Transmitting power adjusting method in satellite communication

Abstract

The invention discloses a method for adjusting transmitting power in satellite communication, and belongs to the technical field of satellite communication. The method comprises the following steps: firstly, scanning a communication frequency band and an adjacent frequency band by using spectrum monitoring equipment on a satellite, constructing a plurality of background noise power sequences and extracting background noise features; a corresponding sequence is formed according to the bit error rate and the signal-to-noise ratio of the satellite emission signal, and bit error rate features and signal-to-noise ratio features are extracted; acquiring a demand coefficient according to the newest bit error rate and the difference between the signal-to-noise ratio and the target value; and finally, inputting a plurality of groups of background noise features, bit error rate features, signal-to-noise ratio features and demand coefficients as samples into the trained full connection layer-BP neural network to obtain a transmitting power increment, adding the transmitting power increment with the initial transmitting power, and determining the transmitting power of the next signal, thereby effectively improving the transmitting power adjustment precision.

Description

Technical Field

[0001] The present invention relates to the technical field of satellite communication, and particularly to a method for adjusting transmission power in satellite communication. Background Art

[0002] With the increasing congestion of communication spectrum resources, different communication systems coexist within a limited frequency band, and the possibility of signal interference increases significantly. The modern communication network is highly dense and complex, making it extremely difficult to utilize spectrum resources. The growing demands for spectrum from various systems such as mobile communication, broadcasting, and satellite communication have made the problems of frequency congestion and interference more prominent. The traditional fixed-power transmission scheme is difficult to adapt to such a complex communication environment. Some existing methods for adjusting transmission power only adjust the transmission power based on the signal-to-noise ratio at the receiving end. Although the signal quality can be improved to a certain extent, this method does not consider the impact of the real-time spectrum of the frequency band used in satellite communication and its adjacent frequency bands on the current transmitted signal, so there is a problem of low accuracy in adjusting transmission power. Summary of the Invention

[0003] Aiming at the above deficiencies in the prior art, the method for adjusting transmission power in satellite communication provided by the present invention solves the problem of low accuracy in adjusting transmission power existing in the prior art.

[0004] To achieve the above invention object, the technical solution adopted by the present invention is: A method for adjusting transmission power in satellite communication, comprising the following steps:

[0005] Scan the frequency band used in satellite communication and its adjacent frequency bands through the spectrum monitoring device on the satellite to construct multiple background noise power sequences;

[0006] Extract features from each background noise power sequence to obtain a set of background noise features;

[0007] According to the bit error rate and signal-to-noise ratio of each satellite transmitted signal, form a bit error rate sequence and a signal-to-noise ratio sequence, and extract features from the bit error rate sequence and the signal-to-noise ratio sequence respectively to obtain a set of bit error rate features and a set of signal-to-noise ratio features;

[0008] Obtain a demand coefficient according to the gap between the latest bit error rate and the target bit error rate, and the gap between the latest signal-to-noise ratio and the target signal-to-noise ratio;

[0009] Take multiple sets of background noise features, a set of bit error rate features, a set of signal-to-noise ratio features, and the demand coefficient as samples and input them into the trained fully connected layer - BP neural network to obtain the transmission power increment;

[0010] Add the transmission power increment to the initial transmission power to obtain the transmission power of the next signal.

[0011] Further, the process of constructing multiple background noise power sequences includes:

[0012] When the satellite does not transmit signals, the frequency band used for satellite communication is scanned by the spectrum monitoring device on the satellite to obtain the first background signals in each historical time period, calculate the background noise power, and construct the first background noise power sequence;

[0013] When the satellite does not transmit signals, an adjacent frequency band of the frequency band used for satellite communication is scanned by the spectrum monitoring device on the satellite to obtain the second background signals in each historical time period, calculate the background noise power, and construct the second background noise power sequence;

[0014] When the satellite does not transmit signals, another adjacent frequency band of the frequency band used for satellite communication is scanned by the spectrum monitoring device on the satellite to obtain the third background signals in each time period, calculate the background noise power, and construct the third background noise power sequence.

[0015] Further, the formula for calculating the background noise power is: , where P is the background noise power, x n is the nth signal value in the background signals of a time period, N is the length of the background signals of a time period, | | is the absolute value symbol, and n is a positive integer.

[0016] Further, a set of background noise characteristics includes: the background noise power mean and the background noise power increment;

[0017] The background noise power mean is the mean of the background noise power sequence;

[0018] The background noise power increment is the difference between the latest background noise power and the earliest background noise power in the background noise power sequence.

[0019] Further, a set of bit error rate characteristics includes: the bit error rate mean and the bit error rate increment;

[0020] The bit error rate mean is the mean of the bit error rate sequence composed of the bit error rates of the satellite transmitted signals at each historical moment;

[0021] The bit error rate increment is the difference between the latest bit error rate and the earliest bit error rate in the bit error rate sequence;

[0022] A set of signal-to-noise ratio characteristics includes: the signal-to-noise ratio mean and the signal-to-noise ratio increment;

[0023] The signal-to-noise ratio mean is the mean of the signal-to-noise ratio sequence composed of the signal-to-noise ratios of the satellite transmitted signals at each historical moment;

[0024] The signal-to-noise ratio increment is the difference between the latest signal-to-noise ratio and the earliest signal-to-noise ratio in the signal-to-noise ratio sequence.

[0025] Furthermore, the process of calculating the demand coefficient includes:

[0026] Judge whether the latest bit error rate is greater than the target bit error rate. If so, subtract the target bit error rate from the latest bit error rate to obtain the bit error rate difference, and take the ratio of the bit error rate difference to the target bit error rate as the first demand component. If not, assign 0 to the first demand component;

[0027] Judge whether the latest signal-to-noise ratio is less than the target signal-to-noise ratio. If so, subtract the latest signal-to-noise ratio from the target signal-to-noise ratio to obtain the signal-to-noise ratio difference, and take the ratio of the signal-to-noise ratio difference to the target signal-to-noise ratio as the second demand component; if not, assign 0 to the second demand component;

[0028] Add the first demand component and the second demand component to obtain the demand coefficient.

[0029] Furthermore, the fully connected layer - BP neural network includes: the first fully connected layer, the second fully connected layer, the third fully connected layer, the fourth fully connected layer, the fifth fully connected layer, the input layer, the first hidden layer, the second hidden layer and the output layer;

[0030] The input end of the first fully connected layer is used to input the first group of background noise features;

[0031] The input end of the second fully connected layer is used to input the second group of background noise features;

[0032] The input end of the third fully connected layer is used to input the third group of background noise features;

[0033] The input end of the fourth fully connected layer is used to input a group of bit error rate features;

[0034] The input end of the fifth fully connected layer is used to input a group of signal-to-noise ratio features;

[0035] The input layer is used to input the outputs of the first fully connected layer, the second fully connected layer, the third fully connected layer, the fourth fully connected layer, the fifth fully connected layer and the demand coefficient into the input end of the first hidden layer;

[0036] The output end of the first hidden layer is connected to the input end of the second hidden layer;

[0037] The input end of the output layer is connected to the output end of the second hidden layer, and its output end serves as the output end of the fully connected layer - BP neural network.

[0038] Further, the process of training the fully-connected layer - BP neural network includes: a first training stage and a second training stage. The first training stage is used to train the fully-connected layer - BP neural network according to the gap between the predicted transmit power increment of the fully-connected layer - BP neural network and the maximum transmit power increment. The second training stage is used to retrain the fully-connected layer - BP neural network after the first training stage to find the optimal weights and biases corresponding to the minimum transmit power increment.

[0039] Further, the process of the second training stage includes:

[0040] Input the samples of the nth training into the fully-connected layer - BP neural network after the first training stage to obtain the transmit power increment of the nth training, where n is the number of training times in the second training stage;

[0041] When the transmit power increment of the nth training is greater than the maximum transmit power increment, take the maximum transmit power increment as the transmit power increment of the nth training, and add the transmit power increment of the nth training to the initial transmit power to obtain the transmit power of the nth training;

[0042] Use the transmit power of the nth training to transmit the satellite test signal;

[0043] Statistically analyze the bit error rate and signal-to-noise ratio of the satellite test signal received by the receiving end, and calculate the second objective value based on the objective function;

[0044] Update the weights and biases in the fully-connected layer - BP neural network according to the second objective value, increment n by 1, and loop the training process until the number of training times reaches the target number of training times;

[0045] In all trainings of the second training stage, mark the number of training times with a bit error rate less than the target bit error rate and a signal-to-noise ratio greater than the target bit error rate as the pending number of times;

[0046] Among all the pending numbers of times, mark the pending number of times corresponding to the minimum transmit power increment as the target number of times;

[0047] Take the weights and biases corresponding to the target number of times as the optimal parameters, and the training of the fully-connected layer - BP neural network is completed.

[0048] Further, when the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; when the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: ; when the bit error rate is less than or equal to the target bit error rate and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; when the bit error rate is less than or equal to the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: G = c, where Bact The bit error rate of the satellite test signal is B tar The target bit error rate is S act The signal-to-noise ratio of the satellite test signal is S tar The target signal-to-noise ratio is G, G is the second target value, and c is a constant between 0 and 1.

[0049] The beneficial effects of the present invention are as follows:

[0050] 1. The present invention constructs a background noise power sequence by scanning the satellite communication frequency band and adjacent frequency bands, and combines the features extracted from the bit error rate sequence and the signal-to-noise ratio sequence, as well as the demand coefficient obtained according to the gap between the bit error rate, signal-to-noise ratio and the target value, providing rich and comprehensive data samples for the fully connected layer - BP neural network. Compared with the traditional method that only relies on the signal-to-noise ratio at the receiving end, it can calculate the transmit power increment more accurately, thus significantly improving the accuracy of transmit power adjustment and effectively ensuring the stability and accuracy of signal transmission.

[0051] 2. The present invention considers the influence of the real-time spectrum state of the frequency band used for satellite communication and its adjacent frequency bands on the signal, and at the same time, according to the latest gap between the bit error rate and signal-to-noise ratio and the target value, obtains the demand coefficient to characterize the actual demand of the current satellite communication system for transmit power adjustment, improving the prediction accuracy of the fully connected layer - BP neural network. Description of the Drawings

[0052] Figure 1 is a flowchart of a method for adjusting transmit power in satellite communication;

[0053] Figure 2 is a schematic structural diagram of a fully connected layer - BP neural network. Detailed Embodiment

[0054] The following describes the detailed embodiment of the present invention to facilitate those skilled in the art to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the detailed embodiment. For those ordinary skilled in the art in the technical field, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are within the scope of protection.

[0055] As Figure 1 shown, a method for adjusting transmit power in satellite communication includes the following steps:

[0056] Scan the frequency band used for satellite communication and adjacent frequency bands through the spectrum monitoring device on the satellite to construct a plurality of background noise power sequences;

[0057] Extract features from each background noise power sequence to obtain a set of background noise features;

[0058] According to the bit error rate and signal-to-noise ratio of the signals transmitted by each satellite, a bit error rate sequence and a signal-to-noise ratio sequence are formed, and features are extracted from the bit error rate sequence and the signal-to-noise ratio sequence respectively to obtain a set of bit error rate features and a set of signal-to-noise ratio features;

[0059] According to the gap between the latest bit error rate and the target bit error rate, and the gap between the latest signal-to-noise ratio and the target signal-to-noise ratio, a demand coefficient is obtained;

[0060] Taking multiple groups of background noise features, a set of bit error rate features, a set of signal-to-noise ratio features and the demand coefficient as samples, input them into the trained fully connected layer - BP neural network to obtain the transmit power increment;

[0061] Add the transmit power increment to the initial transmit power to obtain the transmit power of the next signal.

[0062] In this embodiment, the initial transmit power is the average daily transmit power of the satellite.

[0063] In this embodiment, the process of constructing multiple background noise power sequences includes:

[0064] When the satellite does not transmit signals, scan the frequency band used for satellite communication through the spectrum monitoring device on the satellite to obtain the first background signals in each historical time period, calculate the background noise power, and construct the first background noise power sequence;

[0065] When the satellite does not transmit signals, scan an adjacent frequency band of the frequency band used for satellite communication through the spectrum monitoring device on the satellite to obtain the second background signals in each historical time period, calculate the background noise power, and construct the second background noise power sequence;

[0066] When the satellite does not transmit signals, scan another adjacent frequency band of the frequency band used for satellite communication through the spectrum monitoring device on the satellite to obtain the third background signals in each time period, calculate the background noise power, and construct the third background noise power sequence.

[0067] The spectrum monitoring device is also called a spectrum monitoring receiver or a spectrum analyzer.

[0068] The commonly used frequency bands for satellite communication, from low to high, include: L band, S band, C band, X band, Ku band, Ka band, V band, W band. If the satellite is going to use the S band to transmit signals, the adjacent frequency bands are the L band and the C band. If the satellite uses the L band to transmit signals, the adjacent frequency band is only the S band, and then a background noise power sequence is filled with 0.

[0069] In this embodiment, the formula for calculating the background noise power is: , where P is the background noise power, xn is the nth signal value in the background signal for a time period, N is the length of the background signal for a time period, || is the absolute value symbol, and n is a positive integer.

[0070] Co-channel interference: When multiple satellite communication systems or terrestrial communication devices operate on the same frequency, co-channel interference will occur. For example, in some popular frequency bands, there may be communication systems of multiple satellite operators operating simultaneously, and the signals between these systems interfere with each other, resulting in a decrease in the signal quality at the receiving end. To overcome co-channel interference, the transmitting end needs to increase the transmission power so that its own signal strength can still meet the demodulation requirements of the receiving end in the interference environment.

[0071] Adjacent-channel interference: Signals of adjacent frequencies interfere with the target signal due to reasons such as spectral leakage. For example, in the case of tight satellite communication frequency band allocation, the interference of adjacent-channel signals cannot be ignored. To reduce the impact of adjacent-channel interference, the transmitting end needs to adjust the transmission power to ensure the communication quality.

[0072] In this embodiment, when a signal needs to be transmitted, the one-hour period before the current moment is equally divided into six 10-minute time periods. In each 10-minute period, the first background signal, the second background signal, and the third background signal are respectively obtained, and the background noise power is calculated to obtain the first background noise power sequence, the second background noise power sequence, and the third background noise power sequence with a length of 6.

[0073] In this embodiment, a set of background noise characteristics includes: the background noise power mean and the background noise power increment;

[0074] The background noise power mean is the mean of the background noise power sequence;

[0075] The background noise power increment is the difference between the latest background noise power and the earliest background noise power in the background noise power sequence.

[0076] The background noise power mean can represent the average situation of the background noise power sequence, and the background noise power increment reflects the change of the background noise power over a period of time. The power increment can sensitively capture this change trend, enabling the fully connected layer - BP neural network to timely understand the dynamic characteristics of the noise.

[0077] In this embodiment, a set of bit error rate characteristics includes: the bit error rate mean and the bit error rate increment;

[0078] The bit error rate mean is the mean of the bit error rate sequence composed of the bit error rates of the satellite transmitted signals at each historical moment;

[0079] The bit error rate increment is the difference between the latest bit error rate and the earliest bit error rate in the bit error rate sequence.

[0080] The mean bit error rate reflects the average level of the bit error rate of satellite transmission signals in a historical period, and the bit error rate increment reflects the change trend of the bit error rate over time, enabling the timely detection of bit error rate fluctuations.

[0081] A set of signal-to-noise ratio characteristics includes: the mean signal-to-noise ratio and the signal-to-noise ratio increment;

[0082] The mean signal-to-noise ratio is the mean of the signal-to-noise ratio sequence composed of the signal-to-noise ratios of satellite transmission signals at each historical moment;

[0083] The signal-to-noise ratio increment is the difference obtained by subtracting the earliest signal-to-noise ratio from the latest signal-to-noise ratio in the signal-to-noise ratio sequence.

[0084] The mean signal-to-noise ratio gives the average quality status of satellite transmission signals at historical moments, and the signal-to-noise ratio increment reflects the change trend of the signal-to-noise ratio.

[0085] In this embodiment, the process of calculating the demand coefficient includes:

[0086] Determine whether the latest bit error rate is greater than the target bit error rate. If so, subtract the target bit error rate from the latest bit error rate to obtain the bit error rate difference, and use the ratio of the bit error rate difference to the target bit error rate as the first demand component. If not, assign a value of 0 to the first demand component;

[0087] Determine whether the latest signal-to-noise ratio is less than the target signal-to-noise ratio. If so, subtract the latest signal-to-noise ratio from the target signal-to-noise ratio to obtain the signal-to-noise ratio difference, and use the ratio of the signal-to-noise ratio difference to the target signal-to-noise ratio as the second demand component; if not, assign a value of 0 to the second demand component;

[0088] Add the first demand component and the second demand component to obtain the demand coefficient.

[0089] Voice communication has a relatively high tolerance for bit error rates, and the target bit error rate is set between 0.001 and 0.01. Data communication has extremely high requirements for accuracy, and the target bit error rate can be set between 0.000001 and 0.000000001. The target signal-to-noise ratio for ordinary voice calls is basically 15 - 25 dB to ensure clear voice signals, and the target signal-to-noise ratio for high-quality voice communication is 25 - 35 dB. For general data transmission (such as web browsing, simple file downloads, etc.): the target signal-to-noise ratio is usually 20 - 30 dB. For critical data transmission (such as financial transaction data, scientific research data transmission, etc.): the target signal-to-noise ratio often requires 25 - 40 dB or even higher.

[0090] The present invention determines whether to calculate the first demand component by judging the magnitude relationship between the latest bit error rate and the target bit error rate. When the latest bit error rate is greater than the target bit error rate, the ratio of the bit error rate difference to the target bit error rate is used as the first demand component to quantify the degree of deviation of the bit error rate from the target value. If the bit error rate exceeds the target value by a large margin, the value of the first demand component is large, indicating an urgent need for adjusting the transmission power due to the bit error rate problem; if it does not exceed the target value, it is assigned a value of 0, indicating that the current bit error rate is within the acceptable range and there is no such power adjustment requirement in this regard.

[0091] For the signal-to-noise ratio, judge the magnitude of the latest value and the target value. If the latest signal-to-noise ratio is less than the target signal-to-noise ratio, subtract the latest signal-to-noise ratio from the target signal-to-noise ratio, and take the ratio of the signal-to-noise ratio difference to the target signal-to-noise ratio as the second demand component to reflect the deviation of the latest signal-to-noise ratio. If the target value is satisfied, it is assigned a value of 0, that is, there is no power adjustment requirement driven by this factor.

[0092] Adding the first demand component and the second demand component to obtain the demand coefficient realizes the comprehensive consideration of the adjustment demand of the satellite communication system for the transmission power from the perspectives of two key signal quality indicators, namely the bit error rate and the signal-to-noise ratio.

[0093] As Figure 2 shown, the fully connected layer - BP neural network includes: the first fully connected layer, the second fully connected layer, the third fully connected layer, the fourth fully connected layer, the fifth fully connected layer, the input layer, the first hidden layer, the second hidden layer, and the output layer;

[0094] The input end of the first fully connected layer is used to input the first group of background noise features;

[0095] The input end of the second fully connected layer is used to input the second group of background noise features;

[0096] The input end of the third fully connected layer is used to input the third group of background noise features;

[0097] The input end of the fourth fully connected layer is used to input a group of bit error rate features;

[0098] The input end of the fifth fully connected layer is used to input a group of signal-to-noise ratio features;

[0099] The input layer is used to input the outputs of the first fully connected layer, the second fully connected layer, the third fully connected layer, the fourth fully connected layer, the fifth fully connected layer, and the demand coefficient into the input end of the first hidden layer;

[0100] The output end of the first hidden layer is connected to the input end of the second hidden layer;

[0101] The input end of the output layer is connected to the output end of the second hidden layer, and its output end serves as the output end of the fully connected layer - BP neural network.

[0102] In this embodiment, the expressions of the fully connected layers are all: y = f(ω x1 x1 + b1) + f(ω x2 x2 + b2), where y is the output of the fully connected layer, x1 is the mean value, x2 is the increment, ω x1 is the weight of x1, b x1 is the bias of x1, ω x2 is the weight of x2, b x2 is the bias of x2, and f is the activation function.

[0103] As Figure 2 shown, there are 6 nodes in the input layer, the first hidden layer, and the second hidden layer of the fully connected layer - BP neural network.

[0104] The present invention performs feature fusion through each fully connected layer, and then inputs the first, second, third, fourth, and fifth fully connected layers and the demand coefficient into the BP neural network to achieve the improvement of the accuracy of predicting the transmit power increment through the combination of multi-dimensional features.

[0105] In this embodiment, the fully connected layer - BP neural network can be trained by the existing gradient descent method. More preferably, the process of training the fully connected layer - BP neural network includes: a first training stage and a second training stage. The first training stage is used to train the fully connected layer - BP neural network according to the gap between the transmit power increment predicted by the fully connected layer - BP neural network and the maximum transmit power increment; the second training stage is used to retrain the fully connected layer - BP neural network after the first training stage to find the optimal weights and biases corresponding to the minimum transmit power increment.

[0106] The process of the first training stage includes:

[0107] Input the i-th sample into the fully connected layer - BP neural network to obtain the transmit power increment of the i-th training, where i is the number of training times in the first training stage;

[0108] Take the absolute value of the difference between the transmit power increment of the i-th training and the maximum transmit power increment as the first target value;

[0109] Update the weights and biases in the fully connected layer - BP neural network according to the first target value, increment i by 1, and loop the training process until the first target value is less than the target threshold, and the first stage training of the fully connected layer - BP neural network is completed.

[0110] In this embodiment, the specific value of the target threshold can be set according to requirements. When higher training accuracy is required, the target threshold can be set to approach 0, so that the predicted transmit power increment is extremely close to the maximum transmit power increment.

[0111] The process of the second training stage includes:

[0112] Input the samples of the nth training into the fully connected layer - BP neural network after the first training stage (i.e., use the weights and parameters of the first training stage as the starting values of the second training stage) to obtain the transmit power increment of the nth training, where n is the number of training times in the second training stage;

[0113] When the transmit power increment of the nth training is greater than the maximum transmit power increment, take the maximum transmit power increment as the transmit power increment of the nth training, and add the transmit power increment of the nth training to the initial transmit power to obtain the transmit power of the nth training;

[0114] Use the transmit power of the nth training to transmit the satellite test signal;

[0115] Statistically calculate the bit error rate and signal - to - noise ratio of the satellite test signal received at the receiving end, and calculate the second objective value based on the objective function;

[0116] Update the weights and biases in the fully connected layer - BP neural network according to the second objective value, increment n by 1, and loop the training process until the number of training times reaches the target number of training times;

[0117] In all the trainings in the second training stage, mark the number of training times with a bit error rate less than the target bit error rate and a signal - to - noise ratio greater than the target bit error rate as the undetermined number of times;

[0118] Among all the undetermined numbers of times, mark the undetermined number of times corresponding to the minimum transmit power increment as the target number of times;

[0119] Take the weights and biases corresponding to the target number of times as the optimal parameters, and the training of the fully connected layer - BP neural network is completed.

[0120] In the present invention, the in - sample data includes: multiple groups of background noise characteristics, one group of bit error rate characteristics, one group of signal - to - noise ratio characteristics, and a demand coefficient.

[0121] The first training stage trains the network with the gap between the predicted transmit power increment and the maximum transmit power increment as the first objective value, which can guide the network to adjust parameters in the direction of the maximum transmit power increment, enabling the network to have the weights and biases for predicting the maximum transmit power increment. However, the maximum transmit power increment is not the optimal choice, so a second training stage is set up to find the weights and biases that meet the requirements.

[0122] In the second training stage, when the predicted transmit power increment is greater than the maximum transmit power increment, take the maximum transmit power increment as the actual transmit power increment, which avoids the transmit power exceeding the maximum value allowed by the system, ensures the safe operation of the satellite communication equipment, prevents damage to the equipment caused by excessive transmit power, and also avoids unnecessary interference to other communication systems.

[0123] By transmitting satellite test signals and counting the bit error rate and signal-to-noise ratio at the receiving end to calculate the second target value, and then updating the weights and biases of the network based on this target value, the training method based on the actual communication effect enables the network to learn the true relationship between the transmission power and the communication quality, rather than just the theoretical numerical relationship. Thus, in actual applications, the network can more accurately adjust the transmission power according to the feedback of the communication quality, improving the reliability and stability of communication.

[0124] Since the satellite is powered by batteries and the adjustment of the satellite's transmission power cannot be increased without limit, the number of times corresponding to the minimum increase in transmission power is selected as the target number of times within the pending number of times, so as to find the training result that makes the adjustment of the transmission power most reasonable (with the smallest increase) on the premise of meeting the communication quality requirements. The corresponding weights and biases are used as the optimal parameters. In subsequent applications, the neural network can not only ensure the communication quality but also adjust the transmission power in an energy-saving manner.

[0125] In this embodiment, when the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; when the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: ; when the bit error rate is less than or equal to the target bit error rate and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; when the bit error rate is less than or equal to the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: G = c, where B act is the bit error rate of the satellite test signal, B tar is the target bit error rate, S act is the signal-to-noise ratio of the satellite test signal, S tar is the target signal-to-noise ratio, G is the second target value, and c is a constant between 0 and 1.

[0126] In the present invention, when the bit error rate is greater than the target bit error rate, the ratio of the bit error rate to the target bit error rate is used to reflect the situation where the bit error rate exceeds the target bit error rate. When the signal-to-noise ratio is less than the target signal-to-noise ratio, the ratio of the target signal-to-noise ratio to the signal-to-noise ratio is used to reflect the situation where the signal-to-noise ratio is less than the target signal-to-noise ratio, so that the larger the bit error rate and the smaller the signal-to-noise ratio, the larger the target value of the objective function, and the decrease amplitude of the weights and biases changes with the situation of the signal-to-noise ratio and the bit error rate. When both the bit error rate and the signal-to-noise ratio meet the requirements, a constant between 0 and 1 is assigned to the target value to facilitate the update of the weights and biases. For example, when c is set to 0.1, c has a smaller value, and the search for the weights and biases is more detailed, and more training times are required.

[0127] In this embodiment, in the second stage, the target number of training times is set to 500 times.

[0128] In this embodiment, in the first training stage, the formulas for updating the weights and biases are as follows: , , where ω i+1 is the weight for the (i + 1)-th training, b i+1 is the bias for the (i + 1)-th training, ω i is the weight for the i-th training, b i is the bias for the i-th training, G i is the first target value for the i-th training, and μ is the step size.

[0129] In the second training stage, the formulas for updating the weights and biases are as follows: , , where ω n+1 is the weight for the (n + 1)-th training, b n+1 is the bias for the (n + 1)-th training, ω n is the weight for the n-th training, b n is the bias for the n-th training, and G n is the second target value for the n-th training.

[0130] In this embodiment, the empirical value of the step size μ is 0.001, and the specific value can be adjusted according to needs during the training process.

[0131] The present invention constructs a background noise power sequence by scanning the satellite communication frequency band and its adjacent frequency bands, and combines the features extracted from the bit error rate sequence and the signal-to-noise ratio sequence, as well as the demand coefficient obtained based on the gap between the bit error rate, signal-to-noise ratio and the target value, providing rich and comprehensive data samples for the fully connected layer - BP neural network. Compared with the traditional method that only relies on the signal-to-noise ratio at the receiving end, it can calculate the transmit power increment more accurately, thus significantly improving the accuracy of transmit power adjustment and effectively ensuring the stability and accuracy of signal transmission.

[0132] The present invention considers the influence of the real-time spectrum state of the frequency band used in satellite communication and its adjacent frequency bands on the signal, and at the same time obtains the demand coefficient according to the gap between the latest bit error rate and signal-to-noise ratio and the target value, which represents the actual demand for transmit power adjustment in the current satellite communication system, improving the prediction accuracy of the fully connected layer - BP neural network.

[0133] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for adjusting transmission power in satellite communication, characterized in that: The following steps are involved: The frequency band used for satellite communication and adjacent frequency bands are scanned by spectrum monitoring equipment on the satellite to construct multiple background noise power sequences; Extract features from each background noise power sequence to obtain a set of background noise features; According to the bit error rate and signal-to-noise ratio of the signals transmitted by each satellite, a bit error rate sequence and a signal-to-noise ratio sequence are formed, and features are extracted from the bit error rate sequence and the signal-to-noise ratio sequence respectively to obtain a set of bit error rate features and a set of signal-to-noise ratio features; Obtain the demand coefficient based on the gap between the latest bit error rate and the target bit error rate, and the gap between the latest signal-to-noise ratio and the target signal-to-noise ratio; Multiple sets of background noise features, a set of bit error rate features, a set of signal-to-noise ratio features and demand coefficients are input as samples into the trained fully connected layer-BP neural network to obtain the transmit power increment; The transmit power increment is added to the initial transmit power to obtain the transmit power of the next signal.

2. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: The process of constructing multiple background noise power sequences includes: When the satellite is not transmitting a signal, the frequency band used for satellite communication is scanned by a spectrum monitoring device on the satellite to obtain a first background signal in each historical time period, calculate the background noise power, and construct a first background noise power sequence; When the satellite is not transmitting a signal, a frequency band adjacent to the frequency band used for satellite communication is scanned by a spectrum monitoring device on the satellite to obtain a second background signal in each historical time period, calculate the background noise power, and construct a second background noise power sequence; When the satellite is not transmitting a signal, a spectrum monitoring device on the satellite is used to scan another adjacent frequency band of the frequency band used for satellite communication to obtain a third background signal in each time period, calculate the background noise power, and construct a third background noise power sequence.

3. The method for adjusting transmission power in satellite communication according to claim 2, characterized in that: The formula for calculating background noise power is: , where P is the background noise power, x n is the nth signal value in the background signal of a time period, N is the length of the background signal of a time period, | | is the absolute value sign, and n is a positive integer.

4. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: A set of background noise features includes: background noise power mean and background noise power increment; The mean background noise power is the mean of the background noise power sequence; The background noise power increment is the difference between the latest background noise power and the earliest background noise power in the background noise power sequence.

5. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: A set of bit error rate characteristics includes: bit error rate mean and bit error rate delta; The mean bit error rate is the mean of the bit error rate sequence composed of the bit error rates of satellite transmission signals at each historical moment; The bit error rate increment is the difference between the latest bit error rate and the earliest bit error rate in the bit error rate sequence; A set of signal-to-noise ratio features include: signal-to-noise ratio mean and signal-to-noise ratio increment; The mean value of the signal-to-noise ratio is the mean value of the signal-to-noise ratio sequence composed of the signal-to-noise ratio of the satellite transmission signal at each historical moment; The signal-to-noise ratio increment is the difference between the latest signal-to-noise ratio and the earliest signal-to-noise ratio in the signal-to-noise ratio sequence.

6. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: The process of calculating the demand factor includes: Determine whether the latest bit error rate is greater than the target bit error rate. If so, subtract the latest bit error rate from the target bit error rate to obtain the bit error rate difference. The ratio of the bit error rate difference to the target bit error rate is used as the first demand component. If not, assign 0 to the first demand component. Determine whether the latest signal-to-noise ratio is less than the target signal-to-noise ratio. If so, subtract the target signal-to-noise ratio from the latest signal-to-noise ratio to obtain the signal-to-noise ratio difference, and use the ratio of the signal-to-noise ratio difference to the target signal-to-noise ratio as the second demand component; if not, assign 0 to the second demand component; The first demand component and the second demand component are added together to obtain the demand coefficient.

7. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: The fully connected layer-BP neural network includes: a first fully connected layer, a second fully connected layer, a third fully connected layer, a fourth fully connected layer, a fifth fully connected layer, an input layer, a first hidden layer, a second hidden layer and an output layer; The input end of the first fully connected layer is used to input a first set of background noise features; The input end of the second fully connected layer is used to input a second set of background noise features; The input end of the third fully connected layer is used to input the third set of background noise features; The input end of the fourth fully connected layer is used to input a set of bit error rate features; The input end of the fifth fully connected layer is used to input a set of signal-to-noise ratio features; The input layer is used to input the output of the first fully connected layer, the output of the second fully connected layer, the output of the third fully connected layer, the output of the fourth fully connected layer, the output of the fifth fully connected layer and the demand coefficient into the input end of the first hidden layer; The output terminal of the first hidden layer is connected to the input terminal of the second hidden layer; The input end of the output layer is connected to the output end of the second hidden layer, and its output end serves as the output end of the fully connected layer - BP neural network.

8. The method for adjusting transmission power in satellite communication according to claim 1, characterized in that: The process of training the fully connected layer-BP neural network includes: a first training stage and a second training stage. The first training stage is used to train the fully connected layer-BP neural network according to the difference between the transmit power increment predicted by the fully connected layer-BP neural network and the maximum transmit power increment; the second training stage is used to re-train the fully connected layer-BP neural network after the first training stage to find the optimal weight and bias corresponding to the minimum transmit power increment.

9. The method for adjusting transmission power in satellite communication according to claim 8, characterized in that: The second training phase includes: Input the samples of the nth training into the fully connected layer-BP neural network after the first training stage to obtain the transmit power increment of the nth training, where n is the number of training times in the second training stage; When the transmit power increment of the nth training is greater than the maximum transmit power increment, the maximum transmit power increment is used as the transmit power increment of the nth training, and the transmit power increment of the nth training is added to the initial transmit power to obtain the transmit power of the nth training; The satellite test signal is transmitted using the transmission power of the nth training; Counting the bit error rate and signal-to-noise ratio of the satellite test signal received by the receiving end, and calculating the second target value based on the target function; The weights and biases in the fully connected layer-BP neural network are updated according to the second target value, n is incremented by 1, and the training process is repeated until the number of training times reaches the target number of training times; In all training times of the second training stage, the number of training times in which the marked bit error rate is less than the target bit error rate and the signal-to-noise ratio is greater than the target bit error rate is the pending number; Among all pending times, mark the pending time corresponding to the minimum transmit power increment as the target time; The weights and biases corresponding to the target times are taken as the optimal parameters, and the training of the fully connected layer-BP neural network is completed.

10. The method for adjusting transmission power in satellite communication according to claim 9, characterized in that: When the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; When the bit error rate is greater than the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: ; When the bit error rate is less than or equal to the target bit error rate, and the signal-to-noise ratio is less than the target signal-to-noise ratio, the objective function is: ; When the bit error rate is less than or equal to the target bit error rate and the signal-to-noise ratio is greater than or equal to the target signal-to-noise ratio, the objective function is: G=c, where B act is the bit error rate of the satellite test signal, B tar is the target bit error rate, S act is the signal-to-noise ratio of the satellite test signal, S tar is the target signal-to-noise ratio, G is the second target value, and c is a constant between 0 and 1.

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