Algebraic Soft-decision Decoding Research Articles

Efficient VLSI Architecture for Soft-Decision Decoding of Reed–Solomon Codes

Reed-Solomon (RS) codes have very broad applications in digital communication and storage systems. The developed algebraic soft-decision decoding (ASD) algorithms of RS codes can achieve substantial coding gain with polynomial complexity. Among the ASD algorithms with practical multiplicity assignment schemes, the bit-level generalized minimum distance (BGMD) decoding algorithm can achieve similar or higher coding gain with lower complexity. ASD algorithms consist of two major steps: the interpolation and the factorization. In this paper, novel architectures for both steps are proposed for the BGMD decoder. The interpolation architecture is based on the newly proposed Lee-O'Sullivan (LO) algorithm. By exploiting the characteristics of the LO algorithm and the multiplicity assignment scheme in the BGMD decoder, the proposed interpolation architecture for a (255, 239) RS code can achieve 25% higher efficiency in terms of speed/area ratio than prior efforts. Root computation over finite fields and polynomial updating are the two main steps of the factorization. A low-latency and prediction-free scheme is introduced in this paper for the root computation in the BGMD decoder. In addition, novel coefficient storage schemes and parallel processing architectures are developed to reduce the latency of the polynomial updating. The proposed factorization architecture is 126% more efficient than the previous direct root computation factorization architecture.

Performance Analysis of Algebraic Soft-Decision Decoding of Reed–Solomon Codes

We investigate the decoding region for algebraic soft-decision decoding (ASD) of Reed-Solomon (RS) codes in a discrete, memoryless, additive-noise channel. An expression is derived for the error correction radius within which the soft-decision decoder produces a list that contains the transmitted codeword. The error radius for ASD is shown to be larger than that of Guruswami-Sudan (GS) hard-decision decoding for a subset of low and medium-rate codes. These results are also extended to multivariable interpolation in the sense of Parvaresh and Vardy.

Algebraic Soft-Decision Decoding of Reed–Solomon Codes Using Bit-Level Soft Information

The performance of algebraic soft-decision decoding of Reed-Solomon codes using bit-level soft information is investigated. Optimal multiplicity assignment strategies for algebraic soft-decision decoding (SDD) with infinite cost are first studied over erasure channels and the binary-symmetric channel. The corresponding decoding radii are calculated in closed forms and tight bounds on the error probability are derived. The multiplicity assignment strategy and the corresponding performance analysis are then generalized to characterize the decoding region of algebraic SDD over a mixed error and bit-level erasure channel. The bit-level decoding region of the proposed multiplicity assignment strategy is shown to be significantly larger than that of conventional Berlekamp-Massey decoding. As an application, a bit-level generalized minimum distance decoding algorithm is proposed. The proposed decoding compares favorably with many other Reed-Solomon SDD algorithms over various channels. Moreover, owing to the simplicity of the proposed bit-level generalized minimum distance decoding, its performance can be tightly bounded using order statistics.

Low-Latency Factorization Architecture for Algebraic Soft-Decision Decoding of Reed–Solomon Codes

Bivariate polynomial factorization is an important stage of algebraic soft-decision decoding of Reed-Solomon (RS) codes and contributes to a significant portion of the overall decoding latency. With the exhaustive search-based root computation method, factorization latency is dominated by the root computation step, especially for RS codes defined over very large finite fields. The root-order prediction method proposed by Zhang and Parhi only improves average latency, but does not have any effect on the worst-case latency of the factorization procedure. Thus, neither approach is well-suited for delay-sensitive applications. In this paper, a novel architecture based on direct root computation is proposed to greatly reduce the factorization latency. Direct root computation is feasible because in most practical applications of algebraic soft-decision decoding of RS codes, enough decoding gain can be achieved with a relatively low interpolation cost, which results in a bivariate polynomial with low Y-degree. Compared with existing works, not only does the new architecture have a significantly smaller worst-case decoding latency, but it is also more area efficient since the corresponding hardware for routing polynomial coefficients is eliminated.

Reliability-Based Forward Recursive Algorithms for Algebraic Soft-Decision Decoding of Reed--Solomon Codes

We propose efficient forward recursive algorithms for algebraic soft-decision list decoding of Reed-Solomon codes, which utilize channel reliability information, and outperform the Koetter-Vardy (KV) algorithm with lower decoding latency. We evaluate the performance of the proposed decoding algorithms on additive white Gaussian noise and partial response channels. Simulation results show that we can achieve better performance on a modified extended-extended partial response class 4 channel than on the best possible performance of the KV algorithm, as given by the asymptotic bound for high-rate codes.

Algebraic Soft-Decision Decoding of Reed-Solomon Codes with Erasures on Gaussian Channels

The search for increasing the error performance of algebraic soft-decision decoding of high rate Reed-Solomon (RS) codes motivates the development of this work in an attempt to determine the ultimate error-correcting capabilities of algebraic soft-decision decoding of RS codes in Gaussian channels with erasures. It is shown through simulation that a significant performance improvement can be obtained when the unreliable bits received from channel output are declared as erased bits. An alternative method to construct the reliability matrix is developed through the mapping of the a posteriori channel probabilities in order to assign equal multiplicity for symbols with the same erased bits patterns. Conversely, it is also shown through simulation that trying to declare the unreliable symbols received from the channel as erasures does not lead to any performance gain and in some cases it would affect the decoder performance.

High-Speed Interpolation Architecture for Soft-Decision Decoding of Reed–Solomon Codes

Algebraic soft-decision decoding of Reed-Solomon (RS) codes delivers promising coding gains over conventional hard-decision decoding. The most computationally demanding step in soft-decision decoding of RS codes is bivariate polynomial interpolation. In this paper, we present a hybrid data format-based interpolation architecture that is well suited for high-speed implementation of the soft-decision decoders. It will be shown that this architecture is highly scalable and can be extensively pipelined. It also enables maximum overlap in time for computations at adjacent iterations. It is estimated that the proposed architecture can achieve significantly higher throughput than conventional designs with equivalent or lower hardware complexity

Applications of Algebraic Soft-Decision Decoding of Reed–Solomon Codes

Efficient soft-decision decoding of Reed–Solomon codes is made possible by the Koetter–Vardy (KV) algorithm which consists of a front-end to the interpolation-based Guruswami–Sudan list decoding algorithm. This paper approaches the soft-decision KV algorithm from the point of view of a communications systems designer who wants to know what benefits the algorithm can give, and how the extra complexity introduced by soft decoding can be managed at the systems level. We show how to reduce the computational complexity and memory requirements of the soft-decision front-end. Applications to wireless communications over Rayleigh fading channels and magnetic recording channels are proposed. For a high-rate (RS 9225,239) Reed–Solomon code, 2–3 dB of soft-decision gain is possible over a Rayleigh fading channel using 16-quadrature amplitude modulation. For shorter codes and at lower rates, the gain can be as large as 9 dB. To lower the complexity of decoding on the systems level, the redecoding architecture is explored which uses only the appropriate amount of complexity to decode each packet. An error-detection criterion based on the properties of the KV decoder is proposed for the redecoding architecture. Queuing analysis verifies the practicality of the redecoding architecture by showing that only a modestly sized RAM buffer is required.

Exponential Error Bounds for Algebraic Soft-Decision Decoding of Reed–Solomon Codes

Algebraic soft-decision decoding of Reed-Solomon codes is a promising technique for exploiting reliability information in the decoding process. While the algorithmic aspects of the decoding algorithm are reasonably well understood and, in particular, complexity is polynomially bounded in the length of the code, the performance analysis has relied almost entirely on simulation results. Analytical exponential error bounds that can be used to tightly bound the performance of Reed-Solomon codes under algebraic soft-decision decoding are presented in this paper. The analysis is used in a number of examples and several extensions and consequences of the results are presented.

Iterative algebraic soft-decision list decoding of Reed-Solomon codes

In this paper, we present an iterative soft-decision decoding algorithm for Reed-Solomon codes offering both complexity and performance advantages over previously known decoding algorithms. Our algorithm is a list decoding algorithm which combines two powerful soft decision decoding techniques which were previously regarded in the literature as competitive, namely, the Koetter-Vardy algebraic soft-decision decoding algorithm and belief-propagation based on adaptive parity check matrices, recently proposed by Jiang and Narayanan. Building on the Jiang-Narayanan algorithm, we present a belief-propagation based algorithm with a significant reduction in computational complexity. We introduce the concept of using a belief-propagation based decoder to enhance the soft-input information prior to decoding with an algebraic soft-decision decoder. Our algorithm can also be viewed as an interpolation multiplicity assignment scheme for algebraic soft-decision decoding of Reed-Solomon codes.

Towards a VLSI Architecture for Interpolation-Based Soft-Decision Reed-Solomon Decoders

The Koetter-Vardy algorithm is an algebraic soft-decision decoder for Reed-Solomon codes which is based on the Guruswami-Sudan list decoder. There are three main steps: (1) multiplicity calculation, (2) interpolation and (3) root finding. The Koetter-Vardy algorithm seems challenging to implement due to the high cost of interpolation. Motivated by a VLSI implementation viewpoint we propose an improvement to the interpolation algorithm that uses a transformation of the received word to reduce the number of iterations. We show how to reduce the memory requirements and give an efficient VLSI implementation for the Hasse derivative.

Application of Soft-Decision Reed–Solomon Decoding to Magnetic Recording Channels

We report on the application of algebraic soft-decision decoding of Reed-Solomon codes to magnetic recording channels. We present efficient algorithm implementations of the Koetter-Vardy algorithm and introduce a soft version of the Chase algorithm, which expands the decoding list without substantial increase in complexity. Our evaluation of these algorithms, based on realistic simulations on magnetic recording channels, shows that improved performance can be achieved with a small increase in hardware complexity.

Algebraic soft-decision decoding of reed-solomon codes

A polynomial-time soft-decision decoding algorithm for Reed-Solomon codes is developed. This list-decoding algorithm is algebraic in nature and builds upon the interpolation procedure proposed by Guruswami and Sudan(see ibid., vol.45, p.1757-67, Sept. 1999) for hard-decision decoding. Algebraic soft-decision decoding is achieved by means of converting the probabilistic reliability information into a set of interpolation points, along with their multiplicities. The proposed conversion procedure is shown to be asymptotically optimal for a certain probabilistic model. The resulting soft-decoding algorithm significantly outperforms both the Guruswami-Sudan decoding and the generalized minimum distance (GMD) decoding of Reed-Solomon codes, while maintaining a complexity that is polynomial in the length of the code. Asymptotic analysis for alarge number of interpolation points is presented, leading to a geo- metric characterization of the decoding regions of the proposed algorithm. It is then shown that the asymptotic performance can be approached as closely as desired with a list size that does not depend on the length of the code.