Polarization Weight Family Methods for Polar Code Construction

Yi-jie, Zhou; Li, Rong; Zhang, Huazi; Luo, Hejia; Wang, Jun

doi:10.1109/vtcspring.2018.8417498

Cited by 16 publications

(10 citation statements)

References 11 publications

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“…For BEC channels, the iterative calculations of Bhattacharyya parameters in [1] are performed to select the information set. For comparison, the PW construction with β = 2 1/4 in [7] is also used in the construction. The information set is fixed once selected.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In the case that j ∈ Q ′ n and j ∈ I, the puncturing set Q ′ renders an information bit channel j to be a punctured channel, which bears an error probability P b (W (j) N ) = 1/2. Seen from (7), the union bound P B (Q ′ ) > 1/2. Therefore, P B (Q ′ ) ≥ P B (Q) considering the fact that Q n ⊆ F and j ∈ Q ′ n is possibly in the set I.…”

Section: B Optimal Puncturing Patternmentioning

confidence: 99%

“…Assume the underlying channel is a BEC with an erasure probability 0.5. Then the bit channel quality is sorted in the descending order of: [7,6,5,3,4,2,1,0]. With Q = 4 coded symbols to be punctured, WQP selects P 0 = {0, 1, 2, 4} as the initial puncturing set, shown from Fig.…”

Section: Comparison With Qupmentioning

confidence: 99%

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Optimal Puncturing of Polar Codes With a Fixed Information Set

Song

Niu

2019

IEEE Access

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For a given polar code construction, the existing literature on puncturing for polar codes focuses in finding the optimal puncturing pattern, and then re-selecting the information set. This paper devotes itself to find the optimal puncturing pattern when the information set is fixed. Puncturing the coded bits corresponding to the worst quality bit channels, called the worst quality puncturing (WQP), is proposed, which is analyzed to minimize the bit channel quality loss at the punctured positions. Simulation results show that WQP outperforms the best existing puncturing schemes when the information set is fixed.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: B Optimal Puncturing Patternmentioning

confidence: 99%

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Optimal Puncturing of Polar Codes With a Fixed Information Set

Song

Niu

2019

IEEE Access

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“…We use the polarization weight method [14] to allocate frozen bits, this is a channel-independent approximation method [15]. For this method, the information bits satisfy I 0 ⊆ I 1 ⊆ • • • ⊆ I a , thus the subcode condition needed by Construction D is met.…”

Section: B Polar Code Lattices Under Scl Decodingmentioning

confidence: 99%

Design of Polar Code Lattices of Finite Dimension

Ludwiniananda

Liu

Anwar

et al. 2021

2021 IEEE International Symposium on Information Theory (ISIT)

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Polar code lattices are formed from binary polar codes using Construction D. In this paper, we propose a design technique for finite-dimension polar code lattices. The dimension n and target probability of decoding error are parameters for this design. To select the rates of the Construction D component codes, rather than using the capacity as in past work, we use the explicit finite-length properties of the polar code. Under successive cancellation decoding, density evolution allows choosing code rates that satisfy the equal error probability rule. At an errorrate of 10 −4 , a dimension n = 128 polar code lattice achieves a VNR of 2.5 dB, within 0.2 dB of the best-known BCH code lattice, but with significantly lower decoding complexity.

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“…In [12] and [13], two efficient rate matching methods were proposed. In [14], a polar code construction method called polarization weight (PW) was proposed, which efficiently simplifies the selection of information set. Furthermore, a novel blind detection method with polar codes was proposed in [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Block Combined Decoding for Cell Search With Polar Codes

et al. 2019

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Polar codes have been applied in many applications due to the excellent decoding performance as well as relatively low encoding and decoding complexity. In the cell search procedure for the fifth generation (5G) systems, part of the common information bits (CIBs) is transmitted in successive transmission blocks. In this paper, multiple-block combined decoding (MBCD) schemes are proposed for both non-systematic and systematic polar codes to enhance cell search. In the proposed MBCD schemes, the log-likelihood ratios (LLRs) of the CIBs are combined to improve the reliabilities of the corresponding sub-channels. The common information sets are further optimized. For both schemes, low-complexity optimization methods are proposed. Meanwhile, the closed-form expression of block error rate (BLER) performance is derived, which is verified by simulation results. The simulation results show that the proposed MBCD schemes can significantly improve the BLER performance, leading to a latency reduction in cell search. INDEX TERMS Polar codes, cell search, multiple-block combined decoding.

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Polarization Weight Family Methods for Polar Code Construction

Cited by 16 publications

References 11 publications

Optimal Puncturing of Polar Codes With a Fixed Information Set

Optimal Puncturing of Polar Codes With a Fixed Information Set

Design of Polar Code Lattices of Finite Dimension

Multiple-Block Combined Decoding for Cell Search With Polar Codes

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