Belief Propagation Based Joint Detection and Decoding for Resistive Random Access Memories

Sun, Ce; Cai, Kui; Song, Guanghui; Quek, Tony Q. S.; Fei, Zesong

doi:10.1109/tcomm.2022.3156074

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…In addition, the GA has also achieved good results in polar code constructions [46][47][48][49]. In [46], the authors proposed a GA-based framework to construct polar codes using the BER as the fitness function of the GA, thereby reducing the size of the SCL list and the number of iterations.…”

Section: Ga Based Decoding Algortithmsmentioning

confidence: 99%

“…In [47], the authors proposed a scheme for constructing polar codes based on a hash table update population, which reduced the computational complexity of repeated candidates. Aiming at the joint detector and decoder over the resistive random-access memory (ReRAM) channel model, effective polar codes were constructed using the GA in [48].…”

Section: Ga Based Decoding Algortithmsmentioning

confidence: 99%

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Improving Decodability of Polar Codes by Adding Noise

et al. 2022

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This paper presents an online perturbed and directed neural-evolutionary (Online-PDNE) decoding algorithm for polar codes, in which the perturbation noise and online directed neuro-evolutionary noise sequences are sequentially added to the received sequence for re-decoding if the standard polar decoding fails. The new decoding algorithm converts uncorrectable received sequences into error-correcting regions of their decoding space for correct decoding by adding specific noises. To reduce the decoding complexity and delay, the PDNE decoding algorithm and sole neural-evolutionary (SNE) decoding algorithm for polar codes are further proposed, which provide a considerable tradeoff between the decoding performance and complexity by acquiring the neural-evolutionary noise in an offline manner. Numerical results suggest that our proposed decoding algorithms outperform the other conventional decoding algorithms. At high signal-to-noise ratio (SNR) region, the Online-PDNE decoding algorithm improves bit error rate (BER) performance by more than four orders of magnitude compared with the conventional simplified successive cancellation (SSC) decoding algorithm. Furthermore, in the mid-high SNR region, the average normalized complexity of the proposed algorithm is almost the same as that of the SSC decoding algorithm, while preserving the decoding performance gain.

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