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Deep Learning Based Blind Recognition of Channel Code Parameters

Dehdashtian, Sepehr | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53706 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Saleh Kaleybar, Saber; Hashemi, Matin
  7. Abstract:
  8. In the communication systems, the raw signals of information are mainly encoded so as to prevent the detrimental effects of channel noises and distortions. After some processes, this encoded signal is passed through the channel. At the receiver side, the received signal has to be decoded to extract the information signal. In order to decode the received signal, the receiver require prior knowledge about the encoder parameters. The traditional approach is to send the encoder parameters along with the encoded signals. However, this transmission overhead might occupy a considerable amount of bandwidth since the type of coding may alter several times in a fraction of a second based on the channel conditions. To resolve this issue, blind recognition algorithms have been proposed to recover the transmission parameters without any signaling from the transmitter. In this scenario, the receiver tries to identify the parameters merely from the received encoded signal. This thesis proposes a deep learning-based solution that I) is capable of identifying the channel code parameters for several coding schemes (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters
  9. Keywords:
  10. Convolutional Neural Network ; Deep Learning ; Fading Channel ; Multipath Fading Channel ; Blind Identification ; Code Configuration

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