Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

Wang, Xianbin; Zhang, Huazi; Li, Rong; Huang, Lingchen; Dai, Shengchen; Huangfu, Yourui; Wang, Jun

doi:10.1109/pimrc.2019.8904878

Cited by 23 publications

(11 citation statements)

References 16 publications

(24 reference statements)

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“…Despite its ubiquitous use, and to the best of our knowledge, the learning approach to BF decoding presented in this paper is novel. In fact, with the exception of the recent work in [15], we were unable to find references that discuss RL for channel coding. Thus, we briefly review some other iterative decoding algorithms, based on sequential decision-making steps, for which RL is applicable.…”

Section: Introductionmentioning

confidence: 89%

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

Carpi

Häger

Martalò

et al. 2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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In this paper, we use reinforcement learning to find effective decoding strategies for binary linear codes. We start by reviewing several iterative decoding algorithms that involve a decision-making process at each step, including bitflipping (BF) decoding, residual belief propagation, and anchor decoding. We then illustrate how such algorithms can be mapped to Markov decision processes allowing for data-driven learning of optimal decision strategies, rather than basing decisions on heuristics or intuition. As a case study, we consider BF decoding for both the binary symmetric and additive white Gaussian noise channel. Our results show that learned BF decoders can offer a range of performance-complexity trade-offs for the considered Reed-Muller and BCH codes, and achieve near-optimal performance in some cases. We also demonstrate learning convergence speed-ups when biasing the learning process towards correct decoding decisions, as opposed to relying only on random explorations and past knowledge.

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Section: Introductionmentioning

confidence: 89%

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

Carpi

Häger

Martalò

et al. 2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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“…In Table II, we compare the hardware complexity of the proposed design and the LSTM design without domain knowledge, whose inputs are the LLRs of -bit message in paths and the outputs are the whole information set. The design rule of the latter design is similar to [7].…”

Section: Complexity Analysis Of Neural Network Designmentioning

confidence: 99%

“…Recently, as deep learning (DL) has many revolutionary breakthroughs in the field of computer vision and natural language processing, many researchers have also been dedicated to applying this powerful technique to enhance decoding algorithms [3]- [7]. However, their decoding capacity is still worse than the state-of-the-art CRC-assisted successive cancellation list (CA-SCL) [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity LSTM-Assisted Bit-Flipping Algorithm For Successive Cancellation List Polar Decoder

Chen

2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Although the cyclic redundancy check (CRC)assisted successive cancellation list bit-flipping (CA-SCLF) decoders have been developed to obtain a better performance, the solution to error bit correction (bitflipping) problem is still imperfect and hard to design. In this work, we leverage the expert knowledge in communication systems and adopt deep learning (DL) technique to obtain the better solution. A low-complexity long short-term memory network (LSTM)-assisted CA-SCLF decoder is proposed to further improve the performance of conventional CA-SCLF and avoid complexity and memory overhead. Our test results show that we can effectively improve the BLER performance by 0.11dB compared to prior work and reduce the complexity and memory overhead by over 30% of the network.

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“…For the SCF decoding algorithm, it is critical to accurately locate candidate flipping bits and reduce decoding delay. In order to accurately locate the candidate bits, Wang et al proposed a deep learning-assisted SCF decoding algorithm, using a long short-term memory (LSTM) network and reinforcement learning to find the error bits [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Bit-Flipping Decoding of Polar Codes

Wang

et al. 2021

Entropy

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A traditional successive cancellation (SC) decoding algorithm produces error propagation in the decoding process. In order to improve the SC decoding performance, it is important to solve the error propagation. In this paper, we propose a new algorithm combining reinforcement learning and SC flip (SCF) decoding of polar codes, which is called a Q-learning-assisted SCF (QLSCF) decoding algorithm. The proposed QLSCF decoding algorithm uses reinforcement learning technology to select candidate bits for the SC flipping decoding. We establish a reinforcement learning model for selecting candidate bits, and the agent selects candidate bits to decode the information sequence. In our scheme, the decoding delay caused by the metric ordering can be removed during the decoding process. Simulation results demonstrate that the decoding delay of the proposed algorithm is reduced compared with the SCF decoding algorithm, based on critical set without loss of performance.

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Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

Cited by 23 publications

References 16 publications

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding

Low-Complexity LSTM-Assisted Bit-Flipping Algorithm For Successive Cancellation List Polar Decoder

Reinforcement Learning for Bit-Flipping Decoding of Polar Codes

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