CN114580279B - Low-orbit satellite communication self-adaptive coding method based on LSTM - Google Patents
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Abstract
The invention discloses a low-orbit satellite communication self-adaptive coding method based on LSTM, which is characterized by comprising the following steps: step 1, ground equipment acquires local real-time weather and longitude and latitude; step 2, generating satellite position information according to satellite orbit information stored in ground equipment, and representing the satellite position information by three-dimensional coordinates; step 3, establishing a prediction SNR model based on LSTM by using real-time weather, longitude, latitude and position information; and 4, selecting an optimal coding scheme according to a table look-up method. The invention has the advantages that: predicting channel quality SNR (signal to noise ratio) containing weather information by using LSTM (least squares), considering SNR at the first several moments, and fully utilizing satellite operation rules; as LSTM can learn information such as longitude and latitude, and further learn rain attenuation values of different areas, the global universal model is obtained.
Description
Technical Field
The invention relates to the technical field of computer networks, in particular to a low-orbit satellite communication adaptive coding (Adaptive Coding Mod ulation, ACM) method based on a long-short-time memory network (Lon g Short Term Memory, LSTM).
Background
The low orbit satellite is a satellite which is tens to hundreds of kilometers away from the earth surface, and the channel capacity is improved compared with that of a medium and high orbit satellite due to the fact that the low orbit satellite is closer to the earth surface. The low orbit satellite can not have dead angle coverage, and the strategic position is very important for providing high-quality communication for air and ground facilities. Low-orbit satellite communications face the dual challenges of extreme weather availability and rapid throughput rise: the low orbit satellite needs to keep high availability in the scenes of disaster relief, polar regions, rain and snow and the like, and with the increase of network bandwidth and video content, the total throughput of the network can be further improved, and when the low orbit satellite is applied to the fields of unmanned driving, video watching and the like, lower time delay and higher bandwidth are needed.
Adaptive coding (Adaption Coding Modulation, ACM) is an important approach to improve satellite band utilization to improve throughput. In conventional satellite communications, a margin of 6dB is usually provided for rain fade, but due to different cloud cover thickness, the global rain fade value is different, and the rain fade value is only available in rainy days, and the rain fade in sunny days is 0, so ACM is usually performed according to weather in practice to improve throughput. ACM in low-orbit satellite communications has technical difficulties such as: the low-orbit satellite has high motion speed, frequent switching and needs rapid real-time prediction; the global rain attenuation values are different in all places, and a global model needs to be established; global weather changes dynamically and is difficult to capture and synchronize quickly.
In the low-orbit satellite communication adaptive coding problem, researchers have used methods such as identifying antenna Noise temperature, linear regression, exponential regression, support vector regression (Support Vector Regression, SVR) and the like to predict Signal-to-Noise Ratio (SNR), but none of them uses information that the satellite has a strong motion law. A Long short-term memory (LSTM) is a cyclic neural network with memory, which is characterized in that a forgetting gate, an input gate and an output gate control the memory degree of historical information and the adoption degree of new information, and the identification of the passing information of a satellite can be realized through a memory unit.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an LSTM-based low-orbit satellite communication self-adaptive coding method.
In order to achieve the above object, the present invention adopts the following technical scheme:
An LSTM-based low-orbit satellite communication adaptive coding method comprises the following steps:
Step 1, ground equipment acquires local real-time weather and longitude and latitude;
step 2, generating satellite position information according to satellite orbit information stored in ground equipment, and representing the satellite position information by three-dimensional coordinates;
step 3, establishing a prediction SNR model based on LSTM by using real-time weather, longitude, latitude and position information;
and 4, selecting an optimal coding scheme according to a table look-up method.
Further, the specific steps of step 1 are as follows:
step 1.1, ground equipment acquires longitude and latitude of the ground equipment;
step 1.2, the ground equipment requests a website to acquire real-time weather according to longitude and latitude;
It should be noted that, because the single communication time between the low-orbit satellite and the ground equipment is very short, usually several minutes, the weather at the communication starting time is considered as the weather of the communication, so that only one weather condition is required at the communication starting time;
further, the specific steps of step 2 are as follows:
Step 2.1, the ground equipment needs to store the orbit information of each satellite and has the capability of finding out the satellites which can communicate in a specified time; the orbit information is information describing and predicting satellite positions through calculation, is expressed by two rows of orbit element forms, adopts two rows of ASCII codes of 80 characters to store data, and occupies little space;
step 2.2, converting the position of the satellite from orbit information into three-dimensional coordinates;
further, the specific steps of step3 are as follows:
Step 3.1 forming a sequence using real-time weather, latitude and longitude and position information
(UElat,UElon,Satlat,SatLon,Satalt,Weather)；
Wherein, the UE lat,UElon is the longitude and latitude of the ground equipment, the Sat lat,SatLon,Satalt is the longitude and latitude and the altitude of the satellite, and the Weather is the local real-time Weather;
Step 3.2, inputting the sequence in the step 3.1 into an LSTM network for training, and using MAE as an error function; MAE is the absolute value of the difference between the true SNR and the predicted SNR; the error function is used for back propagation and updating parameters in the network;
step 3.3, predicting and verifying by using the trained LSTM;
The verification step is to prove that the network is not over fitted, divide all data sets into three parts of a training set, a verification set and a test set, and observe the conditions on the verification set during training to determine the over fitting condition of the model;
further, the specific steps of step 4 are as follows:
selecting a coding scheme according to the predicted SNR table lookup, and selecting a coding scheme with the lowest redundancy rate, wherein the packet error rate (Package Error Rate, PER) is smaller than 0.1;
the packet error rate is the probability of error code in one frame of data in communication, and is generally considered to be smaller than 0.1, so that normal communication can be realized;
Here, the redundancy rate is a ratio of the number of bits used for error correction in forward error correction coding to the total number of bits, that is, redundancy rate= (number of codes used for error correction/total number of bits) ×100%.
Compared with the prior art, the invention has the advantages that:
1. predicting channel quality SNR (signal to noise ratio) containing weather information by using LSTM (least squares), considering SNR at the first several moments, and fully utilizing satellite operation rules;
2. the LSTM can learn information such as longitude and latitude, so that rain attenuation values of different areas are learned, and a global universal model is obtained;
Drawings
FIG. 1 is a diagram of an LSTM network in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of LSTM neurons in accordance with an embodiment of the invention;
FIG. 3 is a diagram of packet error rates for different coding scheme redundancy rates in an embodiment of the present invention;
FIG. 4 is a graph showing MAE accumulation profiles (Cumulative Distribution Function, CDF) in different regions of the world for different methods in accordance with embodiments of the present invention;
Fig. 5 is a CDF diagram of the optimal coding scheme matching rate (Optimal Match Ration, OMR) of different methods in different regions around the world according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the accompanying drawings and by way of examples in order to make the objects, technical solutions and advantages of the invention more apparent.
As shown in fig. 1, this embodiment discusses an application of the LSTM-based low-orbit satellite communication adaptive coding method in satellite-ground communication.
The present embodiment is based on an LSTM network, which is responsible for identifying the two-dimensional position of a User Equipment (UE), the three-dimensional position of a satellite, and the weather condition where the UE is located, and predicting the SNR at the next time according to the communication quality at the past time. The embodiment of the low-orbit satellite communication adaptive coding method based on LSTM provided by the invention is as follows:
Step 1, ground equipment acquires local real-time weather and longitude and latitude thereof:
Step 1.1, the ground equipment acquires the longitude and latitude of the ground equipment, if the real-time longitude and latitude of the UE is (UE lat,UElon), the longitude and latitude information can be obtained by GPS satellite positioning or base station positioning, and if the information is missing, the position of the ground base station interacted with by the UE last can be used as a default position;
step 1.2, the ground equipment acquires real-time weather according to a longitude and latitude request website, and after acquiring longitude and latitude information, the UE can request a weather API to acquire the real-time weather of the ground;
Wherein, API is Application Programming Interface, the abbreviation of the application program interface, the user sends request to API, API will give corresponding answer;
It is noted that only the start of each link requires a query for local weather, since the connection time for low-orbit satellites is very short, typically a few minutes. For Starlink, the communication diameter is 1100 km, and the longest connection time is 170 seconds, so that the server is not excessively burdened with inquiring the weather only when the connection is started, and excessive communication resources are not additionally occupied.
Step 2, generating satellite position information according to satellite orbit information stored in ground equipment, and representing the satellite position information by three-dimensional coordinates:
step 2.1 the ground equipment needs to store orbit information for each satellite and has the ability to find satellites that can communicate within a specified time:
The information of the satellite orbit is stored in two rows of orbit information, the storage mode is universal worldwide, the storage space is saved, and even if the orbit information of all satellites in orbit at present is stored, only 800KB space is needed.
TABLE 1 satellite CALSPHERE orbit information
Table 1 is orbit parameter information for satellite CALSPHERE 1 transmitted on day 4 of 2022, 1:
The first row and first column represent their NASA unique numbers, U represents a non-confidential satellite;
the first row and the second column represent the international number of the aircraft;
The third column of the first row is the emission time, say 22 means 2022, 004.58268302 is that it was emitted on day 4.58268302 of 2 022;
The second column of the second row is the track pitch;
the second row and the third column are the longitude of the rising intersection point;
the second row and the fourth column are track eccentricities;
the fifth column of the second row is the near-place angular distance;
The sixth column of the second row is a straight-near point angle;
the second row and the seventh column are the flat motion rate;
from the above orbit information, satellites and their specific positions within the communication distance at the present time can be calculated.
Step 2.2, converting the position of the satellite from orbit information to three-dimensional coordinates, converting the orbit information to three-dimensional coordinate information by adopting methods such as Lagrangian polynomial interpolation, newton polynomial interpolation, interior-Viel successive linear interpolation and the like, and acquiring the positions of the satellite at different moments by using satellite simulation software STK (System tool kit, STK) under a simulation scene;
Step 3, establishing a model of predicting SNR based on LSTM by using the information in the step1 and the step 2;
Step 3.1 the information in step 1, step 2 is used to compose a sequence
Combining the information obtained in the step 1 and the step 2, and representing the weather information in an One-Hot encoding mode, wherein in practice, the weather information is represented by (Sunny, cloudy, rainy), the weather information is encoded as (1, 0) in sunny days, the weather information is encoded as (0, 1, 0) in cloudy days, the rain and snow weather is considered to be precipitation weather, and the rain and snow weather is uniformly regarded as rainfall weather, and the weather information is encoded as (0, 1);
Eight tuples of the input LSTM network are constructed so far
X t＝(UElat,UElon,Satlat,SatLon,Satalt, sunny, cloudy, rainy), wherein UE lat,UElon is the latitude and longitude of the ground device, respectively, sat lat,SatLon,Satalt is the latitude and longitude and altitude of the satellite, (Sunny, cloudy, rainy) represents the local weather;
at each moment in the simulation, i.e. every 1 second, there is a corresponding message.
Step 3.2, inputting the sequence in the last step into an LSTM network for training, and using MAE as an error function;
The LSTM network is structured as shown in FIG. 1, incorporating an embedded layer (Embedding Layer) to handle weather and location information, respectively. There are 3 neurons responsible for handling One-Hot encoded weather information and 5 neurons responsible for handling location information. The embedded layer is followed by 1 LSTM layer which contains 100 neurons and is responsible for memorizing past information; the LSTM layer is followed by a full-connection layer containing 100 neurons and is responsible for integrating information of the L STM layer; finally, the predicted SNR is output for the output layer containing one neuron.
The LSTM network solves the problem of long-term dependence by means of three gate functions, namely a forget gate, an input gate and an output gate, respectively, as shown in fig. 2, and can memorize long-time information by means of the gate functions;
(1) Forgetting the door: when the network gets new input, if the network needs to forget old information, the network is finished through a forgetting gate. The forgetting gate is an important component of the LSTM network, can control which information needs to be reserved and which information needs to be forgotten, and can avoid the difficult problems of gradient explosion and gradient disappearance caused by the back propagation of the gradient along with time. The forget gate decides what information the LSTM network has forgotten from the last time network state C t-1. The forget gate reads the last time output value h t-1 and the now input value x t, then maps it to a value between 0 and 1 by a sigmoid activation function, and finally multiplies the value by the network state C t-1 to determine C t-1 what information to discard. The forget gate value of 1 indicates that the information of the network state C t-1 is completely reserved, and the value of 0 indicates that the information of the network state C t-1 at the previous time is completely discarded. The expression of the forgetting gate is:
ft＝σ(Wf[ht-1,xt]+bf),
(2) An input door: it is determined which part of the new input information is kept in the network state. The input gate is used to control how much of the current input data x t of the network flows into the memory unit, i.e. how much of the input information x t can be saved into the current network state C t. The input door comprises two parts, a first part: a control signal i t composed of sigmoid activation functions and generated between 0 and 1 for controlling the network state at that time The degree of input; the second part is the candidate network state/>, generated by the tanh layer, at the current momentThis value will be determined by i t to the extent of addition to the network state. The state expression of the input gate is:
it＝σ(Wi[ht-1,xt]+bi),
after the states of the forget gate and the input gate are obtained, the state of the network may be updated:
where C t is the final determined network state at this point.
(3) Output door: the output value is based on the current time network state, but there is a filtering and screening process. The output door also includes two part operation: the first part is a control signal o t between 0 and 1 generated by a sigmoid activation function; the second part is to multiply the output information tanh (C t) generated after the tanh function with the control signal o t to obtain the final output value h t at this moment. The effect of the output gate control memory cell C t on the current output value h t, i.e. which parts of the memory cell will be output at this point.
During the error back propagation, MAE (Mean Absolute Error) is used as an error metric because MAE tends to continue to reduce the error when the error is small compared to MSE (Mean Squared Error), while MSE suffers from gradient vanishing due to its squared nature.
Time-sharing weather information of various global places can be acquired from the internet during training,
3.3, Predicting and verifying by using the LSTM after training;
Because the neural network has a tendency to overfit, the overall dataset is as follows: 2: the proportion of 1 is divided into a training set, a verification set and a test set, and the data of the three data sets are not overlapped and are disturbed. The validation set was predicted every 10 epochs during training to see if the network was overfitted.
And after the training is finished, sampling the test set to obtain a final training result.
The learning rate during training was 0.001, the batch size was set to 128, and the relevant time was set to 5.
After training, MAE from different methods were compared as shown in Table 2:
TABLE 2 MAE for different methods
| Method of | Linear smoothing | Exponential smoothing | SVR | LSTM |
| MAE | 0.114 | 0.062 | 0.032 | 0.017 |
It can be seen that the error of LSTM from the true value is the smallest among them.
The horizontal axis of FIG. 3 is MAE and the vertical axis is the cumulative distribution function (Cumulative Dist ribution Function, CDF) for different locations around the world. It can be seen from the figure that LSTM performs better than other methods in different locations around the world.
And 4, selecting an optimal coding scheme according to a table look-up method.
According to the space consultation system consultation committee (Consultative Committee for SPACE DATA SYSTEMS, CCSDS), the communication codes of the near earth orbit satellites are encoded as AR4JA codes (AR 4 JA) in pseudo random Low density check codes (Quas i-cyclic Low DENSITY PARITY CHECK, QC-LDPC). CCSDS uses the AR4JA code and specifies 3 different data rates, 50%,66%,80%, respectively. As shown in fig. 4, at a lower signal-to-noise ratio, the communication environment is worse, and a coding scheme with lower data rate and higher redundancy rate should be selected to ensure the communication quality; and when the communication quality is better, the coding scheme capable of transmitting more data should be selected to improve the overall throughput.
In practice, the coding scheme with the lowest redundancy rate, with the packet error rate PER less than 0.1, is selected as the optimal coding scheme, which is also often used in actual low-orbit satellite communication scenarios. And the performance of the whole adaptive coding system is expressed by the optimal matching rate (O PTIMAL MATCH Ration, OMR). OMR is the ratio of the optimal coding scheme number to the total test number can be selected under different prediction means.
TABLE 3 OMR for different methods
| Method of | Linear smoothing | Exponential smoothing | SVR | LSTM |
| OMR | 97.9％ | 98.8％ | 99.5％ | 99.8％ |
In fig. 5 it can be seen that the OMR of LSTM is highest compared to other methods and that the performance variance is smaller in different locations around the world than in other methods.
Those of ordinary skill in the art will appreciate that the embodiments described herein are intended to aid the reader in understanding the practice of the invention and that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (1)
1. An LSTM-based low-orbit satellite communication adaptive coding method is characterized by comprising the following steps:
Step 1, ground equipment acquires local real-time weather and longitude and latitude;
The specific steps of the step 1 are as follows:
step 1.1, ground equipment acquires longitude and latitude of the ground equipment;
step 1.2, the ground equipment requests a website to acquire real-time weather according to longitude and latitude;
it should be noted that, because the single communication time between the low-orbit satellite and the ground equipment is very short, the weather at the communication starting time is considered as the weather of the communication, so that only one weather condition is required at the communication starting time;
step 2, generating satellite position information according to satellite orbit information stored in ground equipment, and representing the satellite position information by three-dimensional coordinates;
the specific steps of the step 2 are as follows:
Step 2.1, the ground equipment needs to store the orbit information of each satellite and has the capability of finding out the satellites which can communicate in a specified time; the orbit information is information describing and predicting satellite positions through calculation, is expressed by two rows of orbit element forms, adopts two rows of ASCII codes of 80 characters to store data, and occupies little space;
step 2.2, converting the position of the satellite from orbit information into three-dimensional coordinates;
step 3, establishing a prediction SNR model based on LSTM by using real-time weather, longitude, latitude and position information;
the specific steps of the step 3 are as follows:
Step 3.1 forming a sequence using real-time weather, latitude and longitude and position information
(UElat,UElon,Satlat,SatLon,Satalt，Weather)；
Wherein, the UE lat,UElon is the longitude and latitude of the ground equipment, the Sat lat,SatLon,Satalt is the longitude and latitude and the altitude of the satellite, and the Weather is the local real-time Weather;
Step 3.2, inputting the sequence in the step 3.1 into an LSTM network for training, and using MAE as an error function; MAE is the absolute value of the difference between the true SNR and the predicted SNR; the error function is used for back propagation and updating parameters in the network;
step 3.3, predicting and verifying by using the trained LSTM;
The verification step is to prove that the network is not over fitted, divide all data sets into three parts of a training set, a verification set and a test set, and observe the conditions on the verification set during training to determine the over fitting condition of the model;
Step 4, selecting an optimal coding scheme according to a table look-up method;
the specific steps of the step 4 are as follows:
Selecting a coding scheme according to the predicted SNR table lookup, and selecting a coding scheme with the lowest redundancy rate, wherein the packet error rate of the coding scheme is smaller than 0.1;
The packet error rate is the probability of error code in one frame of data in communication, and is considered to be smaller than 0.1, so that normal communication can be realized;
Here, the redundancy rate is a ratio of the number of bits used for error correction in forward error correction coding to the total number of bits, that is, redundancy rate= (number of codes used for error correction/total number of bits) ×100%.
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