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Register a new userScalable Successive-Cancellation Hardware Decoder for Polar Codes
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Added by
Alexandre J. Raymond
Publication date
2013
fields
Informatics Engineering
and research's language is
English
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Polar codes, discovered by Ar{i}kan, are the first error-correcting codes with an explicit construction to provably achieve channel capacity, asymptotically. However, their error-correction performance at finite lengths tends to be lower than existing capacity-approaching schemes. Using the successive-cancellation algorithm, polar decoders can be designed for very long codes, with low hardware complexity, leveraging the regular structure of such codes. We present an architecture and an implementation of a scalable hardware decoder based on this algorithm. This design is shown to scale to code lengths of up to N = 2^20 on an Altera Stratix IV FPGA, limited almost exclusively by the amount of available SRAM.
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This paper presents a hardware architecture of fast simplified successive cancellation (fast-SSC) algorithm for polar codes, which significantly reduces the decoding latency and dramatically increases the throughput. Algorithmically, fast-SSC algorithm suffers from the fact that its decoder scheduling and the consequent architecture depends on the code rate; this is a challenge for rate-compatible system. However, by exploiting the homogeneousness between the decoding processes of fast constituent polar codes and regular polar codes, the presented design is compatible with any rate. The scheduling plan and the intendedly designed process core are also described. Results show that, compared with the state-of-art decoder, proposed design can achieve at least 60% latency reduction for the codes with length N = 1024. By using Nangate FreePDK 45nm process, proposed design can reach throughput up to 5.81 Gbps and 2.01 Gbps for (1024, 870) and (1024, 512) polar code, respectively.
This paper presents an efficient hardware design approach for list successive cancellation (LSC) decoding of polar codes. By applying path-overlapping scheme, the l instances of (l > 1) successive cancellation (SC) decoder for LSC with list size l can be cut down to only one. This results in a dramatic reduction of the hardware complexity without any decoding performance loss. We also develop novel approaches to reduce the latencyassociated with the pipeline scheme. Simulation results show that with proposed design approach the hardware efficiency is increased significantly over the recently proposed LSC decoders.
Polar codes are a class of channel capacity achieving codes that has been selected for the next generation of wireless communication standards. Successive-cancellation (SC) is the first proposed decoding algorithm, suffering from mediocre error-correction performance at moderate code length. In order to improve the error-correction performance of SC, two approaches are available: (i) SC-List decoding which keeps a list of candidates by running a number of SC decoders in parallel, thus increasing the implementation complexity, and (ii) SC-Flip decoding that relies on a single SC module, and keeps the computational complexity close to SC. In this work, we propose the partitioned SC-Flip (PSCF) decoding algorithm, which outperforms SC-Flip in terms of error-correction performance and average computational complexity, leading to higher throughput and reduced energy consumption per codeword. We also introduce a partitioning scheme that best suits our PSCF decoder. Simulation results show that at equivalent frame error rate, PSCF has up to $5 times$ less computational complexity than the SC-Flip decoder. At equivalent average number of iterations, the error-correction performance of PSCF outperforms SC-Flip by up to $0.15$ dB at frame error rate of $10^{-3}$.
This work analyzes the latency of the simplified successive cancellation (SSC) decoding scheme for polar codes proposed by Alamdar-Yazdi and Kschischang. It is shown that, unlike conventional successive cancellation decoding, where latency is linear in the block length, the latency of SSC decoding is sublinear. More specifically, the latency of SSC decoding is $O(N^{1-1/mu})$, where $N$ is the block length and $mu$ is the scaling exponent of the channel, which captures the speed of convergence of the rate to capacity. Numerical results demonstrate the tightness of the bound and show that most of the latency reduction arises from the parallel decoding of subcodes of rate $0$ or $1$.
A deep-learning-aided successive-cancellation list (DL-SCL) decoding algorithm for polar codes is introduced with deep-learning-aided successive-cancellation (DL-SC) decoding being a specific case of it. The DL-SCL decoder works by allowing additional rounds of SCL decoding when the first SCL decoding attempt fails, using a novel bit-flipping metric. The proposed bit-flipping metric exploits the inherent relations between the information bits in polar codes that are represented by a correlation matrix. The correlation matrix is then optimized using emerging deep-learning techniques. Performance results on a polar code of length 128 with 64 information bits concatenated with a 24-bit cyclic redundancy check show that the proposed bit-flipping metric in the proposed DL-SCL decoder requires up to 66% fewer multiplications and up to 36% fewer additions, without any need to perform transcendental functions, and by providing almost the same error-correction performance in comparison with the state of the art.
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