CN114189317B - A Realization Method of Deep Fusion of Remote Sensing for Communication Navigation - Google Patents

Abstract

The invention discloses a method for realizing communication navigation remote sensing depth fusion, which is characterized in that transmission selection information is configured, so that a signal can adaptively configure a corresponding MCS according to the transmission selection information during data transmission; the transmission selection information has a corresponding selection relation between a transmission signal-to-noise ratio interval and a corresponding MCS; the corresponding MCS is a code modulation pair capable of realizing the maximum throughput in the corresponding transmission signal-to-noise ratio interval. By distinguishing the signal-to-noise ratio, the matching among the current service type, the user requirement and the channel quality of the transmission link can be optimized, the transmission efficiency is improved, and the channel resources are fully utilized.

Description

A Realization Method of Deep Fusion of Remote Sensing for Communication Navigation

technical field

The invention relates to the technical field of satellite data communication, in particular to a method for realizing deep fusion of communication, navigation and remote sensing.

Background technique

Currently, most small cell transmitters use encapsulation/modulation/coding access technologies such as SCPC/FDMA, SCPC DAMA, CDMA, TDMA, and MF-TDMA, which are collectively referred to as carrier types. Each carrier type is suitable for different application modes and service types. Different carrier types will affect application performance, channel overhead, spectrum efficiency, overload capacity, and the total bandwidth utilization of satellites, thus affecting the experience of end users, the operator's revenue and user churn. In the prior art, many VSAT satellite communication systems can support multiple carrier types, but it is generally necessary for operators to choose which carrier type is most suitable for which remote station in the network planning stage, and the satellite terminal purchased by each remote station. The type of (modem) must match the planned carrier mode. However, when user services are constantly enriched and the demands for different data transmissions are constantly changing, it is sometimes difficult for operators to predict which access technology a remote station should use. At this time, this communication system always adopts one or several preset carrier modulation modes, which will hinder the transmission of large-data-volume services, such as discontinuous image jumping, mosaic and so on.

The prior art, such as the Chinese invention patent application with the publication number CN104468032B, discloses an adaptive modulation and coding beam switching method for a mobile broadband satellite communication system applied to a high-speed railway. The method includes: A. Determining the reference threshold value of the signal-to-noise ratio corresponding to each adaptive modulation and coding mode. According to the channel state information of the current mobile broadband satellite channel - the receiving signal-to-noise ratio, calculate the error between its value and the reference threshold value of the signal-to-noise ratio of the corresponding modulation and coding method, and calculate the average signal-to-noise ratio value of the receiving end according to the size of the error. corrections to varying degrees. The corresponding modulation and coding mode is determined according to the average signal-to-noise ratio obtained by the correction. B. If the average SNR value obtained by the correction is smaller than the SNR reference threshold value of the lowest-order modulation and coding mode, perform the beam switching operation. In the environment of high-speed railway mobile broadband satellite communication with relatively fixed routes, the method can effectively improve the transmission performance of the system, ensure the performance of the system bit error rate, and improve the throughput of the system; at the same time, the beam resources are fully utilized to avoid frequent and unnecessary beam switching. .

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a system that can adaptively configure different modulation modes and code rates for different types of services, make full use of channel spectrum resources, and improve system throughput according to different application environments and different service requirements. Implementation method of deep fusion of remote sensing for communication and navigation.

A method for realizing deep fusion of communication, navigation and remote sensing, which configures transmission selection information so that a signal can adaptively configure a corresponding MCS according to the transmission selection information in the transmission data information; the transmission selection information has a transmission signal-to-noise ratio. The corresponding selection relationship between the interval and the corresponding MCS; the corresponding MCS is the coding modulation pair that can achieve the maximum throughput in the corresponding transmission signal-to-noise ratio interval. By distinguishing the signal-to-noise ratio, it is possible to optimize the matching between the current service type, user requirements and channel quality of the transmission link, improve transmission efficiency and make full use of channel resources.

The transmitter obtains the channel state information and the channel signal-to-noise ratio γ of the current transmission link, and calculates a set of optimal switching thresholds { γ _i , i=1, 2, ..., n} through the optimization algorithm; In the process, the decision module will first compare the size relationship between γ and γ _N , and when γ is greater than or equal to γ _N , it is determined that the adaptive system adopts the Nth coding modulation method at this time, otherwise, the comparison between γ and γ _N-1 is continued. size relationship, (N∈i), until the interval that matches the current channel SNR and the corresponding coding and modulation mode are determined. The interval division and classification based on the signal-to-noise ratio is more convenient to obtain, which can reduce the cost of system computing power for obtaining the classification basis.

The receiving end of the system estimates the channel of the current transmission link according to the received transmission signal, and transmits the channel state information to the transmitting end through the forward link; Channel information of the link, according to the transmission selection information, select the MCS that is suitable for the current channel and has the highest spectral efficiency.

The transmitter extracts the channel information from the forward link corresponding to the transmission link, and selects the MCS that is suitable for the current channel and has the highest spectral efficiency according to the transmission selection information; the transmitter can obtain the forward link channel that is not affected by the feedback transmission delay state information, so as to equivalently obtain the channel information of the current transmission link.

The transmitting end extracts the channel information from the forward link corresponding to the transmission link, and using the same channel estimation algorithm, the transmitting end of the small station can obtain the forward link channel state information that is not affected by the feedback transmission delay. Thus, the channel information of the current transmission link is equivalently obtained.

The transmission selection information also includes the feature information extracted according to the current data link transmission information; the feature information set is learned, the abnormal transmission data is distinguished, and after the abnormality is found, the MCS is reselected for the remaining data according to the switching threshold. The feature information is continuously extracted, so that a statistically significant feature information set can be obtained. The characteristic information includes signal strength, amplitude, and value at a certain position in a message or data frame.

The invention also discloses a computer program of a method for realizing deep fusion of communication, navigation and remote sensing, and a storage medium, which store the computer program of the above-mentioned method for realizing deep fusion of communication, navigation and remote sensing.

The present invention adopts the configuration transmission selection information, so that the signal in the transmission data information can configure the corresponding MCS adaptively according to the transmission selection information; the transmission selection information has the corresponding selection of the transmission signal-to-noise ratio interval and the corresponding MCS relationship; the corresponding MCS is the code modulation pair that can achieve the maximum throughput in the corresponding transmission signal-to-noise ratio interval. By distinguishing the signal-to-noise ratio, it can optimize the matching among the current service type, user requirements and channel quality of the transmission link, improve transmission efficiency and make full use of channel resources. Therefore, the present invention has the advantages of adaptively configuring different modulation modes and code rates for different types of services according to different application environments and different service requirements, making full use of channel spectrum resources and improving system throughput.

Description of drawings

1 is a schematic flowchart of an adaptive algorithm module according to an embodiment of the present invention implementing coding and modulation to automatic judgment;

2 is a block diagram of an adaptive modulation and coding structure according to an embodiment of the present invention;

3 is a schematic structural diagram of Embodiment 4 of the present invention;

4 is a schematic structural diagram of Embodiment 5 of the present invention;

FIG. 5 is a schematic diagram of the distribution trend of the Generator generated samples and the training samples according to Embodiment 6 of the present invention;

FIG. 6 is a comparison diagram of system throughput between Embodiment 6 of the present invention and the prior art.

Detailed ways

The present invention will be described in further detail below in conjunction with the accompanying embodiments.

Embodiment 1: a method for realizing deep fusion of communication, navigation and remote sensing, which configures transmission selection information, so that the signal can configure corresponding MCS adaptively according to the transmission selection information in the transmission data information; the transmission selection information has The corresponding selection relationship between the transmission signal-to-noise ratio interval and the corresponding MCS; the corresponding MCS is the coding modulation pair that can achieve the maximum throughput in the corresponding transmission signal-to-noise ratio interval. By distinguishing the signal-to-noise ratio, it is possible to optimize the matching between the current service type, user requirements and channel quality of the transmission link, improve transmission efficiency and make full use of channel resources.

The transmitter obtains the channel state information and the channel signal-to-noise ratio γ of the current transmission link, and calculates a set of optimal switching thresholds { γ _i , i=1, 2, ..., n} through the optimization algorithm; In the process, the decision module will first compare the size relationship between γ and γ _N , and when γ is greater than or equal to γ _N , it is determined that the adaptive system adopts the Nth coding modulation method at this time, otherwise, the comparison between γ and γ _N-1 is continued. size relationship, (N∈i), until the interval that matches the current channel SNR and the corresponding coding and modulation mode are determined. The interval division and classification based on the signal-to-noise ratio is more convenient to obtain, which can reduce the cost of system computing power for obtaining the classification basis.

The receiving end of the system estimates the channel of the current transmission link according to the received transmission signal, and transmits the channel state information to the transmitting end through the forward link; Channel information of the link, according to the transmission selection information, select the MCS that is suitable for the current channel and has the highest spectral efficiency.

The transmitter extracts the channel information from the forward link corresponding to the transmission link, and selects the MCS that is suitable for the current channel and has the highest spectral efficiency according to the transmission selection information; the transmitter can obtain the forward link channel that is not affected by the feedback transmission delay state information, so as to equivalently obtain the channel information of the current transmission link.

The transmitting end extracts the channel information from the forward link corresponding to the transmission link, and using the same channel estimation algorithm, the transmitting end of the small station can obtain the forward link channel state information that is not affected by the feedback transmission delay. Thus, the channel information of the current transmission link is equivalently obtained.

The transmission selection information also includes the feature information extracted according to the current data link transmission information; the feature information set is learned, the abnormal transmission data is distinguished, and after the abnormality is found, the MCS is reselected for the remaining data according to the switching threshold. The feature information is continuously extracted, so that a statistically significant feature information set can be obtained. The characteristic information includes signal strength, amplitude, and value at a certain position in a message or data frame.

The invention also discloses a computer program of a method for realizing deep fusion of communication, navigation and remote sensing, and a storage medium, which store the computer program of the above-mentioned method for realizing deep fusion of communication, navigation and remote sensing.

Example 2:

The user data of the system transmitter (small station) needs to be encoded and modulated before it can be sent from the transmit antenna to the wireless communication link. In order to realize the automatic selection of the coding and modulation mode, the transmitter needs to add an adaptive algorithm module, which can select the channel characteristics according to the corresponding adaptive algorithm, the current channel information fed back by the receiver (master station), and the service requirements of specific users. Matching coding and modulation mode, and send the selected command to the encoder and modulator, and finally realize the optimal coding and modulation of the transmitted signal adaptive. The system receiving end (master station) not only needs to obtain user information from the received signal, but also needs to obtain the channel state information of the current transmission link. Therefore, the receiving end (master station) adds a channel state estimation module to pass the obtained channel information through The feedback link is forwarded to the adaptive algorithm module of the transmitter.

Figure 2 shows the block diagram of the adaptive modulation and coding structure. For the adaptive algorithm module of the transmitter (small station), in order to match the current transmission link and achieve effective adaptation, it is necessary to obtain the optimal coding and modulation pair through the optimization algorithm. Switching the threshold, when the channel information parameter and the decision threshold of a set of coding and modulation pairs are optimally matched, the transmitter selects the coding and modulation pair for the current transmission link.

The switching threshold of the adaptive algorithm module is divided into two scenarios:

The first adaptively adjusts modulation-coding pairs based on channel quality estimates. In this scenario, the channel state needs to be divided into several intervals, and each interval corresponds to a set of modulation and coding pairs that meet the requirements of the bit error rate and have the highest spectral efficiency. Each group of modulation and coding pairs of the adaptive system has a corresponding switching threshold, that is, it is expressed as the lower limit of the interval matching the coding and modulation mode. Assuming that the range of the SNR of the adaptive system can be divided into n+1 intervals, any interval is expressed as [ γ _i , γ _i+1 ), and the defined interval [ γ _i , γ _i+1 ) can achieve the maximum The code-modulation pair for throughput is denoted as MCSi. In other words, when the channel signal-to-noise ratio is in the interval [ γ _i , γ _i+1 ), the adaptive system will select the coded modulation pair MCSi as the coding and modulation method of the next frame bit sequence. Therefore, when the signal-to-noise ratio of the channel obtained by the transmitter is γ , the coding-modulation pair selected by the adaptive algorithm module can be expressed as:

In the formula, MCSi represents the code modulation pair of the i-th group, and γ _i is the decision threshold for switching between the code modulation pair MCSi-1 and MCSi. When γ < γ _i , it means that the environment of the communication link is extremely bad, the system will not assign the coding modulation mode, and the system will interrupt the signal transmission at this time. Finally, the self-adaptive algorithm module adopts the decision algorithm of the above formula, and judges to obtain the optimal code modulation pair by comparing with the threshold value for multiple cycles.

After the transmitter (small station) obtains the channel state information, it calculates a set of optimal handover thresholds { γ _i , i=1, 2, ..., n } through the optimization algorithm. It is assumed that the handover thresholds at this time are a set of SNR values arranged in ascending order, at the same time, the transmitter will also obtain the channel SNR γ of the current transmission link; in the process of cyclic decision, the decision module will first compare the size relationship between γ and γ _N , and when γ is greater than When it is equal to γ _N , it is determined that the adaptive system adopts the Nth coding modulation mode at this time, otherwise, continue to compare the magnitude relationship between γ and γ _N-1 until the interval that matches the current channel SNR and the corresponding code are determined. Modulation mode; if the judgment result of γ < γ ₁ is obtained after N times of judgment, the system automatically determines that the link state is not suitable for transmitting information, and waits for the link to resume normal communication; after several cyclic judgments, the final signal at the transmitter The sequence is code modulated and sent to the receiver. It can be seen from Fig. 3 that the algorithm flow starts from the highest signal-to-noise ratio threshold and decreases successively, and after several cyclic judgments, the matched signal-to-noise ratio interval and the corresponding code modulation pair are obtained. Therefore, for any effective signal-to-noise ratio operating in the signal-to-noise ratio interval, a set of optimal coding and modulation pairs can be determined, thereby ensuring that the system can achieve optimal adaptive transmission.

Example 3:

Based on the previous embodiment, this embodiment provides another scenario. Adaptive adjustment of the carrier modulation and coding pair is performed according to the service type of the transmitting end (small cell) and user requirements. The flow chart is shown in Figure 1, which shows the flow of the adaptive algorithm module to realize the automatic decision of coding and modulation. The transmitter obtains the channel state information. According to the type of business requirements, directly select the corresponding MCS, such as short messages, pictures, videos, location information data, remote sensing image data, and MCS can be preset directly. By finding the switching threshold, and then selecting the interval where the signal-to-noise ratio is located, and selecting the corresponding kth MCS. If the current signal-to-noise ratio is satisfied, then reduce a signal-to-noise ratio interval until the low signal-to-noise ratio interval has been traversed and the low channel has been tried.

Example 4:

FIG. 3 shows a schematic diagram of the architecture of this embodiment. This embodiment provides that the receiver (master station) of the system performs channel estimation on the current transmission link according to the received transmission signal, and transmits the channel state information to the transmitter (small station) through the forward link. The transmitter (small station) adaptively selects a set of coding and modulation pairs suitable for the current channel and with the highest spectral efficiency according to the type of service or data volume to be sent and the intercepted channel information of the satellite downlink.

Example 5:

FIG. 4 is a schematic diagram of the architecture of this embodiment. In this embodiment, the small cell transmitter extracts the channel information of the forward link, which is equivalent to the channel state information of the current reverse link, thereby realizing the adaptive process.

The channel state information obtained by the transmitting end of the small station is no longer the feedback of the channel information from the receiving end of the master station, but extracts the channel information from the corresponding forward link of the transmission link. Using the same channel estimation algorithm, the transmitting end The forward link channel state information that is not affected by the feedback transmission delay can be obtained, thereby equivalently obtaining the channel information of the current transmission link.

For satellite communication links with sufficient correlation between the forward and reverse links, when the transmitter of the small station transmits information to the satellite, it no longer receives the feedback of the channel status information of the reverse link from the receiver of the master station, but extracts the forward direction information. Channel state information in the link, thus avoiding the influence of channel feedback delay. Since the channel information of the uplink and the downlink have related characteristics, the transmitting end of the small station can use the channel state information of the forward link to be equivalent to the state information of the current reverse backhaul transmission link.

Example 6:

This embodiment, on the basis of the previous embodiment, further provides an intelligent error correction technology.

The present invention adopts the self-adaptive carrier modulation and coding technology, according to different application environments and different business requirements, different modulation modes are adaptively configured for different types of business (short message, picture, video, location information data, remote sensing image data). and code rate. When the amount of user service data is small or the link conditions are poor, channel coding with low-order modulation and low bit rate is selected to make the system bit error rate lower than the maximum allowable bit error rate requirement; When the road conditions are good, channel coding with high-order modulation and high code rate is selected, so as to make full use of the channel spectrum resources and improve the system throughput. Generally speaking, in the limited channel resource environment, the technology adopted in the present invention ensures the information transmission quality and efficiency of the communication system.

(I) Prepare training data

The extracted feature information dataset is converted into Gramian Angular Field (GAF) images, and the obtained GAF image samples can be used for the training of GANs and AE in stage (II). Take the preset number of seconds as the basic time interval (take a positive integer multiple of 30, and be less than or equal to 300 seconds.), and divide the recorded feature information into several sub-segments. According to the above operations, the time series data in each basic time interval can be converted into a GAF image.

Pseudo code for feature information training model:

Input: iteration step n ; update substep k of discriminator D in each iteration step

for n iteration steps do

for k update substep do

Randomly generate m latent space vectors {z(1) ,...,z( m )} according to p z(z)

Randomly select m samples {x(1) ,...,x( m )} from the training sample p data(x)

的随机梯度升高方法对D 做更新according to

The stochastic gradient boosting method for updating D

end for

Randomly generate m latent space vectors {z(1) ,...,z( m )} according to p z(z)

的随机梯度速降方法对生成器G做更新according to

update the generator G with the stochastic gradient descent method

end for

In the above pseudocode, p z(z) indicates that the latent space vector z satisfies the prior distribution, and then the generator G (z) is used to map it to the data space, thereby realizing the capture of the data distribution p data(x). Figure 5 shows a schematic diagram that the distribution of the generator generated samples and the training samples tend to be consistent.

(II) Neural Network Training

Using the GAF sample data collected in stage (I), GANs (generative adversarial networks) and AE (autoencoders) are trained successively. For satellite communication links with sufficient correlation between the forward and reverse links, when the transmitter of the small station transmits information to the satellite, it no longer receives the feedback of the channel status information of the reverse link from the receiver of the master station, but extracts the forward direction information. Channel state information in the link, thus avoiding the influence of channel feedback delay. However, this method requires intelligent monitoring, otherwise, if an abnormality occurs, obviously channel resources will be seriously wasted, and at the same time, the corresponding service data transmission is also difficult to meet expectations.

1. Based on the GAF images in the training sample set, the GANs are trained according to the feature information training model pseudo-code algorithm, and the generated distribution (pG) of the generator G in the obtained network is similar to the distribution ( p data) of the training sample images. Generate a random vector based on the normal distribution function, and use G to generate a GAF image. The generated GAF image has a high similarity with the GAF image of the normal sample, and the Euclidean distance between the latent space vectors obtained by the two similar images compressed by the Encoder is also small. That is, interpolate between latent space vector samples, and input G can generate a series of images with smooth transition.

2. Extract the generator G in GANs, embed this into AE as a Decoder, and use the sample data generated in stage (I) for network training. During the training process, the weight coefficient of the generator G is fixed, then the Encoder obtained after training can successfully map the high-dimensional GAF image into a low-dimensional latent space vector.

(III) Switching window judgment

Based on the above work, G in GANs and Encoder in AE are extracted for data anomaly diagnosis. 1. For the monitoring data within the basic time interval, convert it from a time series to a GAF image, and the initial image is recorded as GAF0, input it into the Encoder, output the latent space vector z1, input z1 into the generator G, and output the repeat Construct the image GAF1, further take the reconstructed image GAF1 as the input, and use the Encoder to obtain a new latent space vector z2. On the above basis, calculate the latent space loss Zloss=||z ₂ -z ₁ || ₂ of the reconstructed image.

2. The change of latent space vector can characterize the feature difference of GAF images, so the detected loss of latent space vector can be used as an indicator for data anomaly diagnosis. For the monitoring data within each basic time interval, convert it into a GAF image, and then use the trained GANs and AE to estimate the latent space vector loss Zloss, and estimate the current state according to equations (1) and (2). , the lower cumulative process sum (Ui and Li). On the basis of the above, compare the current state Ui and Li with the preset upper control limit (UCL) and lower control limit (LCL). The current status feature information judgment switch window appears.

3. When the switching window appears, the system recalculates the signal-to-noise ratio of the data transmitted by this service. If the obtained signal-to-noise ratio exceeds the original range, it will switch the remaining data transmission to another one that satisfies the recalculation of the signal-to-noise ratio of the data transmitted by this service. MCS.

Equations (1) and (2) are based on the assumption that x ₁ , x ₂ , ... are k random variables subject to independent and identical distributions, and each variable obeys a Gaussian distribution with mean mx and standard deviation σ x . Through learning, determine the business data and transmission types that are commonly used and can generally be transmitted normally. To transmit special and abnormal data, make temporary judgment during transmission and temporarily switch MCS according to the newly calculated signal-to-noise ratio, so as to avoid that long-term transmission on one channel cannot fully obtain optimized data transmission behavior. FIG. 6 compares the effect between the throughput (GB/s) of the prior art LS-LEOSCN link communication and the throughput of this embodiment. The gain effect of this embodiment is higher in high-frequency channels, and is generally The throughputs of the channels are all greater than those of the prior art.

The present invention adopts the self-adaptive carrier modulation and coding technology, according to different application environments and different business requirements, different modulation modes are adaptively configured for different types of business (short message, picture, video, location information data, remote sensing image data) and code rate. When the amount of user service data is small or the link conditions are poor, channel coding with low-order modulation and low bit rate is selected to make the system bit error rate lower than the maximum allowable bit error rate requirement; When the road conditions are good, channel coding with high-order modulation and high code rate is selected, so as to make full use of the channel spectrum resources and improve the system throughput. Generally speaking, in the limited channel resource environment, the technology adopted in the present invention ensures the information transmission quality and efficiency of the communication system. In particular the following points:

1. The self-adaptive algorithm module, in order to match the current service type, user requirements and channel quality of the transmission link, and achieve effective self-adaptation, it is necessary to obtain the optimal switching threshold of the coding and modulation pair through the optimization algorithm. When the decision threshold of the coding and modulation pair achieves optimal matching, the transmitter selects the coding and modulation pair for the current transmission link.

2. The system receiving end (master station) performs channel estimation on the current transmission link according to the received transmission signal, and transmits the channel state information to the transmitting end (small station) through the forward link.

3. The small station transmitter extracts the channel information from the forward link corresponding to the transmission link. Using the same channel estimation algorithm, the small station transmitter can obtain the forward link channel state information that is not affected by the feedback transmission delay. Thus, the channel information of the current transmission link is equivalently obtained.

The invention also discloses a computer program of a method for realizing deep fusion of communication, navigation and remote sensing and a storage medium for storing the computer program.

Although the present invention has been described in conjunction with preferred embodiments, it is not intended to limit the present invention, and any person skilled in the art, without departing from the spirit and scope of the present invention, can implement various aspects of the subject matter set forth herein. Therefore, the protection scope of the present invention should be determined by the scope defined by the appended claims.

Claims (8)

1. A method for realizing communication navigation remote sensing depth fusion is characterized in that:

configuring transmission selection information to enable the data information to be transmitted by the signal to configure a corresponding MCS in a self-adaptive manner according to the transmission selection information; the transmission selection information has a corresponding selection relation between a transmission signal-to-noise ratio interval and a corresponding MCS; the corresponding MCS is a code modulation pair which can realize the maximum throughput in the corresponding transmission signal-to-noise ratio interval;

the transmission selection information also comprises characteristic information extracted according to the current data link transmission information; and learning the characteristic information set, distinguishing abnormal transmission data, and reselecting the MCS for the residual data according to the switching threshold after the abnormality is found, wherein the characteristic information comprises signal strength, amplitude, and a value of a certain position of a message or a data frame.

2. The method for realizing the communication navigation remote sensing depth fusion as claimed in claim 1, which is characterized in that: the transmitting terminal obtains the channel state information and the channel signal-to-noise ratio gamma of the current transmission link, and a group of optimal switching thresholds { gamma is calculated through an optimization algorithm_iI =1, 2, …, n }; in the cyclic decision process, the decision module firstly compares gamma with gamma_NAnd when γ is greater than or equal to γ_NIf yes, the adaptive system is judged to adopt the Nth code modulation mode, otherwise, the gamma and the gamma are continuously compared_N-1Until the interval matched with the signal-to-noise ratio of the current channel and the corresponding code modulation mode are determined.

3. The method for realizing the communication navigation remote sensing depth fusion as claimed in claim 1, which is characterized in that: the system receiving end carries out channel estimation on the current transmission link according to the received transmission signal and transmits the channel state information to the transmitting end through the forward link; and the transmitting terminal selects the MCS which is suitable for the current channel and has the highest spectrum efficiency according to the transmission selection information according to the service type or the data volume to be transmitted and the intercepted channel information of the satellite downlink.

4. The method for realizing the communication navigation remote sensing depth fusion as claimed in claim 1, which is characterized in that: the receiving end carries out channel estimation on the current transmission link according to the received transmission signal and transmits the channel state information to the transmitting end through the forward link.

5. The method for realizing the communication navigation remote sensing depth fusion as claimed in claim 1, which is characterized in that: the transmitting terminal extracts channel information from a forward link corresponding to a transmission link, and selects an MCS which is suitable for a current channel and has the highest spectrum efficiency according to transmission selection information; the transmitting terminal can obtain the forward link channel state information which is not influenced by the feedback transmission delay, thereby equivalently obtaining the channel information of the current transmission link.

6. The method for realizing the communication navigation remote sensing depth fusion as claimed in claim 3, which is characterized in that: the transmitting terminal extracts the channel information from the forward link corresponding to the transmission link, and the transmitting terminal of the small station can obtain the forward link channel state information which is not influenced by the feedback transmission delay by adopting the same channel estimation algorithm.

7. Computer device, characterized by: comprising one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, the one or more computer programs comprising instructions which, when executed by the apparatus, cause the apparatus to perform the method of any of claims 1 to 6.

8. A computer storage medium characterized by: the computer storage medium stores one or more computer programs whose instructions, when executed by a processor, are capable of performing the method of any one of claims 1 to 6.

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