Suboptimum decoding of block codes

This paper investigates a class of decomposable codes, their distance and structural properties. it is shown that this class includes several classes of well known and efficient codes as subclasses. Several methods for constructing decomposable codes or decomposing codes are presented. A two-stage soft decision decoding scheme for decomposable codes, their translates or unions of translates is devised. This two-stage soft-decision decoding is suboptimum, and provides an excellent trade-off between the error performance and decoding complexity for codes of moderate and long block length

Constructions of Generalized Concatenated Codes and Their Trellis-Based Decoding Complexity

In this correspondence, constructions of generalized concatenated (GC) codes with good rates and distances are presented. Some of the proposed GC codes have simpler trellis omplexity than Euclidean geometry (EG), Reed–Muller (RM), or Bose–Chaudhuri–Hocquenghem (BCH) codes of approximately the same rates and minimum distances, and in addition can be decoded with trellis-based multistage decoding up to their minimum distances. Several codes of the same length, dimension, and minimum distance as the best linear codes known are constructed

Unequal Error Protection QPSK Modulation Codes

The authors use binary linear UEP (LUEP) codes, in combination with a QPSK signal set and Gray mapping, to obtain new efficient block QPSK modulation codes with unequal minimum squared Euclidean distances. They give several examples of codes that have the same minimum squared Euclidean distance as the best QPSK modulation codes of the same rate and length. A new suboptimal two-stage soft-decision decoding is applied to LUEP QPSK modulation codes

Bounds on Block Error Probability for Multilevel Concatenated Codes

Maximum likelihood decoding of long block codes is not feasable due to large complexity. Some classes of codes are shown to be decomposable into multilevel concatenated codes (MLCC). For these codes, multistage decoding provides good trade-off between performance and complexity. In this paper, we derive an upper bound on the probability of block error for MLCC. We use this bound to evaluate difference in performance for different decompositions of some codes. Examples given show that a significant reduction in complexity can be achieved when increasing number of stages of decoding. Resulting performance degradation varies for different decompositions. A guideline is given for finding good m-level decompositions

Soft-decision decoding techniques for linear block codes and their error performance analysis

The first paper presents a new minimum-weight trellis-based soft-decision iterative decoding algorithm for binary linear block codes. The second paper derives an upper bound on the probability of block error for multilevel concatenated codes (MLCC). The bound evaluates difference in performance for different decompositions of some codes. The third paper investigates the bit error probability code for maximum likelihood decoding of binary linear codes. The fourth and final paper included in this report is concerns itself with the construction of multilevel concatenated block modulation codes using a multilevel concatenation scheme for the frequency non-selective Rayleigh fading channel

Trellises and Trellis-Based Decoding Algorithms for Linear Block Codes

In a coded communication system with equiprobable signaling, MLD minimizes the word error probability and delivers the most likely codeword associated with the corresponding received sequence. This decoding has two drawbacks. First, minimization of the word error probability is not equivalent to minimization of the bit error probability. Therefore, MLD becomes suboptimum with respect to the bit error probability. Second, MLD delivers a hard-decision estimate of the received sequence, so that information is lost between the input and output of the ML decoder. This information is important in coded schemes where the decoded sequence is further processed, such as concatenated coding schemes, multi-stage and iterative decoding schemes. In this chapter, we first present a decoding algorithm which both minimizes bit error probability, and provides the corresponding soft information at the output of the decoder. This algorithm is referred to as the MAP (maximum aposteriori probability) decoding algorithm