.g., high levels correspond to real-time voice calls, remote surgical control, and drone flight control commands). A lower level indicates a higher tolerance for latency (e.g., low levels correspond to file downloads, software updates, and batch sensor data transmission). Bit error rate (BER) requirement level refers to a classification index of the maximum allowable block error rate (target BLER) for the transmitted service. It uses a discrete level classification method, dividing the BER requirement into multiple levels (e.g., three levels corresponding to target BLERs of 1%, 5%, and 10%). A smaller target BLER indicates a higher requirement for transmission reliability. The latency sensitivity level and BER requirement level are obtained as follows: the transmitting end reads the service type identifier (such as QoS ClassIdentifier, QCI) carried in the data packet to be transmitted from the upper-layer protocol stack, and then looks up the corresponding information in a pre-configured table mapping service type to latency sensitivity level and BER requirement level.

[0026] By simultaneously acquiring two dimensions of input information—including multi-dimensional channel quality parameters such as SNR, C / I, RSRP, and BLER, and service type parameters including delay sensitivity level and bit error rate requirement level—a comprehensive data foundation is provided for subsequent steps in building channel prediction models and executing service-differentiated modulation and coding scheme decisions. This allows the selection of modulation and coding schemes to no longer rely solely on channel quality, but to comprehensively consider both channel prediction results and service-differentiated requirements for dual decision-making. This lays the foundation for achieving a fine balance between reliability and efficiency when channel prediction uncertainty is high.

[0027] Step S200: Predict the channel quality parameters to obtain the initial predicted value of the channel quality, and estimate the prediction confidence interval of the initial predicted value. The prediction confidence interval is the interval width divided by the upper and lower bounds.

[0028] In one embodiment, before predicting channel quality parameters, a channel prediction model is constructed to extrapolate future channel quality based on historical channel quality observation sequences. This ensures that the channel quality parameters selected by the transmitter based on the chosen modulation and coding scheme are as close as possible to the actual channel state at the time of data transmission. The channel prediction model employs an autoregressive model (which can be a transform-based deep learning model) or a Kalman filter. For example, using a Kalman filter, the channel quality parameters are modeled as a state-space model, constructing state transition equations and observation equations. Based on the channel quality parameters, the Kalman filter, through alternating prediction (time update) and correction (measurement update) steps, simultaneously outputs a predicted state value and a prediction error covariance matrix. The predicted state value serves as the initial predicted channel quality value, and the covariance matrix directly provides quantitative information on prediction uncertainty, enabling adaptive tracking of non-stationary channel changes and making it suitable for scenarios with time-varying channel change rates.

[0029] The prediction confidence interval refers to the probability interval within which the true channel quality value falls under a given confidence probability. It is defined by two numerical values: an upper bound and a lower bound. The upper bound represents the highest possible channel quality value under that confidence probability, while the lower bound represents the lowest possible channel quality value. When using a Kalman filter, the prediction error variance is directly obtained from the diagonal elements of the prediction error covariance matrix. The interval width W is the difference between the upper and lower bounds of the prediction confidence interval, representing the range of fluctuation of the true channel quality value under a given confidence probability. It directly reflects the degree of uncertainty in channel quality prediction. A larger interval width W indicates a larger prediction error variance, suggesting the channel is in a rapidly changing or severely multipath-faded state, making the prediction of future channel quality less reliable. A smaller interval width W indicates a smaller prediction error variance, a relatively stable channel, and higher reliability of the prediction result.

[0030] The above process uses historical CQI sequences as input to predict channel quality at the time after compensating for feedback delay, effectively solving the problem of outdated channel quality information caused by measurement, feedback, and processing delays. Simultaneously, by estimating the prediction confidence interval, not only is a point estimate of the channel quality at the future transmission time obtained, but the uncertainty of this estimate is also quantified. This uncertainty quantification provides crucial criterion information for performing dual decision-making in subsequent steps.

[0031] Step S300: If the interval width exceeds the preset switching threshold, switch to the layered overlay transmission mode. Determine the number of overlay layers corresponding to the layered overlay transmission mode according to the interval width, and form a modulation and coding scheme with a number of layers equal to the number of overlay layers arranged at preset gear intervals between the lower and upper boundaries. The modulation and coding scheme corresponding to the lower boundary constitutes the layer protection layer, and the modulation and coding scheme above the layer protection layer constitutes the enhancement layer.

[0032] In one embodiment, based on the obtained initial channel quality prediction value, the baseline MCS index that satisfies the target block error rate requirement and has the highest spectral efficiency is queried in the pre-stored MCS mapping table. The MCS mapping table is a lookup table pre-configured and stored at the transmitter. Its structure is a correspondence between CQI levels and MCS indices. Each MCS index corresponds to a specific modulation scheme (such as QPSK, 16QAM, 64QAM, 256QAM) and coding rate (such as 1 / 2, 2 / 3, 3 / 4, 5 / 6). The target BLER is the upper limit of the block error rate set by the system according to the service bit error rate requirement level, typically ranging from 1% to 10%. That is, in the MCS mapping table, the MCS index corresponding to each CQI level is the scheme with the highest spectral efficiency under the channel conditions of that CQI level, ensuring that the transmission block error rate does not exceed the target BLER. The process of querying the baseline MCS is as follows: using the initial prediction value as the lookup key, performing exact matching or nearest neighbor matching in the MCS mapping table, and returning the corresponding MCS index as the baseline MCS.

[0033] The preset switching threshold is a pre-defined interval width threshold used to determine whether the uncertainty of the current channel prediction is large enough to warrant switching from a single-scheme transmission mode based on a reference MCS to a layered overlay transmission mode. The switching threshold is determined by statistically analyzing the distribution of the prediction confidence interval width under relatively stable channel conditions through link-level simulation or actual measurements, taking the mean plus twice the standard deviation. Simultaneously with determining the reference MCS, the interval width W is compared with the preset switching threshold. When the interval width W is not greater than the switching threshold, it indicates that the channel prediction is relatively reliable, and the transmitter directly uses the reference MCS as the target MCS for single-scheme transmission (subsequently proceeding to step S500 for service differentiation adjustments before direct transmission). When the interval width W is greater than the switching threshold, it indicates that the uncertainty of the channel prediction exceeds the range that a single reference MCS can reliably adapt to, and the transmitter switches to a layered overlay transmission mode.

[0034] Layered overlay transmission mode refers to a transmission method in which the transmitter simultaneously overlays and transmits multiple signal layers encoded by different MCSs on the same time-frequency resources. It is an enhancement and extension of the single-scheme transmission mode based on the reference MCS. In this mode, the modulation and coding scheme determined by the reference MCS is configured through a set of multi-layer MCSs arranged from low to high order. The signals corresponding to each layer of MCS are superimposed in the power domain at the transmitter and transmitted through the same antenna or antenna array. The receiver demodulates the signals of each layer sequentially from low to high order using successive interference cancellation technology. Layered overlay transmission mode uses the low-order MCS layer to ensure basic transmission capability under worst-case predicted channel conditions, while the high-order MCS enhancement layer strives for higher transmission rates when the actual channel is good, thus achieving a balance between reliability and efficiency when the channel quality is uncertain.

[0035] The number of stacking layers corresponding to the layered overlay transmission mode is determined based on the interval width. After determining the number of stacking layers L, L modulation and coding schemes (i.e., L MCS indices) are arranged between the lower and upper bounds of the prediction confidence interval. The arrangement method is as follows: the prediction confidence interval is evenly divided from the lower to the upper bound according to a preset interval, so that the number of intervals obtained is equal to the number of stacking layers L, and each interval corresponds to one MCS index. The preset interval D = W / L, where W is the interval width and L is the number of stacking layers. The signal-to-noise ratio threshold value corresponding to the i-th layer (i = 1, 2, ..., L) is: γi = lower bound + (i-1)·D. For the i-th layer, the MCS index with the highest spectral efficiency under the target BLER constraint and a demodulation threshold not exceeding γi is selected from the MCS mapping table as the modulation and coding scheme for that layer.

[0036] like Figure 2 As shown, the "safeguard layer" refers to the layer corresponding to the lower bound of the predicted confidence interval in the overlay, i.e., layer 1. Its MCS index demodulation threshold is equal to or lower than the signal-to-noise ratio (SNR) value corresponding to the lower bound of the predicted confidence interval. Therefore, it has a high probability of successful demodulation under any real channel conditions within the predicted confidence interval, ensuring that the basic transmission success rate under the worst predicted channel conditions is not lower than the target BLER requirement. The MCS index of the safeguard layer is the baseline MCS corresponding to the lower bound of the predicted confidence interval. The enhancement layers refer to the remaining layers above the safeguard layer, i.e., layers 2 to L. Their MCS index orders increase from low to high, corresponding to higher spectral efficiency but also requiring better channel conditions for successful demodulation. By performing baseline MCS selection and expanding to a layered overlay transmission mode when the channel prediction uncertainty exceeds the threshold, the binary result of either all correct or all incorrect due to selecting a single baseline MCS when the channel changes rapidly is avoided. The bottom layer, based on the MCS corresponding to the lower bound of the predicted confidence interval, ensures the basic transmission reliability that meets the target BLER in the worst case. The enhancement layer, based on a higher-order MCS, provides additional spectral efficiency gain when the channel is actually good, thus achieving the optimal balance between reliability and efficiency under uncertain conditions.

[0037] In one embodiment, the number of overlay layers corresponding to the layered overlay transmission mode is determined based on the interval width, including but not limited to the following steps: Step S310: Determine the predicted probability distribution of channel quality based on the predicted confidence interval, and calculate the information entropy of the predicted probability distribution. The information entropy characterizes the uncertainty of the channel quality prediction.

[0038] Specifically, the prediction probability distribution refers to the probability distribution function of the actual channel quality taking various possible values ​​within the channel quality range defined by the prediction confidence interval. Based on the assumption that the prediction error follows a zero-mean Gaussian distribution, the prediction probability distribution of channel quality is a Gaussian distribution with the initial prediction value as the mean and the standard deviation of the prediction error as the standard deviation. To facilitate subsequent discretization calculations, this continuous Gaussian distribution is discretized within the range from the lower to the upper bound of the prediction confidence interval: the interval [lower bound, upper bound] is divided into Q sub-intervals (Q is 20 in this embodiment), and the center value of each sub-interval is taken as a discrete channel quality level. The corresponding probability is the integral value of the Gaussian probability density function over that sub-interval (i.e., calculated using the trapezoidal numerical integration method). After discretization, the discrete probability distribution {p1, p2, ..., p_Q} is obtained, where pj represents the probability that the actual channel quality falls within the j-th sub-interval, and the sum of the probability values ​​of all sub-intervals is normalized to 1.

[0039] Information entropy is a measure of the degree of uncertainty of a random variable. For a discrete probability distribution, the formula for calculating information entropy is: H = -Σ j=1-Q The information entropy is calculated as pj·log2(pj), where log2 represents the logarithm to the base 2. When all probabilities are uniformly distributed (i.e., each pj = 1 / Q), the information entropy reaches its maximum value, log2(Q), corresponding to the case with the greatest uncertainty in channel quality prediction. When the probabilities are concentrated in a sub-interval (i.e., one pj is close to 1, and the rest are close to 0), the information entropy is close to 0, corresponding to the case with a high degree of certainty in channel quality prediction. Information entropy H, as a scalar measure of channel quality prediction uncertainty, reflects the morphological characteristics of the prediction probability distribution more accurately than the simple interval width W: when the probability distribution is wide but concentrated in the center, the information entropy is lower than that of a uniform distribution, indicating that uncertainty is not equivalent to the degree implied by the interval width itself.

[0040] Information entropy allows for a more refined quantification of the uncertainty in channel quality prediction. Compared to directly using the interval width as a measure of uncertainty, information entropy can distinguish between different prediction probability distributions that are wide but concentrated and wide and uniform. This makes the determination of the number of subsequent stacking layers more reasonable. Specifically, it avoids increasing complexity and power dispersion caused by setting too many stacking layers when the uncertainty is actually low, and configures sufficient stacking layers to cover the various possible values ​​of channel quality when the uncertainty is indeed high.

[0041] Step S320: Obtain the quantization rule from information entropy to the number of superposition layers. The quantization rule indicates that a larger information entropy corresponds to a larger number of superposition layers.

[0042] Specifically, the quantization rule is a pre-configured mapping relationship stored at the transmitter. Its function is to discretize continuous information entropy values ​​into corresponding overlay layer values. The possible range of information entropy H is divided into several continuous sub-intervals, each sub-interval corresponding to an overlay layer value. The quantization rule adopts a uniform quantization method, and the quantization step size is the ratio of the result of subtracting the minimum value from the maximum information entropy to the preset upper limit of the number of layers minus 2. This uniformly maps the information entropy from the minimum to the maximum value to the range of overlay layers from 2 to the upper limit of the number of layers. The larger the information entropy, the more overlay layers are required. That is, more overlay layers are configured when the channel prediction uncertainty is greater, so as to cover a wider range of possible channel quality through more layers of MCS. The upper limit of the number of layers is a pre-set maximum allowable value of the number of overlay layers, used to limit the complexity of the layered overlay transmission mode and the processing burden of successive interference cancellation at the receiver. The upper limit of the number of layers is determined according to the processing capability of the receiver and the maximum allowed number of SIC iterations, typically ranging from 4 to 6 layers. By pre-establishing quantification rules for information entropy to the number of superposition layers, the determination of the number of superposition layers has a clear basis and a deterministic mapping relationship, avoiding the uncertainty of relying on manual experience for parameter tuning. At the same time, the upper limit constraint on the number of layers ensures that the complexity of the system is controllable.

[0043] Step S330: Map the information entropy to the initial number of layers according to the quantization rules, optimize the initial number of layers to obtain the target number of layers, use the target number of layers as the number of superposition layers, and the number of superposition layers shall not exceed the preset upper limit of the number of layers.

[0044] Specifically, according to the quantization rules, the initial number of layers is the result of rounding up the ratio of information entropy to the quantization step size plus 1. This is a coarse-grained quantization result based on information entropy, serving as the starting point for subsequent optimization searches. Optimization is performed based on the initial number of layers to obtain the target number of layers, thus determining the total number of stacking layers, which does not exceed a preset upper limit. By performing optimization searches based on the initial number of layers, the suboptimal layer selection problem that may arise from simply relying on information entropy quantization is overcome. This ensures that the final determined number of stacking layers is the optimal choice that achieves the maximum expected throughput under the current predicted probability distribution and available schemes in the MCS mapping table, thereby achieving the best balance between the complexity and transmission performance of the layered stacking transmission mode.

[0045] In one embodiment, the initial number of layers is optimized to obtain the target number of layers, including but not limited to the following steps: Step S331: Determine multiple candidate layer values ​​within a neighborhood centered on the initial layer number and not exceeding the upper limit of the layer number.

[0046] Specifically, the neighborhood range is a symmetric integer interval [L0-d, L0+d] centered at the initial layer number L0, where d is the neighborhood radius, and d takes the value 1 or 2. Simultaneously, since the layered overlay transmission mode requires at least 2 layers, the lower bound of the neighborhood range is no less than 2, and the upper bound does not exceed the upper limit of the layer number. That is, the set of candidate layer values ​​is {max(2, L0-d), max(2, L0-d)+1, ..., min(upper limit of layer number, L0+d)}. All integer values ​​within the above neighborhood range are considered candidate layer values. For example, when the initial layer number L0=3, the neighborhood radius d=1, and the upper limit of the layer number is 5, the set of candidate layer values ​​is {2, 3, 4}. By limiting the search to a small neighborhood of the initial layer number, it is ensured that a solution better than the initial layer number can be found, while the search space is limited to a small range, making the computational complexity of the optimization process extremely low, which can meet the strict requirements of real-time communication systems for processing latency.

[0047] Step S332: For each candidate layer value, within the prediction confidence interval, configure the corresponding modulation and coding scheme according to the candidate layer value, and calculate the expected throughput under the candidate layer value according to the prediction probability distribution. The expected throughput is obtained by weighting the sum of the rates of each layer according to the probability of successful demodulation, wherein the probability of successful demodulation of each layer is determined by the probability in the prediction probability distribution that the channel quality is not lower than the demodulation threshold corresponding to the candidate layer value.

[0048] Specifically, for candidate layer values, a corresponding number of MCS indices are evenly distributed between the lower and upper bounds of the prediction confidence interval according to the level interval. For example, the signal-to-noise ratio threshold value corresponding to the i-th layer is the lower bound + (i-1)·candidate layer value. According to the MCS mapping table, the MCS index with the highest spectral efficiency under the target BLER constraint, where the demodulation threshold does not exceed the signal-to-noise ratio threshold value, is selected as the scheme for the i-th layer. Each MCS index corresponds to a spectral efficiency value (in bits per symbol or bits per resource particle). This spectral efficiency is determined by the product of the modulation order and the coding rate. For example, the spectral efficiency of 16QAM modulation combined with 3 / 4 code rate coding is log2(16)×3 / 4 = 3 bits per symbol.

[0049] The prerequisite for successful demodulation of each layer is that the actual channel quality at the receiver is not lower than the demodulation threshold corresponding to that layer's scheme. Based on the discrete prediction probability distribution obtained in step S310, the probability of successful demodulation of the i-th layer is the sum of all probabilities that the channel quality is not lower than the demodulation threshold of that layer. Since the demodulation threshold increases from the first layer to the highest layer, the probability of successful demodulation of each layer decreases from the bottom-preserving layer to the highest enhancement layer.

[0050] In successive interference cancellation (SCC) reception, the receiver demodulates sequentially from the lowest layer to the highest enhancement layer. The effective throughput contribution of a given layer requires successful demodulation from the first layer to that layer. Therefore, the expected throughput is the sum of the spectral efficiencies of each layer multiplied by the joint probability of successful demodulation from the first layer to the current layer. This calculation method, by weighting the sum of the spectral efficiencies of each layer according to the joint probability of successful demodulation, accurately reflects the actual average transmission capability of layered superposition transmission under uncertain channel conditions. Since the demodulation threshold of the lowest layer corresponds to the lower bound of the prediction confidence interval, its probability of successful demodulation is close to 97.5% at 95% confidence (corresponding to the one-sided tail probability of a Gaussian distribution), the throughput contribution of the lowest layer has high determinism. By weighting the sum of the spectral efficiencies of each layer according to the joint probability of successful demodulation, the actual average transmission capability of layered superposition transmission under uncertain channel conditions is accurately reflected, thus providing a reliable performance evaluation basis for subsequent selection of the optimal number of layers. When the channel prediction probability distribution is asymmetric, it can automatically sense the direction of probability quality shift and make the selected layer configuration biased towards the direction of probability quality concentration, thereby achieving higher actual throughput than simple layer rules based on symmetry assumptions.

[0051] Step S333: Determine the candidate layer value corresponding to the maximum expected throughput as the target layer number.

[0052] In this step, all candidate layer values ​​are traversed, and the expected throughput corresponding to each candidate layer value is compared. The candidate layer value with the maximum value is taken as the target layer number. When multiple candidate layer values ​​correspond to the same maximum expected throughput, the layer with the smallest value is selected as the target layer number to reduce system implementation complexity and the SIC processing burden at the receiver. The target layer number is determined by the maximum expected throughput criterion, which maximizes the transmission efficiency of the layered overlay transmission mode under the current channel prediction probability distribution. At the same time, since the calculation of the expected throughput already includes the weighted sum of the successful demodulation probability of each layer (i.e., meeting the target BLER requirement), transmission reliability is also taken into account.

[0053] In step S400, under the layered overlay transmission mode, the total power is allocated between the bottom layer and the enhancement layer so that the power ratio of the bottom layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer is allocated in descending order from low to high according to the layer sequence to obtain the initial power allocation result.

[0054] In one embodiment, in the layered overlay transmission mode, after determining the number of overlay layers and the MCS index of each layer, the total transmit power is allocated between the protection layer and each enhancement layer. The total power refers to the maximum transmit power available to the transmitter on the current time-frequency resources, determined according to the linear operating range of the power amplifier and control regulations, and is a known fixed value. The power proportion of the protection layer is the ratio of the power allocated to the protection layer to the total power. The power proportion of the protection layer is determined by a monotonically increasing mapping function with respect to the interval width; that is, as the interval width increases from the switching threshold to the saturation width, the power proportion of the protection layer increases linearly from a preset minimum value to a preset maximum value. When the interval width exceeds the saturation width, the power proportion of the protection layer remains at its maximum value. This mapping function ensures that the greater the channel prediction uncertainty, the more power the protection layer receives, thereby enhancing the signal-to-noise ratio margin of the protection layer under worst-case channel conditions and ensuring that the actual BLER of the protection layer MCS does not exceed the target BLER.

[0055] After determining the power allocation for the lowest enhancement layer, the remaining power is distributed to each enhancement layer. The enhancement layers are allocated power in a proportionally decreasing manner from lowest to highest power, meaning the lowest enhancement layer receives the most power, and the power of each subsequent layer decreases by a fixed multiple of the previous layer's power (0.6). Since lower enhancement layers have lower MCS demodulation thresholds and a higher probability of successful demodulation under the target BLER constraint, allocating more power to them increases their power margin and further improves their success rate. Higher enhancement layers, while having higher spectral efficiency, have lower success rates, and the marginal benefit of allocating too much power to them diminishes.

[0056] The initial power allocation result refers to the set of power values ​​for each layer obtained according to the above mapping function and the proportional decrease method, satisfying that the sum of the power of all layers equals the total power. This result only considers channel prediction uncertainty and does not yet take into account service differentiation requirements; it is used as the initial power allocation result. The linear mapping function of the above process establishes a clear positive correlation between the bottom-layer power ratio and channel prediction uncertainty. During the dynamic change of the interval width, the bottom-layer power ratio can be continuously and smoothly adjusted, avoiding transmission quality jumps caused by abrupt power allocation changes, and maintaining stable transmission performance during the transition period of rapid channel changes.

[0057] Step S500: Correct the initial power allocation result according to the service parameters to obtain the corrected power allocation result, and then send the corresponding protection layer and enhancement layer after power correction allocation.

[0058] In one embodiment, the initial power allocation result only reflects the impact of channel prediction uncertainty on reliability (i.e., the result of the first-level decision), without considering the differentiated requirements of different services in terms of latency sensitivity level and bit error rate requirement level. For services with high reliability requirements (such as remote control commands, which have high latency sensitivity and strict bit error rate requirements), even if the channel prediction uncertainty is the same, more power should be allocated to the protection layer than for throughput-priority services (such as video streams, which have medium latency sensitivity and lenient bit error rate requirements) to shift the selection of the baseline MCS towards a more conservative direction. Conversely, for throughput-priority services, under the same uncertainty, more power should be allocated to the enhancement layer, which is equivalent to shifting the selection of the baseline MCS towards a more aggressive direction to achieve higher spectral efficiency. Therefore, the result of the first-level decision is shifted and corrected according to the service type parameters, and the power configuration of each layer of the target MCS is finally output.

[0059] After obtaining the corrected power allocation results, the transmitter encodes (channel-codes data bits according to the coding rate specified by the MCS index) and modulates (maps the encoded bits to modulation symbols according to the modulation scheme specified by the MCS index) the data to be transmitted according to the corresponding MCS index of each layer, obtaining the modulation symbol sequence of each layer. Then, each layer's modulation symbol sequence is multiplied by the corresponding power scaling factor (the power scaling factor is the square root of the ratio of the allocated power of that layer to the total power) to ensure that the power of each layer's signal meets the corrected power allocation results. Finally, the scaled signals of all layers are superimposed symbol by symbol in the time domain to obtain the superimposed transmitted signal, which is converted into an analog signal by a digital-to-analog converter and transmitted via the radio frequency link. At the same time, the transmitter sends downlink control information to the receiver through the downlink control channel, which includes the MCS index and power allocation ratio information of each layer, instructing the receiver to demodulate and decode according to the corresponding MCS scheme and SIC demodulation order. By adjusting for service differences, the final power allocation strategy can simultaneously respond to changes in both channel prediction uncertainty and service differentiation requirements, achieving the goal of refined resource allocation that ensures the transmission quality of services with high reliability requirements without excessively sacrificing the efficiency of services with throughput priority. By instructing the receiver to demodulate and decode in the corresponding manner through the control channel, consistency in MCS configuration between the transmitter and receiver is ensured.

[0060] In one embodiment, the service parameters include a latency sensitivity parameter and a bit error rate parameter; the initial power allocation result is corrected based on the service parameters to obtain a corrected power allocation result, including but not limited to the following steps: Step S510: The delay sensitivity parameter and the bit error rate parameter are fused to obtain the comprehensive demand weight.

[0061] Specifically, the weighted geometric mean fusion method is used to calculate the overall demand weight, and the formula is expressed as: ω = (D_s) β ·(1 / BER_req) (1-β) / C_norm, D_s are numerical representations of the latency sensitivity level (e.g., 1.0 for high, 0.5 for medium, and 0.2 for low), BER_req is the target BLER value corresponding to the bit error rate requirement level (e.g., 0.01 for strict, 0.05 for medium, and 0.10 for lenient), β is the fusion weight coefficient, set to 0.5, and C_norm is a normalization constant used to normalize the value range of the comprehensive requirement weight ω to between 0 and 1. A higher comprehensive requirement weight ω (closer to 1) indicates a higher reliability requirement for the service to be transmitted—either due to a high latency sensitivity level (not allowing additional latency caused by retransmissions due to transmission failures), a strict bit error rate requirement level (target BLER as low as 1%), or both. A lower comprehensive requirement weight ω (closer to 0) indicates a greater emphasis on transmission efficiency for the service to be transmitted, tolerating a certain degree of transmission failures and retransmissions (target BLER can be relaxed to 10%). By integrating multi-dimensional business type parameters into a single comprehensive demand weight, the input dimensions of subsequent power correction steps are simplified. This allows the MCS offset correction process to rely on only one scalar index for judgment and calculation, reducing implementation complexity while retaining the ability to fully represent differentiated business needs.

[0062] Step S520: Compare the comprehensive demand weight with the preset weight threshold to obtain the comparison result.

[0063] Specifically, the weight threshold is a pre-defined criterion used to classify services to be transmitted into two categories: "high reliability requirements" (corresponding to a conservative shift) and "throughput priority requirements" (corresponding to a radical shift). During system configuration, it is determined based on the statistical characteristics of the various services carried by the system. For example, the comprehensive requirement weights corresponding to all pre-configured service types are calculated and ranked, and the median is taken as the weight threshold, which can be 0.5. The comprehensive requirement weight is compared with the pre-defined weight threshold. The comparison result is a binary judgment. When the comprehensive requirement weight is higher than the weight threshold, the comparison result indicates that the current service to be transmitted is a throughput priority service and should be shifted in the radical direction. When the comprehensive requirement weight is lower than or equal to the weight threshold, the comparison result indicates that the current service to be transmitted is a throughput priority service and should be shifted in the radical direction. By comparing the comprehensive requirement weight with the weight threshold, the reliability requirements of the services to be transmitted are classified, providing a clear basis for determining the direction of MCS offset correction and enabling differentiated power adjustment strategies for different types of services.

[0064] Step S530: When the comparison result indicates that the comprehensive demand weight is higher than the weight threshold, the power correction direction is determined. The power correction direction indicates that the power allocated to the enhancement layer in the initial power allocation result is reduced, and the reduced power is superimposed on the protection layer.

[0065] Specifically, when the current service to be transmitted is indicated to be a high-reliability service, the offset correction direction is determined to be a conservative shift. This means reducing some power from the enhancement layer and transferring it to the safeguard layer, which is equivalent to shifting the offset correction to a lower-order (more reliable) direction based on the baseline MCS selection. This indicates that when the latency sensitivity level of the service to be transmitted is high and the bit error rate requirement is strict, ensuring transmission reliability (ensuring the actual BLER does not exceed the strict target BLER, such as 1%) takes precedence over increasing the transmission rate. Further increasing the safeguard layer power based on the initial power allocation result can improve the signal-to-noise ratio margin of the safeguard layer under worst-case channel conditions, thereby further reducing the actual BLER of the safeguard layer and giving it a larger safety margin under the target BLER constraint. Simultaneously, although reducing power from the enhancement layer will reduce the successful demodulation probability and spectral efficiency of the enhancement layer, for high-reliability services, the stable transmission of the safeguard layer is more important than the rate gain of the enhancement layer. This offset correction is equivalent to shifting the effective target MCS to a lower-order direction in the MCS mapping table by several index bits.

[0066] The power reduction is obtained from the enhancement layer, specifically by reducing power sequentially downwards from the highest enhancement layer. When the service to be transmitted is a high-reliability service and the overall demand weight is significantly higher than the weight threshold, the power of the highest enhancement layer may be completely reduced (i.e., the power of this layer drops to zero, equivalent to canceling the MCS of this layer). In this case, the reduction will continue to the next higher enhancement layer until the cumulative reduction reaches the target power correction amount. For high-reliability services (such as remote control commands, high latency sensitivity level, and target BLER of 1%), the transmission reliability guarantee is further strengthened by shifting towards a conservative direction under the condition of large channel prediction uncertainty. This ensures that even if the actual channel quality drops to the lower bound of the prediction confidence interval or even slightly below the lower bound, the layer still has sufficient power margin to ensure correct demodulation, effectively avoiding transmission failures of high-reliability services and the resulting unacceptable retransmission delays.

[0067] In one embodiment, after comparing the comprehensive demand weight with a preset weight threshold to obtain the comparison result, the adaptive coding and modulation method based on channel quality and service awareness further includes, but is not limited to, the following steps: Step S540: When the comparison result indicates that the overall demand weight is lower than or equal to the weight threshold, the power correction direction is determined. The power correction direction indicates that the power allocated to the bottom layer in the initial power allocation result is reduced, and the reduced power is redistributed to each enhancement layer.

[0068] Specifically, when the comparison result indicates that the current service to be transmitted is a throughput-priority service, the offset correction direction is determined to be an aggressive shift, that is, reducing some power from the baseline protection layer and redistributing it to each enhancement layer. This is equivalent to shifting the offset correction to a higher-order (more efficient) direction based on the baseline MCS selection. This means that when the latency sensitivity level of the service to be transmitted is low and the bit error rate requirement level is lenient (the target BLER can be relaxed to 10%), the service can tolerate a certain degree of transmission failure and retransmission, and values ​​the spectral efficiency obtained when the channel is actually good. Appropriately reducing the baseline protection power and increasing the enhancement layer power based on the initial power allocation result can improve the signal-to-noise ratio margin and successful demodulation probability of the enhancement layer, thereby obtaining higher spectral efficiency when the channel is actually good. Although the reduction of the baseline protection power will slightly reduce its reliability under the worst channel conditions (making the actual BLER of the baseline protection layer close to but still not exceeding the lenient target BLER such as 10%), for throughput-priority services, this small sacrifice in reliability can be compensated by ARQ / HARQ retransmission mechanisms, and the resulting improvement in spectral efficiency is a continuous net benefit.

[0069] The power reduced from the baseline is proportionally allocated to the enhancement layers according to the ratio in the initial power allocation result. This maintains the power reduction relationship between enhancement layers while increasing the overall power level of each enhancement layer to achieve higher spectral efficiency when the channel is actually good. For throughput-priority services (such as file transfer and video streaming, where the target BLER can be relaxed to 10%), a more aggressive offset correction fully utilizes the relatively relaxed reliability requirements of these services. While maintaining basic transmission guarantees (the baseline BLER still does not exceed the relaxed target BLER), higher spectral efficiency is achieved, thereby improving the overall system throughput. The baseline power in the allocation result is reduced accordingly, and the reduced power is redistributed to the various enhancement layers.

[0070] Step S550: Determine the target power correction amount according to the power correction direction, and output the power of each layer after the target power correction amount has been added or subtracted, as the corrected power allocation result.

[0071] Specifically, the target power correction amount is calculated based on the determined power correction direction, and this correction amount is applied to the initial power allocation result, outputting the corrected power values ​​for each layer. When the power correction direction shifts towards a conservative direction, the corrected guaranteed-base power is the initial guaranteed-base power plus the correction amount, and the power of each enhancement layer is reduced accordingly. When the power correction direction shifts towards an aggressive direction, the corrected guaranteed-base power is the initial guaranteed-base power minus the correction amount, and the power of each enhancement layer is increased accordingly. The sum of the power of each layer after correction is still equal to the total power, and the power value of each layer is non-negative. If the power of an enhancement layer is reduced to zero or a negative value during the correction process, the layer is marked as canceled, its MCS index is removed from the transmission scheme, and the power allocation is reset to zero. The final output target MCS layer power configuration is the corrected power allocation result, including the MCS index of each layer and the corresponding transmit power value. By determining the specific target power correction amount and performing addition and subtraction corrections, the complete process of the dual decision-making mechanism is completed. The first decision determines the baseline MCS and initial power allocation, and the second decision performs offset correction based on the service type parameters. Finally, the output is a target MCS power configuration scheme that adapts to both channel prediction uncertainty and service differentiation requirements, ensuring that the corrected power allocation result satisfies both the total power constraint and the non-negative power constraint of each layer.

[0072] In one embodiment, the target power correction amount is determined based on the power correction direction, including but not limited to the following steps: Step S551: Establish a positive correlation mapping function between interval width, comprehensive demand weight and anti-fading power margin factor, wherein the positive correlation mapping function calculates and outputs the anti-fading power margin factor.

[0073] Specifically, the anti-fading power margin factor is a dimensionless scaling factor ranging from 0 to 1, reflecting the strength of the offset correction to be applied under the current channel prediction uncertainty and service demand bias. A larger anti-fading power margin factor indicates that a larger MCS offset correction is needed to cope with higher channel fading risk and more extreme service demand bias; a smaller anti-fading power margin factor indicates that the offset correction can be smaller.

[0074] A positive correlation mapping function is established between interval width, integrated demand weight, and anti-fading power margin factor. The specific form of the positive correlation mapping function is as follows: the anti-fading power margin factor is obtained by multiplying the normalized value of the interval width relative to the handover threshold by a certain power and the absolute distance of the integrated demand weight from the weight threshold by a certain power, followed by normalization. The exponent parameter is always set to 0.5. The positive correlation characteristic of this function is reflected in the fact that the larger the interval width, the larger the anti-fading power margin factor; and the farther the integrated demand weight deviates from the threshold, the larger the anti-fading power margin factor. The coupling of the two factors through a product means that the anti-fading power margin factor will only take a large value when both channel uncertainty and service demand bias are large. When one factor is small, even if the other factor is large, the anti-fading power margin factor will be lowered, thus avoiding excessive MCS offset correction. By fusing information from the two dimensions of interval width and integrated demand weight into a single anti-fading power margin factor, the MCS offset correction strength adapts simultaneously to changes in both channel prediction uncertainty and service differentiation requirements. Compared to calculating the correction amounts of the two factors independently and then superimposing them, the product coupling method can achieve mutual modulation of the two factors.

[0075] Step S552: Obtain the basic correction amount corresponding to the type of service to be transmitted in the preset mapping table.

[0076] In this step, the mapping table is a pre-configured lookup table stored at the transmitter, with a structure that represents a one-to-one correspondence between service type identifiers and basic correction values. The service types in the mapping table are indexed by service type identifiers (such as QCI values) obtained from the upper-layer protocol stack in step S100, and include at least the following categories: real-time voice services (e.g., QCI=1), video streaming services (e.g., QCI=2), remote control command services (e.g., QCI=3), file transfer services (e.g., QCI=6), sensor data acquisition services (e.g., QCI=7), etc. For each service type, based on its typical transmission frame length, maximum allowed retransmission count, target BLER, and target transmission success rate, the minimum power margin required to ensure the transmission quality of the service under various channel conditions is calculated through link-level simulation. This minimum power margin is then used as the basic correction value for that service type.

[0077] The base correction ΔP_base is a power value expressed as a percentage of total power, representing the MCS offset correction reference value that should be applied to this type of service under standard channel uncertainty and standard service requirement bias. The base correction value corresponding to the type of service to be transmitted is retrieved from a preset mapping table. For example, the base correction value for a remote control command service (high latency sensitivity, target BLER of 1%, no retransmissions) is set to 15% of the total power. The service type identifier field (QCI value or equivalent identifier) ​​is extracted from the header of the data packet to be transmitted. Using this service type identifier as an index, an exact match is performed in the mapping table, returning the corresponding base correction value. If no exact match for the service type identifier exists in the mapping table, the base correction value corresponding to the service type closest to this service type in terms of latency sensitivity and bit error rate requirement is used as a substitute. By establishing a correspondence between service types and base correction values ​​through the preset mapping table, different types of services obtain differentiated MCS offset correction reference values. This design enables the offset correction mechanism to differentiate based on the inherent characteristics of different services (such as frame length, retransmission capability, target BLER level, etc.), rather than applying uniform correction parameters to all services, thereby improving the pertinence and effectiveness of service-differentiated adjustments.

[0078] Step S553: ​​Multiply the base correction amount by the anti-fading power margin factor to obtain the target power correction amount.

[0079] Specifically, the base correction ΔP_base is multiplied by the anti-fading power margin factor to obtain the final target power correction, which is the final power adjustment range of the MCS offset correction.

[0080] Since the anti-fading power margin factor ranges from 0 to 1, the target power correction ranges from 0 to the full value of the base correction. When the channel prediction uncertainty is minimal and the service demand is not extreme, the target power correction is close to zero, meaning almost no offset correction is performed, and the baseline MCS configuration is sufficient. When the uncertainty is high and the service demand is highly biased, the target power correction is close to the full value of the base correction, meaning maximum offset correction is performed. By using the anti-fading power margin factor as a scaling factor for the base correction, continuous adaptive adjustment of the target power correction to the channel prediction uncertainty and the degree of service demand bias is achieved. This constrains the dynamic range of the MCS offset correction to [0, ΔP_base]. Even under the most extreme combination of channel and service conditions, the correction will not exceed the safety upper limit (i.e., the base correction) determined in advance through link-level simulation. This avoids the extreme situation where excessive offset correction leads to the power of the protection layer or enhancement layer being reduced to the point of being unable to function properly. It naturally ensures the rationality and stability of the power allocation after correction without the need for additional complex constraint judgment logic.

[0081] In one embodiment, after power correction allocation and before transmitting the superimposed power-preserving layer and enhancement layer, the adaptive coding and modulation method based on channel quality and service awareness further includes a feasibility verification process for the power-preserving layer. The feasibility verification process includes, but is not limited to, the following steps: Step S601: Under the channel conditions corresponding to the lower bound of the predicted confidence interval, a verification is performed to ensure that the power allocated to the underlying layer meets the demodulation threshold corresponding to the modulation and coding scheme.

[0082] Specifically, after the power correction allocation is completed and before actual superposition transmission, a feasibility verification is performed on the power allocation result of the layer-protection scheme to ensure that the layer-protection scheme can be correctly demodulated under the worst-case channel conditions corresponding to the lower bound of the predicted confidence interval. The verification process is as follows: determine the demodulation threshold corresponding to the layer-protection scheme index, then calculate the equivalent signal-to-noise ratio (SNR) generated at the receiver by the power allocated to the layer-protection scheme after correction under the lower bound channel conditions, and finally compare the equivalent SNR with the demodulation threshold. If it is not lower than the demodulation threshold, the verification passes and proceeds to step S602; if it is lower than the demodulation threshold, the verification fails and proceeds to step S603 or S604 for adjustment. By performing a feasibility verification before actual transmission, basic transmission reliability (actual BLER does not exceed the target BLER) is guaranteed under the worst-case predicted channel conditions, providing a final quality assurance mechanism for subsequent secure transmission.

[0083] Step S602: When the required power of the base layer is less than the total power and there is surplus power, the surplus power is allocated to the reinforcement layer.

[0084] In this step, when the verification result shows that the power required for the layer to reach the demodulation threshold under the lower bound channel conditions is less than the total power (i.e., after satisfying the demodulation threshold of the layer's MCS), there is still surplus power available for the enhancement layers), the current modified power allocation scheme is confirmed to be feasible. Each enhancement layer is then configured and transmits normally according to the modified power allocation result. The power required for the layer to maintain reliability refers to the minimum transmit power required to make the received signal-to-noise ratio of the layer to exactly reach the demodulation threshold corresponding to its MCS under the channel conditions corresponding to the lower bound of the prediction confidence interval. When the power allocated to the layer to maintain reliability is corrected, it indicates that the current power allocation scheme satisfies both the reliability requirements of the layer to maintain reliability and reserves effective transmit power for the enhancement layers. The modified power allocation result has completed the power allocation for each layer, and each enhancement layer operates normally according to the modified power allocation result without further adjustment. Under normal circumstances where the verification passes, the rationality of the power allocation is confirmed, allowing the enhancement layers to obtain effective power without affecting the reliability of the layer to maintain reliability, thereby ensuring basic transmission while utilizing the remaining transmission capacity to pursue higher spectral efficiency.

[0085] Step S603: When the difference between the required power of the protection layer and the total power is less than the preset power threshold and there is no remaining power, delete the enhancement layer and concentrate the total power to the protection layer for transmission.

[0086] Specifically, the preset power threshold is a pre-defined minimum power value used to determine whether the remaining power allocated to the enhancement layer is insufficient to support the effective transmission of any enhancement layer MCS. It represents the minimum power required for the lowest-order enhancement layer MCS scheme in the MCS mapping table to operate normally at its demodulation threshold (actual BLER not exceeding the target BLER). When the remaining value after subtracting the power required by the preservation layer from the total power is less than this preset power threshold, even if the enhancement layer receives this remaining power, it cannot achieve the received signal-to-noise ratio (SNR) at the demodulation threshold corresponding to the MCS, inevitably leading to transmission failure (actual BLER far exceeding the target BLER). In this case, retaining the enhancement layer not only fails to provide effective spectral efficiency gain but also generates inter-layer interference in the preservation layer's SIC demodulation process. The preset power threshold is set to 5% of the total power.

[0087] When the demodulation threshold power required by the base layer MCS is close to but does not exceed the total power, making the power available for allocation to the enhancement layers extremely small, all enhancement layers are deleted, and the encoding, modulation, and superposition processes of the enhancement layers are canceled. This results in the transmitted signal containing only the base layer signal, equivalent to exiting the layered superposition transmission mode and returning to the single MCS transmission mode. The minimal power originally allocated to the enhancement layers is incorporated into the base layer, which receives the full total power P_total. The margin of its signal-to-noise ratio at the receiver, exceeding the demodulation threshold, further improves the transmission reliability of the base layer MCS (making the actual BLER much lower than the target BLER). When notifying the receiver via the control channel, only the single MCS index information of the base layer is carried, and the receiver performs standard demodulation and decoding according to this single MCS method.

[0088] When the channel conditions are extremely poor and the total power is barely enough to support the bottom layer MCS, the above process avoids inter-layer interference of the ineffective enhancement layers on the bottom layer by timely deleting the enhancement layers that cannot work effectively and concentrating all the power on the bottom layer. At the same time, it simplifies the demodulation process at the receiver and maximizes the basic transmission reliability under extremely poor channel conditions.

[0089] Step S604: When all the total power is allocated to the layer protection and the power is below the demodulation threshold of the modulation and coding scheme of the layer protection, the modulation and coding scheme of the layer protection is lowered until it can be demodulated.

[0090] Specifically, when even if all the total power is concentrated on the layer-protected layer, the demodulation threshold corresponding to the current scheme index still cannot be reached, the transmitter lowers the demodulation threshold requirement by adjusting the scheme index of the layer-protected layer in the mapping table until correct demodulation can be achieved under the current power and channel conditions. The adjustment process is carried out iteratively, and the MCS indices in the MCS mapping table are arranged from low to high demodulation threshold (corresponding to low to high spectral efficiency). The down-adjustment operation involves shifting the current MCS index of the underlying layer in the MCS mapping table by one or more index bits towards a lower demodulation threshold. Specifically, the following steps are taken: First, the MCS index of the underlying layer is decremented by 1 (i.e., switched to the adjacent lower-order scheme in the MCS mapping table), and the demodulation threshold corresponding to the new MCS index is obtained. Then, the received signal-to-noise ratio (SNR) of the underlying layer under the lower bound channel condition under all total power conditions is recalculated to determine whether it is not lower than the demodulation threshold. If it is satisfied, the down-adjustment stops, and the new MCS index is used as the final MCS index of the underlying layer. The receiver is notified through the control channel to demodulate and decode according to this MCS method. If it is still not satisfied, the MCS index is decremented by 1 again, and the above judgment process is repeated until an MCS index with a demodulation threshold not exceeding the currently achievable received SNR is found.

[0091] If the requirements are still not met after downgrading to the lowest-order scheme index in the mapping table, it is determined that the current channel conditions have exceeded the system's transmission capacity. The transmitter suspends data transmission and waits for the next measurement cycle to re-execute step S200, the channel prediction process. The finally determined scheme index is then notified to the receiver via the control channel for demodulation and decoding in the corresponding manner.

[0092] Lowering the MCS index means reducing the modulation order and / or coding rate, resulting in a decrease in spectral efficiency while preserving the lowest spectral density. For example, lowering it from MCS index 15 (corresponding to 64QAM-3 / 4, spectral efficiency 4.5 bits / symbol) to MCS index 5 (corresponding to QPSK-1 / 2, spectral efficiency 1 bit / symbol). Although the spectral efficiency is significantly reduced, it ensures that data can still be correctly demodulated and recovered under extremely poor channel conditions (the actual BLER does not exceed the target BLER), avoiding transmission interruptions.

[0093] By adjusting the MCS index in the MCS mapping table as a final fallback mechanism, the system ensures that it can maintain basic communication capabilities under any possible channel conditions. This step, together with steps S601 to S603, forms a progressively advancing security chain: first, it checks whether the current MCS power is sufficient; if the power is insufficient, it concentrates the power to the lower level; if the power is still insufficient after concentrating, it ensures successful transmission by lowering the MCS level (sacrificing spectral efficiency for reliability). Thus, under any extreme circumstances, there will be no situation where communication is completely impossible, demonstrating the system's ultimate guarantee of transmission reliability.

[0094] It should be noted that when using a single underlying layer transmission or a single scheme transmission method, the signal of the corresponding modulation and coding scheme is transmitted based on the corrected power allocation result.

[0095] The adaptive coding and modulation method based on channel quality and service awareness provided in this application achieves a balance between transmission reliability and efficiency in highly dynamic scenarios through the synergistic effect of the following dual decision-making mechanisms: The first decision (reference MCS selection and hierarchical expansion) constructs a channel prediction module based on an AR model or Kalman filter, and predicts the channel quality after compensating for feedback delay with historical CQI sequences as input. When the prediction uncertainty is controllable, the reference MCS that meets the target BLER and has the highest spectral efficiency is directly selected from the MCS mapping table based on the predicted value. When the prediction uncertainty exceeds the threshold, the reference MCS is expanded into a multi-layer MCS configuration with hierarchical superposition transmission mode. The second decision (service differentiation adjustment) offsets and corrects the result of the first decision based on the delay sensitivity level and bit error rate requirement level obtained from the upper-layer protocol stack. This shifts the MCS configuration of high reliability services towards a conservative direction and the MCS configuration of throughput-priority services towards an aggressive direction. Finally, the target MCS index and its power configuration are output. The transmitter processes the data according to the configuration and notifies the receiver through the control channel to demodulate and decode in the corresponding manner.

[0096] like Figure 3As shown in the figure, this application embodiment provides an adaptive coding and modulation device 100 based on channel quality and service awareness, which is installed at the transmitting end. The adaptive coding and modulation device 100 based on channel quality and service awareness acquires the channel quality parameters of the wireless communication link periodically measured by the receiving end and the service parameters of the service to be transmitted acquired by the transmitting end through the parameter acquisition module 110; it uses the prediction estimation module 120 to predict the channel quality parameters to obtain the initial predicted value of the channel quality, and estimates the prediction confidence interval of the initial predicted value. The prediction confidence interval is the interval width divided by the upper and lower bounds; the mode determination module 130 switches to the layered overlay transmission mode when the interval width exceeds the preset switching threshold, and determines the layered overlay transmission mode according to the interval width. The number of overlay layers corresponding to the mode forms a modulation and coding scheme with a number of layers equal to the number of overlay layers arranged at preset intervals between the lower and upper boundaries. The modulation and coding scheme corresponding to the lower boundary constitutes the base layer, and the modulation and coding scheme above the base layer constitutes the enhancement layer. In the layered overlay transmission mode, the power allocation module 140 distributes the total power between the base layer and the enhancement layer so that the power proportion of the base layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer decreases from low to high according to the layer sequence to obtain the initial power allocation result. The power adjustment and transmission module 150 corrects the initial power allocation result according to the service parameters to obtain the corrected power allocation result. The base layer and enhancement layer corresponding to the corrected power allocation are overlaid and transmitted.

[0097] It should be noted that the parameter acquisition module 110 is connected to the prediction and estimation module 120, the prediction and estimation module 120 is connected to the mode determination module 130, the mode determination module 130 is connected to the power allocation module 140, and the power allocation module 140 is connected to the power adjustment and transmission module 150. The channel quality and service-aware adaptive coding and modulation apparatus 100 is used to execute the channel quality and service-aware adaptive coding and modulation method.

[0098] It should also be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0099] This application also discloses an electronic device. (See reference...) Figure 4 , Figure 4This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 505, and at least one communication bus 502.

[0100] The communication bus 502 is used to enable communication between these components.

[0101] The user interface 503 may include a display screen and a camera. Optionally, the user interface 503 may also include a standard wired interface and a wireless interface.

[0102] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0103] The processor 501 may include one or more processing cores. The processor 501 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 505, and by calling data stored in memory 505. Optionally, the processor 501 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array. The processor 501 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and Modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 501.

[0104] The memory 505 may include random access memory (RAM) or read-only memory. Optionally, the memory 505 may include a non-transitory computer-readable storage medium. The memory 505 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 505 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 505 may also be at least one storage device located remotely from the aforementioned processor 501. (Refer to...) Figure 4 The memory 505, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program based on an adaptive coding and modulation method that is based on channel quality and service awareness.

[0105] exist Figure 4 In the illustrated electronic device 500, the user interface 503 is mainly used to provide an input interface for the user and acquire user input data; while the processor 501 can be used to call an application program stored in the memory 505 that is a channel quality and service-aware adaptive coding and modulation method. When executed by one or more processors 501, the electronic device 500 performs one or more methods as described in the above embodiments. It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0106] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0107] In the various embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between apparatuses or units may be electrical or other forms.

[0108] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0109] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0110] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0111] The above are merely exemplary embodiments of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will readily conceive of those skilled in the art upon consideration of the specification and the disclosure of practical truths.

[0112] This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. An adaptive coding and modulation method based on channel quality and service awareness, characterized in that, The method includes: The receiver periodically measures the channel quality parameters of the wireless communication link and the transmitter obtains the service parameters of the service to be transmitted. The channel quality parameters are predicted to obtain initial predicted values ​​of the channel quality, and the prediction confidence interval of the initial predicted values ​​is estimated. The prediction confidence interval is the width of the interval divided by the upper and lower bounds. When the interval width exceeds a preset switching threshold, the system switches to a layered overlay transmission mode. The number of overlay layers corresponding to the layered overlay transmission mode is determined according to the interval width. A modulation and coding scheme with a number of layers equal to the number of overlay layers is formed between the lower boundary and the upper boundary according to a preset gear interval. The modulation and coding scheme corresponding to the lower boundary constitutes the layer protection layer, and the modulation and coding scheme above the layer protection layer constitutes the enhancement layer. In the layered overlay transmission mode, the total power is distributed between the bottom layer and the enhancement layer, so that the power proportion of the bottom layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer is distributed in descending order from low to high according to the layer sequence to obtain the initial power allocation result; The initial power allocation result is corrected according to the service parameters to obtain the corrected power allocation result. The corresponding protection layer and enhancement layer after power correction are superimposed and then sent.

2. The method according to claim 1, characterized in that, Determining the number of overlay layers corresponding to the layered overlay transmission mode based on the interval width includes: The predicted probability distribution of channel quality is determined based on the predicted confidence interval, and the information entropy of the predicted probability distribution is calculated, wherein the information entropy characterizes the degree of uncertainty in the channel quality prediction. A quantization rule is obtained to derive the information entropy from the number of stacking layers, wherein the quantization rule indicates that a larger information entropy corresponds to a larger number of stacking layers; The information entropy is mapped to an initial number of layers according to the quantization rules, and the initial number of layers is optimized to obtain a target number of layers. The target number of layers is used as the number of superposition layers, and the number of superposition layers does not exceed a preset upper limit of the number of layers.

3. The method according to claim 2, characterized in that, The optimization of the initial number of layers to obtain the target number of layers includes: Within a neighborhood centered on the initial layer number and not exceeding the upper limit of the layer number, multiple candidate layer values ​​are determined; For each candidate layer value, within the prediction confidence interval, a corresponding modulation and coding scheme is configured according to the candidate layer value, and the expected throughput under the candidate layer value is calculated according to the prediction probability distribution. The expected throughput is obtained by weighting and summing the rates of each layer according to their probability of successful demodulation, wherein the probability of each layer being successfully demodulated is determined by the probability in the prediction probability distribution that the channel quality is not lower than the demodulation threshold corresponding to the candidate layer value. The candidate layer value corresponding to the maximum value of the expected throughput is determined as the target layer number.

4. The method according to claim 1, characterized in that, After power correction allocation and before transmitting the superimposed layer corresponding to the power correction allocation and the enhancement layer, the method further includes a feasibility verification process for the layer, the feasibility verification process including: Under the channel conditions corresponding to the lower bound of the predicted confidence interval, the power allocated to the layer that satisfies the demodulation threshold corresponding to the modulation and coding scheme is verified. When the required power of the protection layer is less than the total power and there is residual power, the residual power is allocated to the reinforcement layer; When the difference between the required power of the protection layer and the total power is less than a preset power threshold, and there is no remaining power, the enhancement layer is deleted, and the total power is concentrated and allocated to the protection layer for transmission. When the total power is allocated entirely to the layer that preserves the power level and is below the demodulation threshold of the modulation and coding scheme of the layer that preserves the power level, the modulation and coding scheme of the layer that preserves the power level is lowered until it can be demodulated.

5. The method according to claim 1, characterized in that, The service parameters include latency sensitivity parameters and bit error rate parameters; The step of correcting the initial power allocation result based on the service parameters to obtain the corrected power allocation result includes: The latency sensitivity parameter and bit error rate parameter are fused to obtain the comprehensive demand weight; The comprehensive demand weight is compared with a preset weight threshold to obtain the comparison result; When the comparison result indicates that the comprehensive demand weight is higher than the weight threshold, a power correction direction is determined. The power correction direction indicates that the power allocated to the enhancement layer in the initial power allocation result is reduced, and the reduced power is added to the protection layer. Based on the power correction direction, a target power correction amount is determined, and each layer of the stacked layers is corrected according to the target power correction amount to obtain the corrected power allocation result.

6. The method according to claim 5, characterized in that, After comparing the comprehensive demand weight with a preset weight threshold to obtain a comparison result, the method further includes: When the comparison result indicates that the overall demand weight is lower than or equal to the weight threshold, a power correction direction is determined. The power correction direction indicates that the power allocated to the base layer in the initial power allocation result is reduced, and the reduced power is redistributed to each of the enhancement layers. Based on the power correction direction, determine the target power correction amount, and output the power of each layer after the target power correction amount has been adjusted by addition or subtraction, as the corrected power allocation result.

7. The method according to claim 5 or 6, characterized in that, Determining the target power correction amount based on the power correction direction includes: Establish a positive correlation mapping function between the interval width, the comprehensive demand weight, and the anti-fading power margin factor, wherein the positive correlation mapping function calculates and outputs the anti-fading power margin factor; Obtain the basic correction amount corresponding to the type of service to be transmitted as described in the preset mapping table; The target power correction is obtained by multiplying the base correction amount by the anti-fading power margin factor.

8. An adaptive coding and modulation apparatus based on channel quality and service awareness, characterized in that, The device includes: The parameter acquisition module is used to acquire the channel quality parameters of the wireless communication link periodically measured by the receiver and the service parameters of the service to be transmitted acquired by the transmitter. The prediction estimation module is used to predict the channel quality parameters, obtain an initial predicted value of the channel quality, and estimate the prediction confidence interval of the initial predicted value, wherein the prediction confidence interval is the interval width divided by an upper bound and a lower bound. The mode determination module is used to switch to a layered overlay transmission mode when the interval width exceeds a preset switching threshold. It determines the number of overlay layers corresponding to the layered overlay transmission mode according to the interval width, and forms a modulation and coding scheme with a number of layers equal to the number of overlay layers arranged at preset gear intervals between the lower boundary and the upper boundary. The modulation and coding scheme corresponding to the lower boundary constitutes the layer protection layer, and the modulation and coding scheme above the layer protection layer constitutes the enhancement layer. The power allocation module is used to allocate the total power between the bottom layer and the enhancement layer in the layered overlay transmission mode, so that the power ratio of the bottom layer increases with the increase of the interval width, and the remaining power corresponding to the enhancement layer is allocated in descending order from low to high according to the layer sequence to obtain the initial power allocation result. The power adjustment and transmission module is used to correct the initial power allocation result according to the service parameters, obtain the corrected power allocation result, and then transmit the corresponding protection layer and enhancement layer after power correction allocation.

9. An electronic device, characterized in that, The device includes a processor, a memory, a user interface, a communication bus, and a network interface. The processor, the memory, the user interface, and the network interface are respectively connected to the communication bus. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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