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IEEE International Conference on Acoustics Speech and Signal Processing

A Memory Efficient Serial LDPC Decoder Architecture

Abhiram Prabhakar, Krishna R. Narayanan

Published Oct 4, 2006• 6 citations
Publisher

Abstract

We present a memory efficient serial low density parity check (LDPC) decoder that implements a modified sum product algorithm (SPA). The modification is similar to the approximate min constraint presented by C. Jones et al. (see IEEE Conf. Military Commun., MILCOM 2003, p.157-162, 2003) but differs in hardware implementation to suit a serial architecture. Our main contribution is the proposed architecture that exploits the min constraint to reduce the storage of extrinsic messages which forms the bulk of the hardware. The least reliable bit to check input along with the check sum are the only quantities stored in the decoder. Extrinsic message memory reduction increases with the rate of the code and up to 68% saving is achieved for a rate 9/10 code. Simulation results show that the proposed changes do not degrade the bit error rate performance.

DOI
10.1109/icassp.2005.1416235

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We present a memory efficient serial low density parity check (LDPC) decoder that implements a modified sum product algorithm (SPA). The modification is similar to the approximate min constraint presented by C. Jones et al. (see IEEE Conf. Military Commun., MILCOM 2003, p.157-162, 2003) but differs in hardware implementation to suit a serial architecture. Our main contribution is the proposed architecture that exploits the min constraint to reduce the storage of extrinsic messages which forms the bulk of the hardware. The least reliable bit to check input along with the check sum are the only quantities stored in the decoder. Extrinsic message memory reduction increases with the rate of the code and up to 68% saving is achieved for a rate 9/10 code. Simulation results show that the proposed changes do not degrade the bit error rate performance.
In this paper, we propose low complexity implementations of sum-product algorithm (SPA) for decoding low-density parity-check (LDPC) codes. The finite precision implementations for SPA have an important tradeoff between the decoding performance and hardware complexity caused by two dominant area consuming factors. One is the memory for updated messages storage and the other is the look-up table (LUT) for implementation of nonlinear function Psi(x). The proposed two types of variable quantization schemes result in significant reductions of LUT sizes. Computer simulation results indicate that the performance losses of SPA with reduced LUT by proposed schemes are less than 0.05 dB compared to that of SPA with original LUT. Moreover, the proposed implementation offers a large reduction in the finite wordlength for storage of updated messages using a kind of compression technique without performance losses. As a result, using the proposed implementation based on variable quantization scheme and data compression technique, the novel proposed implementation for SPA decoder architecture offers significant reductions of 75% and 33% in hardware complexities of LUT and memories, respectively, without significant performance degradations over conventional uniform quantization scheme. Furthermore, since each LUT with variable (6:3) quantization scheme II has only 8 entries, its hardware implementation can be replaced with a small combination circuit instead of readonly-memory (ROM) for LUT
We describe a decoder architecture intended for decoding array-code-based low-density parity-check (LDPC) codes using the sum-product algorithm (SPA). The advantages of the proposed architecture, as compared to the fully parallel implementation of the SPA, are: reduced memory size, avoidance of complex signal interconnect patterns, and ease of programmability to accommodate various code parameters. These advantages are derived from exploiting the well-defined structure of the parity-check matrix of array-code based LDPC codes. Sum-product decoding with modified message-passing schedules are proposed to simplify the decoder implementation further.
This paper proposes a hardware-and memoryefficient architecture for Low-Density Parity-Check (LDPC) decoding, targeting enhanced-performance applications with constrained resources.The design integrates two novel techniques: (i) the Variable Single minimum Min-Sum (VSMS) algorithm, which reduces hardware complexity by identifying the first minimum value and its position during check node processing, while improving error correction through a correction factor applied in variable node updates; and (ii) a memory splitting strategy that exploits the structural properties of LDPC codes to optimize memory usage.Implementation on a Xilinx Kintex UltraScale+ (xcku5p) FPGA demonstrates a reduction in storage requirements by over 46.2% compared to conventional decoders.Furthermore, the proposed decoder achieves a performance gain of up to 0.38 dB at a Bit Error Rate (BER) of 10⁻⁸, outperforming traditional Min-Sum-based approaches.
This thesis is focused on the design and implementation of binary low-density parity-check (LDPC) code decoders for high-speed modern communication systems. The basic of LDPC codes and the performance and bottlenecks, in terms of complexity and hardware efficiency, of the main soft-decision and hard-decision decoding algorithms (such as Min-Sum, Optimized 2-bit Min-Sum and Reliability-based iterative Majority-Logic) are analyzed. The complexity and performance of those algorithms are improved to allow efficient hardware architectures.\n\nA new decoding algorithm called One-Minimum Min-Sum is proposed. It reduces considerably the complexity of the check node update equations of the Min-Sum algorithm. The second minimum is estimated from the first minimum value by a means of a linear approximation that allows a dynamic adjustment. The Optimized 2-bit Min-Sum algorithm is modified to initialize it with the complete LLR values and to introduce the extrinsic information in the messages sent from the variable nodes. Its variable node equation is reformulated to reduce its complexity. Both algorithms were tested for the (2048,1723) RS-based LDPC code and (16129,15372) LDPC code using an FPGA-based hardware emulator. They exhibit BER performance very close to Min-Sum algorithm and do not introduce early error-floor. \n\nIn order to show the hardware advantages of the proposed algorithms, hardware decoders were implemented in a 90 nm CMOS process and FPGA devices based on two types of architectures: full-parallel and partial-parallel one with horizontal layered schedule. The results show that the decoders are more area-time efficient than other published decoders and that the low-complexity of the Modified Optimized 2-bit Min-Sum allows the implementation of 10 Gbps decoders in current FPGA devices. \n\nFinally, a new hard-decision decoding algorithm, the Historical-Extrinsic Reliability-Based Iterative Decoder, is presented. This algorithm introduces the new idea of considering hard-decision votes as soft-decision to compute the extrinsic information of previous iterations. It is suitable for high-rate codes and improves the BER performance of the previous RBI-MLGD algorithms, with similar complexity.
This paper describes an efficient implementation of the Sum-Product Algorithm (SPA) within a Low Density Parity Check (LDPC) code decoder, where a horizontal process correction term is used to improve the decoding performance of the Min-Sum algorithms. The correction term is implemented as a look-up table. The algorithm uses the correction term redundancy by means of a coordinate transformation to reduce the hardware complexity. Simulations and hardware tests indicate that the decoding performance is very good with the appropriate look-up table.
This paper presents a resource efficient LDPC decoder architecture. The algorithm used for decoding LDPC is the min-sum algorithm. The decoder reduces the inter-connect complexity by restricting the extrinsic message length to 2 bits and also simplifies the check node operation. The algorithm is simulated and the results show that the performance is better than that of other algorithms. This algorithm can be incorporated into partially parallel hardware architecture to get significant savings in hardware resources when implemented in FPGA.