Successive Cancellation Flip Decoding Research Articles

Parity-Check-CRC Concatenated Polar Codes SSCFlip Decoder

Successive cancellation flip decoding requires a large number of extra successive cancellation decoding attempts at low signal-to-noise ratios (SNRs), resulting in high decoding complexity. In addition, it has a long decoding latency. Although modifications have been proposed in successive cancellation flip decoding, these still have high computational complexity at low SNRs due to a huge number of additional successive cancellation decoding attempts. It is desirable to detect the unsuccessful successive cancellation decoding process at an early stage in the additional successive cancellation flip attempts and stop it that can reduce the decoding complexity. This paper combines the parity-check-CRC concatenated polar codes with the low-latency simplified successive cancellation decoding and proposes a parity-check-CRC concatenated polar codes simplified successive cancellation flip (PC-CRC-SSCFlip) decoder. It further employs the parity-check vector to identify the unsuccessful simplified successive cancellation flip decoding at an early stage and terminates so that it can minimize the decoding complexity on average. Additionally, this work proposes an error-prone flipping list by incorporating the empirically observed indices based on channel-induced error distribution along with the first bit of each Rate-1 node. The proposed technique can identify more than one error-prone bit through a flipping list and correct them. In addition, the parity-check vector further narrows down the search space for the identification of erroneous decisions. Simulation results show that 60% of unsuccessful additional successive cancellation decoding attempts terminate early rather than decode the whole codeword. The proposed PC-CRC-SSCFlip decoder has approximately 0.7 dB and 0.3 dB gains over successive cancellation and successive cancellation flip decoders, respectively, at a fixed block error rate (BLER) = 10−3. Additionally, it reduces the average computational complexity and decoding latency of the successive cancellation flip decoder at low-to-medium SNRs while approaching successive cancellation decoding complexity at medium-to-high SNRs.

Distributed CRC scheme for low-complexity successive cancellation flip decoding of polar codes

In this paper, we propose a novel successive cancellation flip (SCF) decoding to reduce the computational complexity compared to the conventional SCF decoding by using distributed CRC bits. The proposed decoding reduces the number of estimations for information bits by early termination of decodings for failed frames of the first SC decoding, while trying to minimize the additional sorting operations. Simulation results show that the proposed SCF decoding reduces the computational complexity of repeated SC decoding at least 27% compared to the conventional SCF decoding.

Dynamic SCL Decoder With Path-Flipping for 5G Polar Codes

Since polar were ratified as part of the 5G standard, low-complexity polar decoders with close-to-optimum error-rate performance have received significant attention. Compared to successive cancellation (SC) decoding, both SC list and SC flip decoding can improve the error rate performance by increasing the number of considered candidate solutions. The combination of both strategies leads to SC list flip (SCLF) decoding, which can provide a tradeoff between error rate performance and area as well as energy. In this letter, we derive a new flip metric for the SCLF decoding process, based on which we propose the dynamic SCLF (D-SCLF) decoding algorithm. Moreover, we exploit the distributed CRC defined in the 5G standard to further optimize the D-SCLF decoding. Numerical results show that for the downlink control channel, our D-SCLF decoder with a list size of only four and only three additional attempts can achieve the performance of a regular list decoder with a list size of eight, leading to an overall memory and average complexity (energy) reduction.

Reinforcement Learning for Bit-Flipping Decoding of Polar Codes

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.

Neural Successive Cancellation Flip Decoding of Polar Codes

Dynamic successive cancellation flip (DSCF) decoding of polar codes is a powerful algorithm that can achieve the error correction performance of successive cancellation list (SCL) decoding, with an average complexity that is close to that of successive cancellation (SC) decoding at practical signal-to-noise ratio (SNR) regimes. However, DSCF decoding requires costly transcendental computations to calculate a bit-flipping metric, which adversely affect its implementation complexity. In this paper, we first show that a direct application of common approximation schemes on the conventional DSCF decoding results in a significant error-correction performance loss. We then introduce an additive perturbation parameter and propose an approximation scheme which completely removes the need to perform transcendental computations in DSCF decoding. Machine learning (ML) techniques are then utilized to optimize the perturbation parameter of the proposed scheme. Furthermore, a quantization scheme is developed to enable efficient hardware implementation. Simulation results show that when compared with DSCF decoding, the proposed decoder with quantization scheme only experiences a negligible error-correction performance degradation of less that 0.08 dB at a target frame-error-rate (FER) of 10− 4, for a polar code of length 512 with 256 information bits. In addition, the bit-flipping metric computation of the proposed decoder reduces up to around 31% of the number of additions used by the bit-flipping metric computation of DSCF decoding, without any need to perform costly transcendental computations and multiplications.

An Improved SC Flip Decoding Algorithm of Polar Codes Based on Genetic Algorithm

Polar codes have been applied for physical downlink control channel in the $5^{\mathrm {th}}$ generation wireless communication system. Although successive cancellation flip (SCF) decoding algorithm can improve decoding performance of polar codes, it has also led to the increasing in decoding latency and calculation complexity. Candidate flipping positions set (CFPS) of traditional SCF decoding is consisted of indexes of all information bits. However, some subchannels are reliable enough so that it is almost impossible to cause decoding errors for these subchannels. In order to reduce decoding latency and calculation complexity of SCF decoding algorithm, a new method of constructing the CFPS based on genetic algorithm (GA) is proposed in this paper. What’s more, the paper fills a gap of applying GA for decoding of polar codes. In our proposed method, indexes of all information bits are used as individuals of GA. Then through some genetic operations, a vector that can indicate the reliability of all information bits is obtained. Based on the obtained vector, a new CFPS is constructed. Simulation results show that SCF decoding algorithm based on CFPS constructed by GA can achieve competitive decoding performance, while keeping lower calculation complexity and decoding latency. Compared with SCF decoding algorithm based on critical set, the normalized decoding latency of proposed SCF decoding algorithm can be reduced by 39% at 1.5dB when code length and code rate are equal to 1024 and 0.5, respectively.

비트 인터리버 기반 극 부호의 개선된 연속적 제거 반전 복호

비트 인터리버 기반 극 부호의 개선된 연속적 제거 반전 복호