^{1}

^{2}

^{1}

^{1}

^{2}

A method for satellite downlink signal detection based on a generative adversarial network is proposed. The generator adversarial network and adversarial network are established, respectively. The generator network realizes the local generator of satellite signals, and the adversarial network is used for high-precision signal detection. The error network is generated by the error signal to form the satellite link downlink. The network reconstructs the optimal weights by generating errors, forms an error matrix for different satellite downlink, and then forms an adaptive matrix weight adjustment. Through the reconstruction of the optimal detection matrix, detection for the downlink signals of multiple satellites is completed. The proposed generative adversarial network can realize the high-precision detection for the downlink signal.

Due to the rapid development of AI technology, technologies are used in signal detection of the satellite to ground links. For the typical satellite to ground downlink, due to the low-orbit satellite, the relatively large-scale movement introduces a Doppler frequency shift. Doppler frequency will occur between the users and the low-orbit satellites. Downlink for low-orbit satellite mobile communication system is sensitive to carrier frequency offset, which seriously destroys the orthogonality between subcarriers and causes distortion to the receiver. A broadband signal detection technology is developed from AI technology. AI technology based on deep learning is applied in various fields, such as inserting and extracting knowledge [

For communication systems, Gaur and Ingram [

For the research of deep learning neural networks, Ghamisi et al. [

Yuan et al. [

For data classification and detection, the above methods do not use deep networks. Bengio et al. [

Deep learning networks, such as deep CNN networks [

The biggest advantage of deep learning is that it can realize the complex nonlinear function approximation of massive data through nonlinear network architecture, then characterize the distribution, and form the ability to learn the essence of data features.

If the features of the data change or the types of data expand, the ability for deep learning describing massive data becomes weaker. For multisatellite downlink signal detection, the multisatellite downlink channel variety is complex. For multisatellite downlink service scenarios, the model established by deep learning is not universal, and the model of each communication type changes. So the number of variables that deep learning can provide is limited, and the number of layers of the deep network is also limited.

Secondly, deep learning requires excessively high-quality training data. The accuracy of data analysis increases as the training data increase. In satellite downlink, high-quality training data cannot be obtained in many communication scenarios, so poor quality data could not be formed for deep learning to obtain a general effective model.

How to use a small amount of training data and establish reliable multisatellite downlink detection in different scenarios is important. In this paper, we use the GAN network to overcome this difficulty in satellite downlink. Zhang et al. [

GAN network is a model that includes the G generator network part and the D network part [

In order to improve the performance of the GAN network, in [

In the optimization process, in [

For multisatellite downlink signal detection, high-quality training data cannot be obtained in many downlink scenarios. The models established by traditional deep learning are not universal, so the model of the downlink data type varies with different channel models. But deep learning requires too much quality for training data. Therefore, we propose the GAN network. The main goal is using few data training information to form through adversarial generation network under poor channel conditions and then to achieve efficient and high-precision signal detection.

Figure

Diagram for multisatellite downlink transmission network.

Due to the high-speed movement, define multipath propagation delay as

The amplitude

In the tapped delay line method to establish a channel model of a satellite-ground link, the multipath channel impulse response is composed of multiple paths with different delay characteristics, and each path has specific signal amplitude fading and power spectrum characteristics. The proposed model is measured with broadband satellite downlink channels in the wilderness, rural, and urban environments, with the signal carrier frequency of 1.02 GHz. Tables

Channel model parameter in the wilderness environment.

Tap | Distribution function | Parameter | Parameter distribution | Value (dB) | Delay (ns) |
---|---|---|---|---|---|

1 | LOS Rician | Rician factor | 6.3 | 0 | |

NLOS Rayleigh | Average multipath power | −9.5 | |||

2 | Rayleigh | Average multipath power | −24.1 | 100 |

Channel model parameter in the rural environment.

Tap | Distribution function | Parameter | Parameter distribution | Value (dB) | Delay (ns) |
---|---|---|---|---|---|

1 | LOS Rician | Rice factor | 5.3 | 0 | |

NLOS Rayleigh | Average multipath power | −12.1 | |||

2 | Rayleigh | Average multipath power | −17.0 | 60 | |

3 | Rayleigh | Average multipath power | −18.3 | 100 | |

4 | Rayleigh | Average multipath power | −19.1 | 130 | |

5 | Rayleigh | Average multipath power | -22.1 | 250 |

Channel model parameter in the urban environment.

Tap | Distribution function | Parameter | Parameter distribution | Value (dB) | Delay (ns) |
---|---|---|---|---|---|

1 | LOS Rician | Rician factor | 9.7 | 0 | |

NLOS Rayleigh | Average multipath power | −7.3 | |||

2 | Rayleigh | Average multipath power | −17.6 | 30 | |

3 | Rayleigh | Average multipath power | −18.3 | 180 | |

4 | Rayleigh | Average multipath power | −19.3 | 60 | |

5 | Rayleigh | Average multipath power | −22.1 | 100 | |

6 | Rayleigh | Average multipath power | −25.3 | 190 | |

7 | Rayleigh | Average multipath power | −28.1 | 250 | |

8 | Rayleigh | Average multipath power | −29.1 | 270 |

Figures

Signal amplitude for each tap (wilderness environment).

Doppler power spectrum for each tap (wilderness environment).

Signal amplitude for each tap (rural environment).

Doppler power spectrum for each tap (rural environment).

Signal amplitude for each tap (urban environment).

Doppler power spectrum for each tap (urban environment).

Due to different satellite simulation, multipath delays of the channels are different, and the signal amplitude fading is completely different. The first path of each simulation scenario has a large impulse response corresponding to the amplitude. This is because the first path has a direct component, and its envelope corresponds to the Rician channel density distribution. For rural simulation scenarios, the reflection of signals from more buildings and the diffraction effect cause a larger number of multipaths, and the extended delay of the received signal is larger, which makes the signal get more severe fading. For the rural simulation scene, compared with the urban scene, the number of buildings is small. The reflection and refraction phenomena are reduced compared to the urban simulation environment, so the number of multipaths is reduced, and the channel fading is flat compared to the urban environment. For urban simulation, because the number of buildings is smaller than the number of urban simulation, and the number of vegetation is reduced compared to the rural environment, the reflection and refraction phenomena are reduced compared to urban and rural areas, and channel fading is also more difficult than urban environments.

Figure

Multisatellite model for the downlink communication system.

Because of receiving multiple satellite signals, the relative frequency offset factors of satellites are also different. When the number of system subcarriers is determined, access interference will be introduced due to differences of multiple satellites. Figure

Define the relative frequency offset factor

The first term is the interference between communication symbols, the second term is the interference introduced by communication access, and the third term is the interference introduced by Gaussian white noise. The multiple satellite signals received by the downlink ground user can be expressed as follows:

Figure

Information processing flow based on the GAN algorithm.

We define the generalized loss function model as follows:

In order to obtain the signal detection of the satellite downlink, we implement the optimal weight

We introduce a generative adversarial network loss cost function to identify multisatellite downlink data. The cost function of the GAN can be expressed as follows:

From formula (

The generator network

GAN network model information processing flow for the satellite signal.

Figure

Figure

Signal processing flow based on the adversarial network.

The process of generating regularization terms is as follows. Define

The first generator network is also satisfied for formula (

The error of the generator network is satisfied, which is to satisfy

The spatial distance can be expressed as follows:

Further simplification is as follows:

Establish the extraction direction of the discriminator feature space, and we could obtain as follows:

Further, the weight can be obtained as follows through GAN network:

Furthermore, we establish an error network as follows:

Through extensibility analysis, further calculations can be obtained:

To further obtain an optimized representation of the weights for GAN network,

Figure

Flow for weight

The meaning of the parameters in Figure

The weight of the GAN network includes the process of generator weight

The generator space

nonlinear processing and sparse coding introduce the nonlinearization to the learning target, especially due to the noise interference.. Regularization becomes complicated, especially for the feature transformation of the projection space, and it is difficult to regularize in the feature space. Therefore, we use a simplified method to replace and reduce the impact of noise. The sparse distance loss function adopted can obtain better target features.

Further, optimization is available as follows:

Further,

Because rank minimization is an NP problem, it is necessary to obtain optimal convergence in the feature space. In particular, the loss function can be expressed by minimizing the rank of the matrix. By completing the operation of the minimum rank of the feature transformation matrix, the objective loss of the function is to obtain the optimal recognition. In order to improve the structural consistency of the cost function and reduce the influence of sampling interference, the cost loss function gives the low-rank optimization method.

Discussion: we should optimize the objective solution. Among them,

To construct an optimization method, convex theory seeks optimization.

In order to verify the detection performance of the proposed GAN algorithm, the average altitude for the satellite is defined as 1450 km, the number of low-orbit satellites is defined as 10, and the spot beams were defined as 7. The parameters for low-orbit satellites are set in accordance with the wilderness model, rural model, and the urban model which are established in the simulation scenarios. Define the maximum working elevation angle of satellite as 35°. Define the downlink for low-orbit satellite transmission bit rate as 160 Mbit/s. We set that the cyclic prefix is greater than the spread delay. We also set the modulation as QPSK.

The proposed adversarial network algorithm can be verified by Matlab. The maximum frequency deviation range allowed as the interval of subcarrier. Figures

BER performance of QPSK signal in the rural environment channel model.

BER performance of QPSK in the urban environment channel model.

QPSK signal constellation (after the MMSE algorithm in [

The conditions in Figures

BER performance of QPSK signal in the wilderness environment channel model.

The downlink multisatellite detection based on the generative adversarial network can be obtained through the simulation of QPSK constellation, which is still selected as the signal mapping. Assuming SNR = 15 dB, the downlink received signal is defined in an urban environment. Figure

QPSK signal constellation (after the ML-SIC algorithm in [

QPSK signal constellation (after the OSIC algorithm in [

QPSK signal constellation (after the GAN algorithm proposed).

In order to verify the integrity of the three networks more effectively and reflect the differentiation of simulation, we use the simulation of 16QAM constellation for the downlink multisatellite detection. Assuming SNR = 10 dB, the downlink received signal is defined in an urban environment.

Figure

16QAM signal constellation (undetected multisatellite signal).

16QAM signal constellation (detected with no generator network).

16QAM signal constellation (detected with no error update network).

16QAM signal constellation (detected with GAN network).

Figure

Analysis of stability based on the proposed algorithm.

In this paper, we have proposed the method for satellite downlink signal detection based on the GAN network. We establish the generator network and adversarial network, respectively. The generator network is established with the local generator of virtual satellite signals, and the adversarial network is established for high-precision signal detection. And we also have established the error network with the error signal from satellite downlink. Then we form an adaptive matrix weight adjustment. Compared with traditional shallow networks, such as MMSE, ML-SIC algorithms, and iterative algorithms, under the same signal-to-noise ratio, the performance is improved by 5 dB.

The data used to support the findings of this work are available from the corresponding author upon request.

The authors declare that they have no conflicts of interest.

Qingyang Guan and Shuang Wu contributed equally to this work.

This work was supported by the National Natural Science Foundation of China (no. 61501306), Scientific Research Initiation Funds for the Doctoral Program of Xi’an International University (Grant nos. XAIU2019002 and XAIU2018070102), General Project of Science and Technology Department of Shaanxi Province (Grant no. 2020JM-638), and the Natural Science Foundation of Liaoning Province of China (no. 2015020026).

_{2,0}norm approximation