Neural Network Based Successive Cancellation Decoding Algorithm for Polar Codes in URLLC

Zhang, Ruiyi; Liu, Fangfang; Zeng, Zhimin; Shang, Qingqing; Zhao, Shulun

doi:10.1109/iswcs.2019.8877103

Cited by 1 publication

(1 citation statement)

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“…In view of the high computational complexity of the BP algorithm, in [21] [22] deep learning-based minimum sum decoding algorithms were proposed, which reduced the computational complexity and improved the decoding speed. In terms of Polar code decoding, there are also many works that use deep learning to improve the performance of Polar code decoding [23]- [26], reduce the delay of decoding [27]- [29], and facilitate hardware implementation [30]- [32].…”

Section: B Related Workmentioning

confidence: 99%

DeepReceiver: A Deep Learning-Based Intelligent Receiver for Wireless Communications in the Physical Layer

Zheng¹,

Chen²,

Yang³

2021

IEEE Trans. Cogn. Commun. Netw.

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A canonical wireless communication system consists of a transmitter and a receiver.The information bit stream is transmitted after coding, modulation, and pulse shaping. Due to the effects of radio frequency (RF) impairments, channel fading, noise and interference, the signal arriving at the receiver will be distorted. The receiver needs to recover the original information from the distorted signal. In this paper, we propose a new receiver model, namely DeepReceiver, that uses a deep neural network to replace the traditional receiver's entire information recovery process. Specifically, we design a one-dimensional convolution DenseNet (1D-Conv-DenseNet) structure and propose to use multiple binary classifiers at the final classification layer to achieve multi-bit information stream recovery. Simulation results show that the proposed DeepReceiver performs better than traditional step-by-step serial hard decision receiver in terms of bit error rate under noise, RF impairments, multipath fading, and cochannel interference.

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