Parameter Estimation of a Convolutional Encoder from Noisy Observations

Dingel, J.; Hagenauer, J.

doi:10.1109/isit.2007.4557147

Cited by 49 publications

(52 citation statements)

References 12 publications

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“…Using the syndrome former of the code, we get the following parity check matrix 8 In other words λ i,i is small for any i and i , with i = i . 9 in the order of 100 as we have seen from our numerical experiments. 10 The rest of the entries in the matrix are zero.…”

Section: A Analysis Of the Code Detection Performancesupporting

confidence: 68%

“…For instance in [3]- [5] novel schemes for blind classification of modulation formats have been proposed. In [6]- [9], blind identification of encoder…”

Section: Introductionmentioning

confidence: 99%

“…This problem is therefore different from [3]- [9] in the sense that (i) we assume that the modulation format is known a priori, and (ii) the objective is to recognize or verify which one of the channel codes out of the possible codes (which is denoted by the "candidate set" from hereon) was used to encode the data stream. To the best of our knowledge, there is very little work in the literature addressing this problem in its generality.…”

Section: Introductionmentioning

confidence: 99%

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Fast Blind Recognition of Channel Codes

Moosavi

Larsson

2014

IEEE Trans. Commun.

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Abstract-We present a fast algorithm that, for a given input sequence and a linear channel code, computes the syndrome posterior probability (SPP) of the code, i.e., the probability that all parity check relations of the code are satisfied. According to this algorithm, the SPP can be computed blindly, i.e., given the soft information on a received sequence we can compute the SPP for the code without first decoding the bits. We show that the proposed scheme is efficient by investigating its computational complexity.We then consider two scenarios where our proposed SPP algorithm can be used. The first scenario is when we are interested in finding out whether a certain code was used to encode a data stream. We formulate a statistical hypothesis test and we investigate its performance. We also compare the performance of our scheme with that of an existing scheme. The second scenario deals with how we can use the algorithm for reducing the computational complexity of a blind decoding process. We propose a heuristic sequential statistical hypotheses test to use the fact that in real applications, the data arrives sequentially, and we investigate its performance using system simulations.

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Section: A Analysis Of the Code Detection Performancesupporting

confidence: 68%

“…For instance in [3]- [5] novel schemes for blind classification of modulation formats have been proposed. In [6]- [9], blind identification of encoder…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Blind Recognition of Channel Codes

Moosavi

Larsson

2014

IEEE Trans. Commun.

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“…(4) here. There is also some relation to methods for estimation of parameters of convolutional codes [7], but it does not appear that the parameter estimates derived therein could be usefully exploited for the detection task at hand.…”

Section: Fast Algorithm For Detection Of the Albmentioning

confidence: 99%

Piggybacking an Additional Lonely Bit on Linearly Coded Payload Data

Larsson

Moosavi

2012

IEEE Wireless Commun. Lett.

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Abstract-We provide a coding scheme, by which an additional lonely bit can be piggybacked on a payload data packet encoded with a linear channel code, at no essential extra cost in power or bandwidth. The underlying principle is to use the additional bit to select which of two linear codes that should be used for encoding the payload packet, this way effectively creating a nonlinear code. We give a fast algorithm for detecting the additional bit, without decoding the data packet. Applications include control signaling, for example, transmission of ACK/NACK bits.

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“…However, it is not suitable for noisy channels. In [19], another approach was presented to identify a 1/n rate convolutional encoder in noisy cases based on the Expectation Maximization algorithm. The authors of [20,21] developed methods for blind recovery of convolutional encoders in turbo code configuration.…”

Section: Introductionmentioning

confidence: 99%

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

Zhou

Huang

Liu

et al. 2013

Entropy

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A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN) channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.

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Parameter Estimation of a Convolutional Encoder from Noisy Observations

Cited by 49 publications

References 12 publications

Fast Blind Recognition of Channel Codes

Fast Blind Recognition of Channel Codes

Piggybacking an Additional Lonely Bit on Linearly Coded Payload Data

Information-Dispersion-Entropy-Based Blind Recognition of Binary BCH Codes in Soft Decision Situations

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