Efficient list decoder architecture for polar codes

Lin, Jun; Yan, Zhiyuan

doi:10.1109/iscas.2014.6865312

Cited by 41 publications

(114 citation statements)

References 6 publications

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“…Eventually, the decoded value u t is fed back into SC process in line 10 to calculate the partial sums. On the other hand, if the index of the current bit is in the set A (see lines [12][13][14][15][16][17], there are two options for the value of v t , i.e., 0 and 1, to be considered in line 23. For each option of 0 and 1, the aforementioned process for t ∈ A c including convolutional encoding, and calculating the path metric is performed and then the two encoded values u t = 0 and 1 are fed back into SC process to update the partial sums β π .…”

Section: List Viterbi Decodingmentioning

confidence: 99%

List Viterbi Decoding of PAC Codes

Rowshan¹,

Viterbo²

2020

Preprint

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<div>Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector channel to the convolutional transformation. The numerical results show that this concatenation improves the Hamming distance properties of polar codes. </div><div>In this work, we implement the parallel list Viterbi algorithm (LVA) and show how the error correction performance moves from the poor performance of the Viterbi algorithm (VA) to the superior performance of list decoding by changing the constraint length, list size, and the sorting strategy (local sorting and global sorting) in the LVA. Also, we analyze the latency of the local sorting of the paths in LVA relative to the global sorting in the list decoding and the trade-off between the sorting latency and the error correction performance.</div>

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Section: List Viterbi Decodingmentioning

confidence: 99%

List Viterbi Decoding of PAC Codes

Rowshan¹,

Viterbo²

2020

Preprint

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“…Early hardware architectures for successive cancellation decoding of polar codes were presented in [32] and [33]. Recently, hardware architectures for successive cancellation list decoders has also emerged [34], [35], [36], [37].…”

Section: Polar Codesmentioning

confidence: 99%

Polar codes for distributed source coding

Onay

2013

Electron. lett.

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Polar codes were invented by Arıkan as the first "capacity achieving" codes for binary-input discrete memoryless symmetric channels with low encoding and decoding complexity. The "polarization phenomenon", which is the underlying principle of polar codes, can be applied to different source and channel coding problems both in single-user and multi-user settings. In this work, polar coding methods for multi-user distributed source coding problems are investigated. First, a restricted version of lossless distributed source coding problem, which is also referred to as the Slepian-Wolf problem, is considered. The restriction is on the distribution of correlated sources. It is shown that if the sources are "binary symmetric" then single-user polar codes can be used to achieve full capacity region without time sharing. Then, a method for two-user polar coding is considered which is used to solve the Slepian-Wolf problem with arbitrary source distributions. This method is also extended to cover multiple-access channel problem which is the dual of Slepian-Wolf problem.Next, two lossy source coding problems in distributed settings are investigated. The first problem is the distributed lossy source coding which is the lossy version of the Slepian-Wolf problem. Although the capacity region of this problem is not known in general, there is a good inner bound called the Berger-Tung inner bound. A polar coding method that can achieve the whole dominant face of the Berger-Tung region is devised. The second problem considered is the multiple description coding problem. The capacity region for this problem is also not known in general. El Gamal-Cover inner bound is the best known bound for this problem. A polar coding method that can achieve any point on the dominant face of El Gamal-Cover region is devised.

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“…The SCL decoder has a corresponding list size L. At each decoding stage, L decoding paths are considered concurrently, and a single output codeword is selected from the list. Moreover, the cyclic redundancy check (CRC) [3,4] is adopted to select the output codeword, which makes the error-rate performance of polar codes competitive with low-density parity-check codes and turbo codes. Instead of using the greedy one-time-pass search of the classic SC decoding, a breadth-first search is adopted in the SCL decoding algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, selecting the L best paths out of 2L possible paths is a important operation of SCL decoding. However, this selecting operation may lie in the critical path of the decoder that determines the maximum operating frequency [4,5]. As a result, considerable attention is paid to developing low-complexity sorting architectures recently [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

Optimized sorting network for successive cancellation list decoding of polar codes

Wang

Feng

et al. 2017

IEICE Electron. Express

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In this paper, we propose an optimized bitonic sorting architecture and a hybrid sorting architecture for addressing the sorting problem of successive cancellation list decoders for polar codes. Since half of the 2L metrics are already sorted, lots of redundant sorting operations can be deleted. According to this property and the characteristics of the sorting network, we put forward several optimization strategies to reduce the compare-and-exchange units and the pipeline depths. For the list size L ≤ 32, the synthesis results show that the area of proposed hybrid sorter is at least 22% smaller than existing sorters, and the latency is at least 25% smaller.

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Efficient list decoder architecture for polar codes

Cited by 41 publications

References 6 publications

List Viterbi Decoding of PAC Codes

List Viterbi Decoding of PAC Codes

Polar codes for distributed source coding

Optimized sorting network for successive cancellation list decoding of polar codes

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