I. Sason and S. Shamai, "Improved Upper Bounds on the ML Decoding Error Probability of Parallel and Serial Concatenated Turbo Codes via their Ensemble Distance Spectrum," IEEE Transactions on Information Theory, vol.46, No.1, pp. 24-47, January 2000.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....works have addressed the problem of computing tight lower and upper bounds on the error probability of binary block codes. This includes the lower bounds given in [6, 7] and the upper bounds given in [8, 9] More recently various lower bounds have been derived on the performance of Turbo codes in [10 12]. All these bounding techniques are based on ML block decoding (versus bit decoding as used in the current article) Reference [13] presents lower and upper bounds on the block error probability including the e ect of non uniform source probability. Recently, we have investigated some properties ....

I. Sason, S. Shamai, \ImprovedUpper Bounds on the ML Decoding Error Probability of Parallel and Serial Concatenated Turbo Codes via Their Ensemble Distance Spectrum," IEEE Transactions on Information Theory, Vol.46, No.1, pp. 24-47, January 2000.

....of the error performance. The Viterbi bound presented in [60] and further elaborated in [1] is a modification of an exponential error bound introduced by Gallager [28] The tangential sphere bound was first derived by Poltyrev [46] and recently developed for Turbo codes by Shamai et al. in [51], Tangential Sphere Bound Union Bound Viterbi Bound 10 z 10 5 , 0 1 2 3 4 5 Figure 4.5: Upper bounds on the ML decoding performance for a rate 1 8, Turbo code with component code generator polynomials [178,168,158,118] 138 and a 100 bit uniform interleaver. The tangential sphere ....

....and the number of erroneous bits corresponding to single error events is contained in aa and ca, respectively. The Viterbi bound and the tangential sphere bound require more tedious calculations, and the details will therefore not be restated here. The reader is referred to [1, 28, 60] and [46, 51] for details on the Viterbi bound and the tangential sphere bound, respectively. FER aJCP2(d) 4.12) 4.4.2 Extrinsic Information Transfer Analysis Extrinsic information transfer (EIT) analysis, which is a tool for prediction of the convergence of the iterative decoding process of Turbo codes ....

I. Sason and S. Shamai. Improved upper bounds on the ml decoding error proba- bility of parallel and serial concatenated Turbo codes via their ensemble distance spectrum. IEEE Transactions on Information Theory, 46(1):24-47, Jan. 2000.

....The modification to the union bounds is used in the performance analysis that follows. 2.4 Tangential Bound The union bound, by its nature, becomes quite loose at SNRs lower than the threshold corresponding to the channel cut o# rates. There have been several tighter bounds proposed recently [10, 11, 12, 13], which can be adopted to extend the useful region lower than the cut o# rate on AWGN channels. However, these improved bounds are not readily applicable to wireless communication systems, where fading channels are more typical. In the following, 8 we extend the tangential bound [12, 13] to an ....

I. Sason and S. Shamai (Shitz), "Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," IEEE Transactions on Information Theory, vol. 46, no. 1, January 2000.

....decoding strategy with respect to ML decoding has been available for years. Recently, several authors tried to extend the range of tightness of the upper bounds to lower SNR s by applying a modi cation of the Gallager bound [10] 4] 11] or by using the tangential sphere bound of Poltyrev [6] [9]. In our approach we start by transforming the decision variables as shown in [11] but continue adopting an idea mentioned in [7] and nally get a new tight upper bound applicable to any linear code with known weight distribution. Unfortunately, the mathematical derivation in one step is exact ....

....we apply our new approximate bounds via numerical integration ( ABN ) as well as in closed form ( ABC ) to several codes and compare them to simulation results. In order to have further references we also calculate the standard union bound and the tangential sphere bound ( TSB ) of Poltyrev [6] [9]. The TSB is the tightest upper bound on the error probability of ML decoding known up to now and signi cantly better than the approaches based on the Gallager bound [4] 11] A. Hadamard Code The weight enumerating function A(W ) describes the weight distribution of a linear code and is de ned ....

I. Sason, and S. Shamai (Shitz), \Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," to appear in IEEE Transactions on Information Theory.

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I. Sason and S. Shamai, "Improved Upper Bounds on the ML Decoding Error Probability of Parallel and Serial Concatenated Turbo Codes via their Ensemble Distance Spectrum," IEEE Transactions on Information Theory, vol.46, No.1, pp. 24-47, January 2000.

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I. Sason and S. Shamai, "Improved Upper Bounds on the ML Decoding Error Probability of Parallel and Serial Concatenated Turbo Codes via their Ensemble Distance Spectrum," IEEE Transactions on Information Theory, vol.46, no.1, pp. 24-47, January 2000.

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I. Sason and S. Shamai, "Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," IEEE Trans. on Inform. Theory, vol. 46, pp. 24--47, January 2000.

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I. Sason and S. Shamai, "Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," IEEE Trans. Inform. Theory, vol. 46, pp. 24--47, Jan. 2000.

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I. Sason and S. Shamai (Shitz), "Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," IEEE Trans. Inform. Theory, vol. 46, pp. 24--47, Jan. 2000.

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I. Sason and S. Shamai, "Improved Upper Bounds on the ML Decoding Error Probability of Parallel and Serial Concatenated Turbo-Codes via their En- semble Distance Spectrum," IEEE Transactions on Information Theory, vol. 46, no. 1, pp. 24-47, January 2000.

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I. Sason and S. Shamai, "Improved upper bounds on the ML decoding error probability of parallel and serial concatenated turbo codes via their ensemble distance spectrum," IEEE Trans. Inform. Theory, vol. 46, pp. 24--47, Jan. 2000.

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