Abstract
Due to the design principle of parallel processing, belief propagation (BP) decoding is attractive, and it provides good error-correction performance compared with successive cancellation (SC) decoding. However, its error-correction performance is still inferior to that of successive cancellation list (SCL) decoding. Consequently, this paper proposes a novel flip-list- (FL)-enabled belief propagation (BP) method to improve the error-correction performance of BP decoding for polar codes with low computational complexity. The proposed technique identifies the vulnerable channel log-likelihood ratio (LLR) that deteriorates the BP decoding result. The FL is utilized to efficiently identify the erroneous channel LLRs and correct them for the next BP decoding attempt. The preprocessed channel LLR through FL improves the error-correction performance with minimal flipping attempts and reduces the computational complexity. The proposed technique was compared with the state-of-the-art BP, i.e., BP bit-flip (BP-BF), generalized BP-flip (GBPF), cyclic redundancy check (CRC)-aided (CA-SCL) decoding, and ordered statistic decoding (OSD), algorithms. Simulation results showed that the FL-BP had an excellent block error rate (BLER) performance gain up to 0.7 dB compared with BP, BP-BF, and GBPF decoder. Besides, the computational complexity was reduced considerably in the high signal-to-noise ratio (SNR) regime compared with the BP-BF and GBPF decoding methods.
Highlights
IntroductionErdal Arikan proved that polar codes have the potential to achieve the Shannon capacity limit [1] of a binary-input discrete memoryless channel with infinite blocklength ( N )
Erdal Arikan proved that polar codes have the potential to achieve the Shannon capacity limit [1] of a binary-input discrete memoryless channel with infinite blocklength ( N )using an successive cancellation (SC) decoder with computational complexity O( NlogN ) [2]
The performance of FL-belief propagation (BP) was validated in terms of the block error rate (BLER) and average computational complexity and compared against the BP, BP bit-flip (BP-BF), generalized BP-flip (GBPF), and CA-Successive cancellation list (SCL) decoders for P(256, 128) and P(512, 256)
Summary
Erdal Arikan proved that polar codes have the potential to achieve the Shannon capacity limit [1] of a binary-input discrete memoryless channel with infinite blocklength ( N ). The BP decoding is free of error propagation phenomena, and all errors that occur are due to the channel noise This implies that flipping of the a priori knowledge of the indices of the low absolute decision LLR and least reliable nonfrozen bit channel is not able to ensure the improvement for the finite blocklength. The FL-BP technique utilizes the corresponding frozen bit indices for the correction of erroneous channel LLRs. Simulation results show high BLER gains with the FL-BP compared with BP, BP bit-flip, GBPF decoding, and CA-SCL while retaining the average computational complexity close to the standard BP decoding in medium-to-high. FL to improve the BLER performance of the BP decoder for the polar code; We propose a novel FL-BP decoding algorithm that incorporates the erring channels from the FL list and performs correction through the preprocessed erroneous channel.
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