Likelihood Soft Decision Research Articles

A Soft Decoding Algorithm with Hard Decision Boundary Factor for High-Order M-APSK Signal

High-order amplitude phase shift keying is adopted in the latest digital satellite broadcasting system, which presents new challenges to the design of a receiver demodulation scheme. This study focuses on the soft decision decoding algorithm. Traditionally, maximum likelihood soft decision based on log likelihood ratio estimation is often used as a decoding scheme. Accordingly, a soft decision algorithm based on hard decision boundary is designed and the corresponding soft decision estimation formula is derived. Numerical analytical results show that the proposed soft decision algorithm can improve the error performance of a system and substantially reduce the complexity of implementation.

Design of an Efficient Maximum Likelihood Soft Decoder for Systematic Short Block Codes

Maximum likelihood soft-decision decoding of linear block codes is addressed in this correspondence. A novel algorithm based on Chase-2 algorithm for the decoding of systematic binary block codes is detailed. A double re-encoding technique in place of the classical algebraic decoding for the computation of the candidate codeword list is the major innovation of the proposed algorithm. This approach has been successfully applied to systematic block codes that have a code rate equal to 1/2 and a parity check matrix composed of an invertible submatrix for the redundancy part. Simulation results show performance close to the optimum maximum likelihood decoding for an excellent tradeoff between BER performance and computational complexity. Then, the challenging issue of designing a decoder for a specific family of short binary block codes, called Cortex codes is also described. Three soft decoders for Cortex codes with lengths equal to 32, 64, and 128 and a code rate equal to 1/2 have been designed. Then, all the decoders were successively implemented onto an field-programmable gate array (FPGA) device. To our knowledge, they are the first efficient digital implementations of Cortex codes.

Near maximum likelihood soft-decision decoding of a particular class of rate-1/2 systematic linear block codes

Presented is a soft-decision decoding algorithm for a particular class of rate-1/2 systematic linear block codes. The proposed algorithm performs successive re-encoding of both the data and parity bits, to produce a list of codewords among which the most likely candidate is chosen. Simulation results show that close to optimal performance can be obtained at reasonable complexity. They validate the potential of the proposed algorithm as a practical approach for soft-decision decoding.

Novel near maximum likelihood soft decision decoding algorithm for linear block codes

The authors present a novel near maximum likelihood (ML) soft decision decoding (SDD) algorithm for linear block codes. The proposed algorithm can be subdivided into three distinct algorithms, each achieving a specific objective. The first algorithm achieves near ML decoding performance by utilising channel measurement information. The second and third algorithms maintain the improved decoding performance achieved, while at the same time reducing both the number of decodings and complexity required. The resultant algorithm is both general in nature and able to offer significant performance improvements. The theoretical results are verified by computer simulation.

On maximum likelihood soft-decision decoding of binary linear codes

Two new implementations of a maximum-likelihood soft-decision decoding algorithm for binary linear codes are derived. Instead of checking only error patterns yielding a codeword when added to the rounded-off version of the received vector, these implementations principally consider all possible column patterns. Compared to known implementations, this allows a more efficient use of branching and bounding techniques. >