Mixed Precision Multi-frame Parallel Low-Density Parity-Check Code Decoder

As the demand for high speed and high quality connectivity is increasing exponentially, channels are getting more and more crowded. The need for a high performance and low error floor channel decoder is apparent. Low-density parity-check code (LDPC) is a linear error correction code that can reach near Shannon limit. In this work, LDPC code construction and decoding algorithms are discussed, the LDPC decoder, in fully parallel and partial parallel, was implemented, and the features and issues related to corresponding architecture are analyzed. Furthermore, a multi-frame processing approach, based on pipelining and out-of-order processing, is proposed. The implemented decoder achieves 12.6 Gbps at 3.0 dB SNR. The mixed precision scheme is explored by adding precision control and alignment units before and after check node units (CNU) to improve performance, as well as error floor. By mixing the 6-bit and 5-bit precision CNUs at 1:1 ratio, the decoder reaches ~0.5 dB lower FER and BER while retaining a low error floor

Mixed Precision Multi-frame Parallel Low-Density Parity-Check Code Decoder

As the demand for high speed and high quality connectivity is increasing exponentially, channels are getting more and more crowded. The need for a high performance and low error floor channel decoder is apparent. Low-density parity-check code (LDPC) is a linear error correction code that can reach near Shannon limit. In this work, LDPC code construction and decoding algorithms are discussed, the LDPC decoder, in fully parallel and partial parallel, was implemented, and the features and issues related to corresponding architecture are analyzed. Furthermore, a multi-frame processing approach, based on pipelining and out-of-order processing, is proposed. The implemented decoder achieves 12.6 Gbps at 3.0 dB SNR. The mixed precision scheme is explored by adding precision control and alignment units before and after check node units (CNU) to improve performance, as well as error floor. By mixing the 6-bit and 5-bit precision CNUs at 1:1 ratio, the decoder reaches ~0.5 dB lower FER and BER while retaining a low error floor

An improvement and a fast DSP implementation of the bit flipping algorithms for low density parity check decoder

For low density parity check (LDPC) decoding, hard-decision algorithms are sometimes more suitable than the soft-decision ones. Particularly in the high throughput and high speed applications. However, there exists a considerable gap in performances between these two classes of algorithms in favor of soft-decision algorithms.  In order to reduce this gap, in this work we introduce two new improved versions of the hard-decision algorithms, the adaptative gradient descent bit-flipping (AGDBF) and adaptative reliability ratio weighted GDBF (ARRWGDBF).  An adaptative weighting and correction factor is introduced in each case to improve the performances of the two algorithms allowing an important gain of bit error rate. As a second contribution of this work a real time implementation of the proposed solutions on a digital signal processors (DSP) is performed in order to optimize and improve the performance of these new approchs. The results of numerical simulations and DSP implementation reveal a faster convergence with a low processing time and a reduction in consumed memory resources when compared to soft-decision algorithms. For the irregular LDPC code, our approachs achieves gains of 0.25 and 0.15 dB respectively for the AGDBF and ARRWGDBF algorithms

Design Trade‐Offs for FPGA Implementation of LDPC Decoders

Low density parity check (LDPC) decoders represent important throughput bottlenecks, as well as major cost and power-consuming components in today\u27s digital circuits for wireless communication and storage. They present a wide range of architectural choices, with different throughput, cost, and error correction capability trade-offs. In this book chapter, we will present an overview of the main design options in the architecture and implementation of these circuits on field programmable gate array (FPGA) devices. We will present the mapping of the main units within the LDPC decoders on the specific embedded components of FPGA device. We will review architectural trade-offs for both flooded and layered scheduling strategies in their FPGA implementation

An Iteratively Decodable Tensor Product Code with Application to Data Storage

The error pattern correcting code (EPCC) can be constructed to provide a syndrome decoding table targeting the dominant error events of an inter-symbol interference channel at the output of the Viterbi detector. For the size of the syndrome table to be manageable and the list of possible error events to be reasonable in size, the codeword length of EPCC needs to be short enough. However, the rate of such a short length code will be too low for hard drive applications. To accommodate the required large redundancy, it is possible to record only a highly compressed function of the parity bits of EPCC's tensor product with a symbol correcting code. In this paper, we show that the proposed tensor error-pattern correcting code (T-EPCC) is linear time encodable and also devise a low-complexity soft iterative decoding algorithm for EPCC's tensor product with q-ary LDPC (T-EPCC-qLDPC). Simulation results show that T-EPCC-qLDPC achieves almost similar performance to single-level qLDPC with a 1/2 KB sector at 50% reduction in decoding complexity. Moreover, 1 KB T-EPCC-qLDPC surpasses the performance of 1/2 KB single-level qLDPC at the same decoder complexity.Comment: Hakim Alhussien, Jaekyun Moon, "An Iteratively Decodable Tensor Product Code with Application to Data Storage

FPGA accelerator of algebraic quasi cyclic LDPC codes for NAND flash memories

In this article, the authors implement an FPGA simulator that accelerates the performance evaluation of very long QC-LDPC codes, and present a novel 8-KB LDPC code for NAND flash memory with better performance