Presentation on theme: "Soft Decision Decoding Algorithms of Reed-Solomon Codes"— Presentation transcript:
1Soft Decision Decoding Algorithms of Reed-Solomon Codes Jing Jiang and Krishna R. NarayananDepartment of Electrical EngineeringTexas A&M UniversityTitle
2Historical Review of Reed Solomon Codes Date of birth: 40 years ago (Reed and Solomon 1960)Related to non-binary BCH codes (Gorenstein and Zierler 1961)Efficient decoder: not until 6 years later (Berlekamp 1967)Linear feedback shift register (LFSR) interpretation (Massey 1969)Other algebraic hard decision decoder:Euclid’s Algorithm (Sugiyama et al. 1975)Frequency-domain decoding (Gore 1973 and Blahut 1979)Most of the algebraic hard decision decoders are of similar complexity, which is about o(d^2)
3Wide Range of Applications of Reed Solomon Codes NASA Deep Space: CC + RS(255, 223, 32)Multimedia Storage:CD: RS(32, 28, 4), RS(28, 24, 4) with interleavingDVD: RS(208, 192, 16), RS(182, 172, 10) product codeDigitial Video Broadcasting: DVB-T CC + RS(204, 188)Magnetic Recording: RS(255,239) etc. (nested RS code)Recording channels are the most important application, perhaps!
4Basic Properties of Reed Solomon Codes Two forms of representations are equivalent!
5Basic Properties of Reed Solomon Codes (cont’d) Properties of RS code:Symbol level cyclic (nonbinary BCH codes)Maximum distance separable (symbol level):Properties of BM algorithm:Decoding region:Decoding complexity: UsuallyBasic properties
6Motivation for RS Soft Decision Decoder sourceRS EncoderinterleavingPR Encodersinkhard decision+AWGNRS DecoderChannel Equalizerde-interleavingRS Coded Turbo Equalization System-+a prioriextrinsicinterleavingHard decision decoder does not fully exploit the decoding capabilityEfficient soft decision decoding of RS codes remains an open problemThe motivation of an efficient SISO decoder for RS codesSoft input soft output (SISO) algorithm is favorable
7Presentation Outline Symbol-level algebraic soft decision decoding Binary expansion of RS codes and soft decoding algorithmsIterative decoding for RS codesSimulation resultsoutlineApplications and future works
9Reliability Assisted Hard Decision Decoding Generalized Minimum Distance (GMD) Decoding (Forney 1966):New distance measure: generalized minimum distanceSuccessively erase the least reliable symbols and run the hard decision decoderGMD is shown to be asymptotically optimalChase Type-II decoding (Chase 1972):Exhaustively flip the least reliable symbols and run the hard decision decoderChase algorithm is also shown to be asymptotically optimalRelated works:Fast GMD (Koetter 1996)Efficient Chase (Kamiya 2001)Combined Chase and GMD for RS codes (Tang et al. 2001)Performance analysis of these algorithms for RS codes seems still openGeneralized Minimum Distance (GMD) Decoding (Forney 1966)Chase decoding (Chase 1972)Related works
10Algebraic Beyond Half dmin List Decoding Bounded distance + 1 decoding (Berlekamp 1996)Beyond decoding for low rate RS codes (Sudan 1997)Decoding up errors (Guruswami and Sudan 1999)A good tutorial paper (JPL Report, McEliece 2003)Algebraic beyond half dmin decoding, which is the kernel of KV algorithm
11Outline of Algebraic Beyond Half Distance Decoding Basic idea: find f(x), which fits as many points in pairsInterpolation Step:Construct a bivariate polynomial of minimum (1,K-1) degree, which has a zero of order at , i.e.:Factorization Step:generate a list of y-roots, i.e.:Pick up the most likely codeword from the list LOutline: 1) curve fitting!2) multiplicity assignment, let (x,y) pass through (alpha, beta) m times3) the larger the m, the larger the decoding radius4) when beta fails in the decoding radius, (y-f(x)) can factorize Q(x,y)5) smart way to construct Q(x,y) and factorize it6) when soft information is available, can do weighted multiplicity assignmentComplexity:Interpolation (Koetter’s fast algorithm):Factorization (Roth and Ruckenstein’s algorithm):
12Algebraic Soft Interpolation Based List Decoding Koetter and Vardy algorithm (Koetter & Vardy 2003)Based on the Guruswami and Sudan’s algebraic list decodingUse the reliability information to assign multiplicitiesKV is optimal in multiplicity assignment for long RS codesReduced complexity KV (Gross et al. submitted 2003)Re-encoding technique: largely reduce the cost for high rate codesVLSI architecture (Ahmed et al. submitted 2003)KV 1) proportional assignment2) asymptotically optimal
13Soft Interpolation Based Decoding Definition:Reliability matrix:Multiplicity matrix:Score:Cost:Basic idea: interpolating more symbols using the soft informationThe interpolation and factorization is the same as GS algorithmSufficient condition for successful decoding:The complexity increases with , maximum number of multiplicityKV 1) performance analysis becomes interesting2) no longer a fixed radius3) sufficient condition
14Recent Works and Remarks Recent works on performance analysis and multiplicity assignment:Gaussian approximation (Parvaresh and Vardy 2003)Exponential bound (Ratnakar and Koetter 2004)Chernoff bound (El-Khamy and McEliece 2004)Performance analysis over BEC and BSC (Jiang and Narayanan 2005)The ultimate gain of algebraic soft decoding (ASD) over AWGN channel is about 1dBComplexity is scalable but prohibitively huge for large multiplicityThe failure pattern of ASD algorithm and optimal multiplicity assignment scheme is of interestKV 1) failure is interesting, can we do better?
15Performance Analysis of ASD over Discrete Alphabet Channels Performance Analysis over BEC and BSC (Jiang and Narayanan, accepted by ISIT2005)The analysis gives some intuition about the decoding radius of ASDWe investigate the bit-level decoding radius for high rate codesFor BEC, bit-level radius is twice as large as that of the BM algorithmFor BSC, bit-level radius is slightly larger than that of the BM algorithmIn conclusion, ASD is limited by its algebraic engineKV 1) failure is interesting, can we do better?
16Binary Image Expansion of RS Codes and Soft Decision Decoding
17Binary Image Expansion of RS Codes over GF(2m) Life becomes much easier when we go to bit level
18Bit-level Weight Enumerator “The major drawback with RS codes (for satellite use) is that the present generation of decoders do not make full use of bit-based soft decision information” (Berlekamp)How does the binary expansion of RS codes perform under ML decoding?Performance analysis using its weight enumeratorAveraged ensemble weight enumerator of RS codes (Retter 1991)It gives some idea about how RS codes perform under ML decodingWhen turbo code comes, people realize convolutional codes are inherently bad codes, but how about algebraic codes?
19Performance Comparison of RS(255,239) Asymptotically be optimal, but practically suffer a loss!
20Performance Comparison of RS(255,127) More significant difference
21Remarks RS codes themselves are good code However, ML decoding is NP-hard (Guruswami and Vardy 2004)Are there sub-optimal decoding algorithms using the binary expansions?Motivation for bit level decomposition
22Trellis based Decoding using BCH Subcode Expansion Maximum-likelihood decoding and variations:Partition RS codes into BCH subcodes and glue vectors (Vardy and Be’ery 1991)Reduced complexity version (Ponnampalam and Vucetic 2002)Soft input soft output version (Ponnampalam and Grant 2003)Maximum-likelihood SISO decoding and variationsTrellis based decoding using the binary image expansions of RS codes over GF(2m) (Vardy & Be’ery 1991)Reduced complexity version (Ponnampalam & Vucetic 2002)SISO (Ponnampalam & Grant 2003)
23Subfield Subcode Decomposition BCH subcodesGlue vectorRemarks:Decomposition greatly reduces the trellis size for short codesImpractical for long codes, since the size of the glue vectors is very largeRelated work:Construct sparse representation for iterative decoding (Milenkovic and Vasic 2004)Subspace subcode of Reed Solomon codes (Hattori et al. 1998)1) BCH subcode and glue vectors2) Useful to construct sparse parity check matrices for short codes
24Reliability based Ordered Statistic Decoding Reliability based decoding:Ordered Statistic Decoding (OSD) (Fossorier and Lin 1995)Box and Match Algorithm (BMA) (Valembois and Fossorier 2004)Ordered Statistic Decoding using preprocessing (Wu et al. 2004)Basic ideas:Order the received bits according to their reliabilitiesPropose hard decision reprocessing based on the most reliable basis (MRB)Remarks:The reliability based scheme is efficient for short to medium length codesThe complexity increases exponentially with the reprocessing orderBMA algorithm trade memory for time complexityEfficient for general linear block codes
26A Quick QuestionHow does the panacea of modern communication, iterative decoding algorithm work for RS codes?Note that all the codes in the literature, for which we can use soft decoding algorithms are sparse graph codes with small constraint length.
27How does standard message passing algorithm work? erased bitsbit nodes………….………..?…………….check nodesThe nice property of ldpc codes is that the parity check matrix is sparse, thus, the probability that two erased bits participate in another check diminishes.If two or more of the incoming messages are erasures the check is erasedFor the AWGN channel, two or more unreliable messages invalidate the check
28A Few Unreliable Bits “Saturate” the Non-sparse Parity Check Matrix Consider RS(7, 5) over GF(23) :Binary image expansion of the parity check matrix of RS(7, 5) over GF(23)Even in the optimistic case, erasure channel, it won’t give good results.Iterative decoding is stuck due to only a few unreliable bits “saturating” the whole non-sparse parity check matrix
29Sparse Parity Check Matrices for RS Codes Can we find an equivalent binary parity check matrix that is sparse?For RS codes, this is not possible!The H matrix is the G matrix of the dual codeThe dual of an RS code is also an MDS CodeEach row has weight at least (K+1)Typically, the row weight is much higherIt is impossible to have a sparse representation of RS codes
30Iterative Decoding for RS Codes Iterative decoding for general linear block codes:Iterative decoding for general linear block codes (Hagenauer et al. 1996)APP decoding using minimum weight parity checks (Lucas et al. 1998)Generalized belief propagation (Yedidia et al. 2000)Recent progress on RS codes:Sub-trellis based iterative decoding (Ungerboeck 2003)Stochastic shifting based iterative decoding (Jiang and Narayanan, 2004)Sparse representation of RS codes using GFFT (Yedidia, 2004)Iterative decoding for general linear block codes (tough problem!)
31Recent Iterative Techniques Sub-trellis based iterative decoding (Ungerboeck 2003)Self concatenation using sub-trellis constructed from the parity check matrix:Binary image expansion of the parity check matrix of RS(7, 5) over GF(23)Remarks:Performance deteriorates due to large number of short cyclesWork for short codes with small minimum distances
32Recent Iterative Techniques (cont’d) Stochastic shifting based iterative decoding (Jiang and Narayanan, 2004)Due to the irregularity in the H matrix, iterative decoding favors some bitsTaking advantage of the cyclic structure of RS codesShift by 2Stochastic shift prevent iterative procedure from getting stuckBest result: RS(63,55) about 0.5dB gain from HDDHowever, for long codes, the performance deteriorates
34Iterative Decoding Based on Adaptive Parity Check Matrix Idea: reduce the sub-matrix corresponding to unreliable bits to a sparse nature using Gaussian eliminationFor example, consider (7,4) Hamming code:transmitted codewordreceived vectorparity check matrixSince the parity check matrix is of full rank, we are guaranteed to get (n-k) LRBWe can make the (n-k) less reliable positions sparse!
35Adaptive Decoding Procedure unreliable bitsbit nodes………….………..…………….check nodesAfter the adaptive update, iterative decoding can proceed
36Gradient Descent and Adaptive Potential Function Geometric interpretation (suggested by Ralf Koetter)Define the tanh domain transform as:The syndrome of a parity check can be expressed as:Define the soft syndrome as:Define the cost function as:The decoding problem is relaxed as minimizing J using gradient descent with the initial value T observed from the channelDefine syndrome as binary summation of all participating bitsDefine the soft syndrome product of the channel reliabilitiesJ is minimized iff the decoding converges to a valid codewordJ is also a function of H. It is adapted such that unreliable bits are separated in order to avoid getting stuck at zero gradient points:
37Two Stage Optimization Procedure Proposed algorithm is a generalization of the iterative decoding scheme proposed by Lucas et al. (1998), two-stage optimization procedure:A variation of standard gradient descentThe damping coefficient serves to control the convergent dynamics
38Avoid Zero Gradient Point Adaptive scheme changes the gradient and prevents it getting stuck at zero gradient pointsZero gradient pointThink about erasure channel. This does happen! J is not minimized but we get zero gradient!
39Variations of the Generic Algorithm Connect unreliable bits as deg-2Incorporate this algorithm with hard decision decoderAdapting the parity check matrix at symbol levelExchange bits in reliable and unreliable part. Run the decoder multiple timesReduced complexity partial updating schemeNot going deep to details!
49Embed the Proposed Algorithm in the Turbo Equalization System sourceRS EncoderinterleavingPR Encodersinkhard decision+AWGNRS DecoderBCJR Equalizerde-interleavingRS Coded Turbo Equalization System-+a prioriextrinsicinterleaving
55Potential Problems in Applications “In theory, there is no difference between theory and practice.But, in practice, there is…” (Jan L.A. van de Snepscheut)General Problems:Coding gain may shrink down in practical systemsConcatenated with CC: difficult to generate the soft informationPerformance in the practical SNRs should be analyzedRespective problems for various decoding schemes:Reliability assisted HDD: Gain is marginal in practical SNRsAlgebraic soft decoding: performance is limited by the algebraic natureReliability based decoding: huge memory, not scalable with SNRSub-code decomposition: only possible for very short codesIterative decoding: adapting Hb at each iteration is a huge costIn practice, there is a huge difference!
56A Case Study (System Setups) Forward Error Control of a Digital Television Transmission Standard:Modulation format: 64 or 16 QAM modulation (semi-set partitioning mapping)Inner code: convolutional code rate=2/3 or 8/9Bit-interleaved coded modulation (BICM)Iterative demodulation and decoding (BICM-ID)The decoded bytes from inner decoder are interleaved and fed to outer decoderOuter code: RS(208,188) using hard decision decodingWill soft decoding algorithm significantly improve the overall performance?Case study!
57A Case Study (Simulation Results) Coding gain may shrink in practical systems
58Future WorksHow to incorporate the proposed ADP with other soft decoding schemes?Taking advantage of the inherent structure of RS codes at bit levelMore powerful decoding tool, e.g., trellisExtend the idea of adaptive algorithms to demodulation and equalizationApply the ADP algorithm to quantization or to solve K-SAT problemsNo really successful bit level soft decoder which can handle long constraint length codes