Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network

Mei, Fan; Chen, Hong; Lei, Yingke

doi:10.3390/s21113884

Cited by 9 publications

(5 citation statements)

References 25 publications

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“…An RNN can process a sequence of arbitrary length by recursively applying a transition function to the internal hidden state vector of the input sequence, in addition to the fact that RNNs have been shown to perform well for FEC coding on receivers that may not know the channel coding types previously, such as cognitive radio [24]. In this sense, a technique based on RNNs is chosen for error correction in a regenerative satellite.…”

Section: Fec In Regenerative Payloadmentioning

confidence: 99%

Onboard Processing in Satellite Communications Using AI Accelerators

et al. 2023

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Satellite communication (SatCom) systems operations centers currently require high human intervention, which leads to increased operational expenditure (OPEX) and implicit latency in human action that causes degradation in the quality of service (QoS). Consequently, new SatCom systems leverage artificial intelligence and machine learning (AI/ML) to provide higher levels of autonomy and control. Onboard processing for advanced AI/ML algorithms, especially deep learning algorithms, requires an improvement of several magnitudes in computing power compared to what is available with legacy, radiation-tolerant, space-grade processors in space vehicles today. The next generation of onboard AI/ML space processors will likely include a diverse landscape of heterogeneous systems. This manuscript identifies the key requirements for onboard AI/ML processing, defines a reference architecture, evaluates different use case scenarios, and assesses the hardware landscape for current and next-generation space AI processors.

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Section: Fec In Regenerative Payloadmentioning

confidence: 99%

Onboard Processing in Satellite Communications Using AI Accelerators

et al. 2023

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“…In the above formula, m is the oversight bit, m=n-k. Take (7,4) Hamming code as an example to illustrate the coding principle of Hamming code. Suppose the resulting matrix G is shown in Equation (2).…”

Section: Coding Principlementioning

confidence: 99%

“…In the transmission of digital signals, errors often occur in the transmitted data stream due to various reasons, resulting in image hopping, discontinuity, mosaic, and other phenomena at the receiving end [1]. Therefore, through the channel coding process, the digital stream is processed accordingly, so that the system has certain error correction and anti-interference capabilities, which can greatly avoid the occurrence of code errors in the transmission of code streams [2].…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of several common error correction codes

2023

ACE

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With the rapid development of information technology and advances in science and technology, people are constantly interacting with data and coding. Error correction codes are integral to ensuring the security, robustness, and accuracy of information and systems, used not only in various engineering fields but in our daily lives as well. Systems that incorporate error correction codes have been proven to perform significantly better than those that do not, which makes it essential to understand the principles behind coding, encoding techniques, and their performance in specific domains.This paper focuses on three widely used error correction codes: Hamming code, RS code, and convolutional code. By introducing each code separately and evaluating their performance in different practical areas using proper simulations and relevant research results, this paper provides a comprehensive understanding of the vital role of error correction codes in channel encoding. Each code has its strengths, weaknesses, and specific applications. Hamming code, for example, is suitable for correcting bit errors in computer memories and communication systems. RS code is widely used in digital storage and broadcast systems due to its high level of error correction capability. Convolutional code is ideal for correcting bit-errors in continuous channels, making it an excellent choice for wireless communication systems. Overall, the results demonstrate that these error correction codes are crucial to guaranteeing data transmission integrity and system robustness. Understanding their principles and performance can guide future research and development of coding techniques in advancing technological progress and improving our daily lives.

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“…While working with time-series data (like the stock market), an attention mechanism can be utilized that can divide the given data into parts so that decoder can utilize specific parts while generating new values. Figure 1 shows the generalized RNN architecture [33].…”

Section: Simple Rnn Modelmentioning

confidence: 99%

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

Dey¹,

Hossain²,

Hossain

et al. 2021

Algorithms

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Investors in the stock market have always been in search of novel and unique techniques so that they can successfully predict stock price movement and make a big profit. However, investors continue to look for improved and new techniques to beat the market instead of old and traditional ones. Therefore, researchers are continuously working to build novel techniques to supply the demand of investors. Different types of recurrent neural networks (RNN) are used in time series analyses, especially in stock price prediction. However, since not all stocks’ prices follow the same trend, a single model cannot be used to predict the movement of all types of stock’s price. Therefore, in this research we conducted a comparative analysis of three commonly used RNNs—simple RNN, Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU)—and analyzed their efficiency for stocks having different stock trends and various price ranges and for different time frequencies. We considered three companies’ datasets from 30 June 2000 to 21 July 2020. The stocks follow different trends of price movements, with price ranges of $30, $50, and $290 during this period. We also analyzed the performance for one-day, three-day, and five-day time intervals. We compared the performance of RNN, LSTM, and GRU in terms of R2 value, MAE, MAPE, and RMSE metrics. The results show that simple RNN is outperformed by LSTM and GRU because RNN is susceptible to vanishing gradient problems, while the other two models are not. Moreover, GRU produces lesser errors comparing to LSTM. It is also evident from the results that as the time intervals get smaller, the models produce lower errors and higher reliability.

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Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network

Cited by 9 publications

References 25 publications

Onboard Processing in Satellite Communications Using AI Accelerators

Onboard Processing in Satellite Communications Using AI Accelerators

Performance analysis of several common error correction codes

Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains

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