XJ-BP: Express Journey Belief Propagation Decoding for Polar Codes

Xu, Jingwei; Che, Tiben; Choi, Gwan

doi:10.1109/glocom.2014.7417316

Cited by 19 publications

(26 citation statements)

References 12 publications

(27 reference statements)

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“…x 3 2) Right-to-left propagation: The L-messages are updated starting from the rightmost stage (i.e., the stage of channel information) until reaching the leftmost stage. The output from two nodes becomes the input to a specific neighboring processing element (PE) (for more details we refer to [15]). One PE updates the Land R-messages as follows [15]:…”

Section: Polar Codesmentioning

confidence: 99%

Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

Cammerer

Gruber

Hoydis

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

179

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The training complexity of deep learning-based channel decoders scales exponentially with the codebook size and therefore with the number of information bits. Thus, neural network decoding (NND) is currently only feasible for very short block lengths. In this work, we show that the conventional iterative decoding algorithm for polar codes can be enhanced when sub-blocks of the decoder are replaced by neural network (NN) based components. Thus, we partition the encoding graph into smaller sub-blocks and train them individually, closely approaching maximum a posteriori (MAP) performance per sub-block. These blocks are then connected via the remaining conventional belief propagation decoding stage(s). The resulting decoding algorithm is non-iterative and inherently enables a highlevel of parallelization, while showing a competitive bit error rate (BER) performance. We examine the degradation through partitioning and compare the resulting decoder to state-of-theart polar decoders such as successive cancellation list and belief propagation decoding.

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Section: Polar Codesmentioning

confidence: 99%

Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

Cammerer

Gruber

Hoydis

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

179

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“…For the proposed concatenated LDPC-Polar code, round-trip scheduling [11] is employed. For the extended factor graph shown in Fig.…”

Section: Scheduling Scheme For Concatenated Ldpc-polar Codementioning

confidence: 99%

Concatenated LDPC-polar codes decoding through belief propagation

Abbas

Fan

Chen

et al. 2017

2017 IEEE International Symposium on Circuits and Systems (ISCAS)

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Abstract-Owing to their capacity-achieving performance and low encoding and decoding complexity, polar codes have drawn much research interests recently. Successive cancellation decoding (SCD) and belief propagation decoding (BPD) are two common approaches for decoding polar codes. SCD is sequential in nature while BPD can run in parallel. Thus BPD is more attractive for low latency applications. However BPD has some performance degradation at higher SNR when compared with SCD. Concatenating LDPC with Polar codes is one popular approach to enhance the performance of BPD , where a short LDPC code is used as an outer code and Polar code is used as an inner code. In this work we propose a new way to construct concatenated LDPC-Polar code, which not only outperforms conventional BPD and existing concatenated LDPC-Polar code but also shows a performance improvement of 0.5 dB at higher SNR regime when compared with SCD.

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“…where L (û i ) and L (x i ) are the LLRs of the estimated message and the estimated transmitted codeword, respectively [14], [15], [16].…”

Section: Belief Propagation Decoder Of Polar Codesmentioning

confidence: 99%

Improving Belief Propagation decoding of polar codes using scattered EXIT charts

Elkelesh

Ebada

Cammerer

et al. 2016

2016 IEEE Information Theory Workshop (ITW)

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Abstract-For finite length polar codes, channel polarization leaves a significant number of channels not fully polarized. Adding a Cyclic Redundancy Check (CRC) to better protect information on the semi-polarized channels has already been successfully applied in the literature, and is straightforward to be used in combination with Successive Cancellation List (SCL) decoding. Belief Propagation (BP) decoding, however, offers more potential for exploiting parallelism in hardware implementation, and thus, we focus our attention on improving the BP decoder. Specifically, similar to the CRC strategy in the SCL-case, we use a short-length "auxiliary" LDPC code together with the polar code to provide a significant improvement in terms of BER. We present the novel concept of "scattered" EXIT charts to design such auxiliary LDPC codes, and achieve net coding gains (i.e. for the same total rate) of 0.4dB at BER of 10 −5 compared to the conventional BP decoder.

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XJ-BP: Express Journey Belief Propagation Decoding for Polar Codes

Cited by 19 publications

References 12 publications

Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

Scaling Deep Learning-Based Decoding of Polar Codes via Partitioning

Concatenated LDPC-polar codes decoding through belief propagation

Improving Belief Propagation decoding of polar codes using scattered EXIT charts

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