Adaptive Modulation and Coding in Satellite-Integrated 5G Communication System

Chen, Dechao; Zhang, Jingwen; Zhao, Rui

doi:10.1109/icct52962.2021.9658103

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…In recent years, the combination of neural networks and coding blind recognition has progressed rapidly [14][15][16][17]. It has been proven that both of the coding schemes and the coding parameter are able to be identified using neural networks [14].…”

Section: Introductionmentioning

confidence: 99%

“…Two types of LDPC codes can be identified in [15], while a 2-dimensional convolution neural network is used to identify the parity-check matrix of LDPC codes with the help of a candidate set [16]. Moreover, a joint modulation and channel coding recognition framework is proposed in [17] for the practical 5G-PDSCH protocols using the novel Res-Inception convolutional NN and the algorithm based on maximal ALLR. Inspired by the deep learning model used in NLP, we propose a novel method based on Transformer for recognizing the coding parameter of LDPC codes.…”

Section: Introductionmentioning

confidence: 99%

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A Cascade Network for Blind Recognition of LDPC Codes

Zhang

2023

Electronics

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Coding blind recognition plays a vital role in non-cooperative communication. Most of the algorithm for coding blind recognition of Low Density Parity Check (LDPC) codes is difficult to apply and the problem of high time complexity and high space complexity cannot be solved. Inspired by deep learning, we propose an architecture for coding blind recognition of LDPC codes. This architecture concatenates a Transformer-based network with a convolution neural network (CNN). The CNN is used to suppress the noise in real time, followed by a Transformer-based neural network aimed to identify the rate and length of the LDPC codes. In order to train denoise networks and recognition networks with high performance, we build our own datasets and define loss functions for the denoise networks. Simulation results show that this architecture is able to achieve better performance than the traditional method at a lower signal-noise ratio (SNR). Compared with the existing methods, this approach is more flexible and can therefore be quickly deployed.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cascade Network for Blind Recognition of LDPC Codes

Zhang

2023

Electronics

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“…With the development of satellite constellations in recent years, [9], AMC has been used for satellite-to-ground communications within complicated and variable channel environments [10,11]. AMC adjusts the modulation and coding scheme according to the varying channel conditions [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Weather-Conscious Adaptive Modulation and Coding Scheme for Satellite-Related Ubiquitous Networking and Computing

Zhang

et al. 2022

Electronics

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As a crucial part of ubiquitous networking and computing (UNC) technologies, low earth orbit (LEO) satellite communications aim at providing internet connectivity services everywhere. To improve the spectrum efficiency of satellite-to-ground communications, adaptive modulation and coding (AMC) are widely used, which can adjust the modulation and coding types according to the varying channel condition. However, satellite-to-ground communication channels have the characterizations such as fast dynamic change, fast switching, and significant fading. These characterizations make it challenging to predict the channel state information accurately and, thus, to perform accurate AMC. For example, rain loss is one of the crucial factors in satellite-to-ground channel fading. In general, it is difficult to build an integrated global model for rain loss because it varies in different regions around the world. Moreover, for the emerging applications of multiple antennas on satellites, the conventional look-up table method cannot cope with the high-dimensional inputs of the multiple antennas. To tackle the above challenges, we propose an AMC method based on deep learning (DL) and deep reinforcement learning (DRL) for ubiquitous satellite-to-ground networks. The proposed method directly processes real-time global weather and location information in the environment and intelligently selects encoding schemes to maximize system throughput. Simulation results show that the proposed method can increase the total throughput. The total number of correctly transmitted bits per unit time is improved, and the efficiency of the satellite-to-ground communication is enhanced.

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