Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions
Sepehr Dehdashtian, Matin Hashemi, Saber Salehkaleybar
Abstract
We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for several coding scheme (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.
Topics & Concepts
Polar codeComputer scienceAdditive white Gaussian noiseChannel (broadcasting)FadingAlgorithmDecoding methodsCode (set theory)Coding (social sciences)Set (abstract data type)Channel codeProbability of errorChannel state informationTheoretical computer scienceBinary erasure channelScheme (mathematics)Robustness (evolution)Source codeViterbi algorithmSignal-to-noise ratio (imaging)State (computer science)Convolutional codeRayleigh fadingRepetition codeSpeech recognitionPattern recognition (psychology)Encoding (memory)Variable-length codeBinary symmetric channelCode division multiple accessError Correcting Code TechniquesWireless Signal Modulation ClassificationAdvanced Wireless Communication Techniques