Topic
Sequential decoding
About: Sequential decoding is a research topic. Over the lifetime, 8667 publications have been published within this topic receiving 204271 citations.
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TL;DR: It is shown that the reliability function of the proposed decoding algorithm is asymptotically twice that of the Viterbi decoding algorithm without repeat request, and that in certain practical situations the proposed algorithm can save about 50 percent in constraint length over the ordinary Viterba algorithm for a given performance.
Abstract: Using the Viterbi decoding algorithm with repeat request for convolutional codes is proposed, and the resulting performance is analyzed by random coding and generating function arguments and by simulation. It is shown that the reliability function of the proposed decoding algorithm is asymptotically twice that of the Viterbi decoding algorithm without repeat request, and that in certain practical situations the proposed algorithm can save about 50 percent in constraint length over the ordinary Viterbi algorithm for a given performance.
159 citations
TL;DR: A systematic maximum-likelihood approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions is taken and a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data is derived.
Abstract: Space-time coding (STC) schemes for communication systems employing multiple transmit and receive antennas have been attracting increased attention. The so-called orthogonal space-time block codes (OSTBCs) have been of particular interest due to their good performance and low decoding complexity. In this paper, we take a systematic maximum-likelihood (ML) approach to the decoding of OSTBC for unknown propagation channels and unknown noise and interference conditions. We derive a low-complexity ML decoding algorithm based on cyclic minimization and assisted by a minimum amount of training data. Furthermore, we discuss the design of optimal training sequences and optimal information transfer to an outer decoder. Numerical examples demonstrate the performance of our algorithm.
157 citations
31 Oct 2005
TL;DR: A new construction of rank codes is presented, which defines new codes and includes known codes, and it is argued that these are different codes.
Abstract: The only known construction of error-correcting codes in rank metric was proposed in 1985. These were codes with fast decoding algorithm. We present a new construction of rank codes, which defines new codes and includes known codes. This is a generalization of E.M. Gabidulin, 1985. Though the new codes seem to be very similar to subcodes of known rank codes, we argue that these are different codes. A fast decoding algorithm is described
155 citations
TL;DR: A generalized VA (GVA) is proposed that makes comparisons on the basis of a label of length L(L\leq K) that decoding theorems for a discrete memoryless channel are proved for GVA decoding and shown to be natural generalizations of those for VA decoding.
Abstract: The Viterbi algorithm (VA), an optimum decoding rule for a Q -ary trellis code of constraint length K , operates by taking the best survivor from each of Q^{K-1} lists of candidates at each decoding step. A generalized VA (GVA) is proposed that makes comparisons on the basis of a label of length L(L\leq K) . It selects, incorporating the notion of list decoding, the S best survivors from each of Q^{L-1} lists of candidates at each decoding step. Coding theorems for a discrete memoryless channel are proved for GVA decoding and shown to be natural generalizations of those for VA decoding. An example of intersymbol interference removal is given to illustrate the practical benefits that the GVA can provide.
155 citations
TL;DR: The geometric perspective clearly indicates the relationship between turbo-decoding and maximum-likelihood decoding, and analysis of the geometry leads to new results concerning existence of fixed points, condition for uniqueness, conditions for stability, and proximity to maximum- likelihood decoding.
Abstract: The spectacular performance offered by turbo codes sparked intense interest in them. A considerable amount of research has simplified, formalized, and extended the ideas inherent in the original turbo code construction. Nevertheless, the nature of the relatively simple ad hoc turbo-decoding algorithm has remained something of a mystery. We present a geometric interpretation of the turbo-decoding algorithm. The geometric perspective clearly indicates the relationship between turbo-decoding and maximum-likelihood decoding. Analysis of the geometry leads to new results concerning existence of fixed points, conditions for uniqueness, conditions for stability, and proximity to maximum-likelihood decoding.
154 citations