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CN-122027083-A - Channel estimation and adaptive modulation coding method and system for hybrid communication

CN122027083ACN 122027083 ACN122027083 ACN 122027083ACN-122027083-A

Abstract

The invention discloses a channel estimation and self-adaptive modulation coding method and a system for hybrid communication. The method comprises the steps of dynamically optimizing a signal attenuation sequence of an RF/FSO hybrid link by adopting an optimization algorithm to obtain optimal decomposition parameters, decomposing the attenuation sequence into a plurality of intrinsic mode function IMF components representing different physical mechanisms based on the parameters, inputting the IMF components and real-time environment parameters into a deep learning network together, predicting a signal attenuation value at the next moment through feature extraction and time sequence learning, and finally dynamically selecting an optimal modulation coding scheme through a classification network according to the predicted value and a preset modulation coding scheme library to realize self-adaptive switching. The invention solves the problems of poor adaptability and low estimation precision of the traditional method in a complex time-varying channel by combining parameter self-adaptive optimization with deep learning prediction, and realizes the integrated improvement of channel estimation accuracy, transmission reliability and spectrum utilization rate.

Inventors

  • TAN RAN
  • YANG RUIKE

Assignees

  • 西安电子科技大学

Dates

Publication Date
20260512
Application Date
20260202

Claims (10)

  1. 1. A method for channel estimation and adaptive modulation and coding for hybrid communications, comprising the steps of: Step 1, dynamically optimizing key decomposition parameters of a signal decomposition algorithm by adopting an optimization algorithm for a signal attenuation sequence of a mixed communication link of radio frequency RF and free space optical FSO at the current moment to obtain optimal decomposition parameters; Step 2, decomposing the signal attenuation sequence into a plurality of intrinsic mode function IMF components through the signal decomposition algorithm based on the optimal decomposition parameters; Step 3, inputting the IMF component and the environmental parameters acquired in real time together into a deep learning network model, and predicting a signal attenuation value at the next moment by extracting channel characteristics and learning a time sequence evolution rule thereof; and 4, dynamically selecting a modulation coding scheme based on the predicted signal attenuation value and combining a preset coding scheme library to realize self-adaptive modulation coding switching.
  2. 2. The method of claim 1, wherein the signal decomposition algorithm in step 1 is a time-domain filtered empirical mode decomposition algorithm, and the key decomposition parameters include a bandwidth threshold and B-spline order.
  3. 3. The method for channel estimation and adaptive modulation and coding for hybrid communication according to claim 2, wherein the method for dynamic optimization of the key decomposition parameters in step 1 is as follows: s1, constructing an objective function, wherein the objective function is a weighted sum of signal decomposition delay, envelope entropy and channel component correlation, wherein the envelope entropy is used for evaluating the spectrum separation performance of signal decomposition, and the channel component correlation is used for evaluating the correlation between an inherent mode function obtained by decomposition and an RF/FSO actual channel component; S2, adopting a snake-gret optimization algorithm, and dynamically adjusting a bandwidth threshold value and a B spline order of a time domain filtering empirical mode decomposition algorithm based on the objective function through an iteration mechanism combining global search and local search; s3, in each iteration, calculating the objective function value according to the current parameter, and updating the searching direction and the parameter value by taking the minimized value as an optimization target; and S4, stopping optimizing when the preset iteration times or the objective function value is converged to the threshold value, and outputting the key decomposition parameters.
  4. 4. A method of channel estimation and adaptive modulation and coding for hybrid communications according to claim 3 wherein said objective function is expressed as follows: Wherein, the Is the time for which the signal is decomposed, Is the entropy of the envelope, Representing the correlation of the RF/FSO channel components.
  5. 5. The method for channel estimation and adaptive modulation and coding for hybrid communication according to claim 1, wherein in step 2, the synchronous decomposition is performed in the time domain and the frequency domain according to the physical mechanism of the signal attenuation sequence; Decomposing the RF attenuation signal to obtain three IMF components which correspond to the rainfall attenuation low-frequency component, the rainfall attenuation high-frequency component and the multipath attenuation component respectively; The FSO attenuation signal is decomposed to obtain five IMF components, which correspond to the directional error attenuation component, the rainfall attenuation low-frequency component, the large-scale turbulence attenuation component, the rainfall attenuation high-frequency component and the small-scale turbulence attenuation component respectively.
  6. 6. The method for channel estimation and adaptive modulation and coding for hybrid communication according to claim 1, wherein the deep learning network model is a CNN-BIGRU-Attention network, and the prediction method is as follows: And inputting the IMF component and the environmental parameter into a network, sequentially extracting spatial channel characteristics through a convolution layer, learning a channel evolution rule through combining time sequence information through a bidirectional gating circulating unit layer, enhancing key characteristic weights through a attention mechanism, integrating the characteristics through a full connection layer, and outputting signal attenuation values of RF and FSO links at the next moment.
  7. 7. The method of claim 1, wherein the predetermined coding scheme library in step 4 comprises an RF modulation coding scheme library and an FSO modulation coding scheme library, each comprising a plurality of modulation coding schemes, each modulation coding scheme being associated with a signal attenuation threshold interval.
  8. 8. The method for channel estimation and adaptive modulation and coding for hybrid communication according to claim 1, wherein said adaptive modulation and coding switching comprises the steps of: Determining a signal attenuation threshold interval which belongs to the predicted signal attenuation value in a preset RF modulation coding scheme library and an FSO modulation coding scheme library according to the predicted signal attenuation value; calculating transition probability of switching from the current modulation coding scheme to each candidate modulation coding scheme through a deep learning classification network; and dynamically selecting an optimal modulation coding scheme from the coding scheme library based on the maximum transition probability and switching the optimal modulation coding scheme so as to maximize the spectrum utilization rate under the condition of meeting the target error rate.
  9. 9. A system for channel estimation and adaptive modulation and coding for hybrid communications, comprising: The decomposition parameter optimization module is used for dynamically optimizing key decomposition parameters of a signal decomposition algorithm by adopting an optimization algorithm to obtain optimal decomposition parameters for a signal attenuation sequence of a Radio Frequency (RF) and Free Space Optical (FSO) mixed communication link at the current moment; the signal decomposition module is used for decomposing the signal attenuation sequence into a plurality of Intrinsic Mode Function (IMF) components through the signal decomposition algorithm based on the optimal decomposition parameters; The prediction module is used for inputting the IMF component and the environmental parameter acquired in real time together and inputting the IMF component and the environmental parameter acquired in real time together into the deep learning network model, and predicting a signal attenuation value at the next moment by extracting channel characteristics and learning a time sequence evolution rule thereof; And the coding switching module is used for dynamically selecting a modulation coding scheme based on the predicted signal attenuation value and combining a preset coding scheme library to realize self-adaptive modulation coding switching.
  10. 10. An electronic device, comprising: A memory for storing a computer program; A processor for implementing the steps of the channel estimation and adaptive modulation coding method for hybrid communication according to any of claims 1-8 when executing said computer program.

Description

Channel estimation and adaptive modulation coding method and system for hybrid communication Technical Field The invention relates to the technical field of wireless communication, in particular to a channel estimation and self-adaptive modulation coding method and system for hybrid communication. Background Among wireless communication, radio frequency communication has become a mainstream communication means due to its mature technical system and wide application. But due to the increasing shortage of radio frequency spectrum resources, communication network spectrum resources are scarce. Second, the transmission rate of radio frequency communications is relatively low. In addition, the single RF link has poor performance under some extreme meteorological conditions, and in order to solve the problems, the free space optical communication (FSO) communication adopts laser or infrared light as a transmission signal, has the advantages of extremely high bandwidth, low delay, almost unlimited frequency spectrum resources and the like, however, the FSO communication is only suitable for a Line of Sight (LOS) link, and the stability of the FSO link is limited by a carrying platform of the FSO link. Finally, a single FSO link is also subject to severe weather factors such as rain, fog, turbulence, etc. To address the respective deficiencies of RF and FSO communications, RF/FSO hybrid communication systems are a potential solution. The RF/FSO hybrid communication system combines long-distance propagation and stability of RF communication with high-bandwidth and low-delay advantages of FSO communication to make up for the deficiency of a single communication link, but in practical application, the RF/FSO hybrid communication system often faces complex application scenarios and weather conditions. If the RF/FSO hybrid communication is to be enabled to implement an intelligent switching mechanism according to the dynamic change of the environment to improve the reliability, the method greatly depends on the adaptive design of the system, i.e. channel estimation and adaptive modulation coding. In recent years, deep learning builds an end-to-end mapping relation in a data driving mode, breaks through the constraint of a traditional model, and shows remarkable advantages in the field of channel estimation. For example, in a large-scale MIMO system, the computational complexity of the traditional algorithm increases exponentially due to the rapid increase of the channel matrix dimension, and the Convolutional Neural Network (CNN) realizes high-efficiency dimension reduction through local perception and weight sharing, so that the time correlation of a time-varying channel can be captured by the cyclic neural network (RNN) and the long-short-period memory network (LSTM) in a high-speed moving scene, the estimation robustness is improved, and the super-resolution reconstruction of a millimeter wave channel can be realized by combining the generation of the countermeasure network (GAN) and the compressed perception technology in a low-pilot overhead scene. Adaptive Modulation and Coding (AMC) technology plays a vital role in modern wireless communication systems to improve the throughput and reliability of the system by dynamically adjusting the modulation and coding scheme according to the channel conditions. The conventional AMC method typically relies on CSI to adjust, however, this method may face performance bottlenecks in complex and dynamic wireless channel environments. Disclosure of Invention Aiming at the problems existing in the prior art, the invention provides a channel estimation and self-adaptive modulation coding method and a system for mixed communication, and the channel estimation algorithm aims at minimizing the comprehensive cost of prediction error and time delay and ensuring the high efficiency and accuracy under a complex time-varying environment. The invention is realized by the following technical scheme: In a first aspect, the present application provides a method for channel estimation and adaptive modulation and coding for hybrid communications, comprising the steps of: Step 1, dynamically optimizing key decomposition parameters of a signal decomposition algorithm by adopting an optimization algorithm for a signal attenuation sequence of a mixed communication link of radio frequency RF and free space optical FSO at the current moment to obtain optimal decomposition parameters; Step 2, decomposing the signal attenuation sequence into a plurality of intrinsic mode function IMF components through the signal decomposition algorithm based on the optimal decomposition parameters; Step 3, inputting the IMF component and the environmental parameters acquired in real time together into a deep learning network model, and predicting a signal attenuation value at the next moment by extracting channel characteristics and learning a time sequence evolution rule thereof; and 4, dynamically selecting a modulat