J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449--2457, 1995.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....speech vocoder produces line spectral parameters that contains 41:5 of residual redundancy due to non uniformity and memory [3] Therefore, the reliable communication of sources with a considerable amount of residual or natural redundancy is an important issue. Several studies (e.g. 1] 4] [10, 13, 16, 20, 21], etc. have shown that appropriate use of the source redundancy can signi cantly improve the system performance. Turbo codes [6, 7] have been regarded as one of the most exciting breakthroughs in channel coding, and excellent performance has been demonstrated for uniform i.i.d. sources over ....

J. Hagenauer, \Source controlled channel decoding," IEEE Trans. Commun., Vol. 43, pp. 2449-2457, Sept. 1995.

....delay constraints. One variation of joint source channel coding (JSCC) utilizes the source induced dependency that, intentionally or unintentionally, remains in the compressed bitstream. This residual redundancy and the techniques that exploit it have been investigated in a variety of guises [1, 2]. In particular, it is possible to perform iterative (turbo) decoding between the redundancy of a channel code and the residual redundancy of a source code [3] This versatile technique can be used, for example, when the stream is entropy coded by a variable length code (VLC) Often there is ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449--2457, September 1995.

....we demonstrate that, when the source is strongly non uniform, there can be a large gain in performing MAP decoding as compared with ML decoding. This is consistent with previous joint source channel coding work regarding the transmission of non uniform sources in single antenna systems (e.g. [1, 2, 5, 9, 13]) To the best of our knowledge, there is no work in the literature on performance analysis or simulation of space time codes under MAP decoding. II. SYSTEM MODEL The multi antenna communication system considered here employs L T transmit and LR receive antennas. The input to the system is an ....

J. Hagenauer, "Source controlled channel decoding," IEEE Trans. Commun. , vol. 43, pp. 2449-2457, Sept. 1995.

....as much as 80 of redundancy in the form of non uniformity (e.g. 18] 49] this corresponds to the a priori probability P rfU k = 0g = 0:97: Therefore, transmission of sources with a considerable amount of residual or natural redundancy is an important issue. Several studies (e.g. 4] 6] [31, 41, 65, 75, 77], etc. have shown that appropriate use of the source redundancy can signi cantly improve the system performance. In this chapter, we investigate the issue of designing Turbo codes for non uniform i.i.d. sources sent over AWGN and Rayleigh fading channels. Unlike the methods in the previous ....

J. Hagenauer, \Source controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449-2457, Sept. 1995.

....repeatedly in subsequent iterations, hence to remove the dependence of the a priori information from the soft outputs in each iteration. It is this extrinsic information that is shared between the constituent decoders, not the actual a posteriori probabilities. Further analysis of turbo codes in [44 47] illuminated both the originally rather ambiguous turbo decoding principle and the development of SISO decoding algorithms suitable for iterative decoding. Soon after the inception of turbo codes, Benedetto et al. applied the turbo principle to the more conventional serially concatenated coding ....

J. Hagenauer, \Source-controlled channel decoding," IEEE Transactions on Communications,vol. 43, no. 9, pp. 2449-57, Sept. 1995.

....these techniques provide improved signal protection against channel errors, with no additional bandwidth requirement, using only the redundancy left due to suboptimal source coding. Researchers have suggested to employ the residual redundancy for improving the performance of channel coders e.g. [3] [7] or designing e#ective source decoders (error concealment units) e.g. 8] 16] Also, iterative schemes that exploit these redundancies during both channel and source decoding processes have been proposed [17] 19] In general, the problem is formulated in the form of a Maximum A Posteriori ....

....exploit the redundancies between the adjacent frames. Next, the resulting 10 dimensional LSF prediction residue vector is quantized using a 3 split Split VQ [47] with an overall rate of 26 bits per frame (bpf) In other words, the vector of prediction residues are split to 3 vectors of dimensions [3, 3, 4] and quantized using 3 full search VQs with bit rates [8, 9, 9] bits, respectively. This configuration is used in 4 out of the 8 rates available in the GSM AMR standard [33] and is identical to what is used in the IS 641 standard [34] Other GSM AMR codec rates use slightly di#erent configurations ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

....coders for improved reconstruction over noisy channels has found increasing attention [22] 41] This redundancy is due to the suboptimal source coding which is caused by, e.g. a constraint on complexity or delay. Researchers have used the residual redundancy for enhanced channel decoding, e.g. [23] [27] or for e#ective source decoding, e.g. 28] 32] The problem is formulated in the form of a Maximum A Posteriori detection or a Minimum Mean Squared Error estimation problem. In [33] Phamdo and Farvardin proposed instantaneous MAP and MMSE decoders as well as a MAP sequence decoder using ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

....redundancy [8] in the output of the source coder for improved reconstruction over noisy channels. This redundancy is due to the suboptimal source coding which is caused by, e.g. a constraint on complexity or delay. In general, this redundancy can be used for enhanced channel decoding, e.g. [9] [13] or for e#ective source decoding, e.g. 14] 19] This is formulated in the form of a Maximum A Posteriori (MAP) detection or a Minimum Mean Squared Error (MMSE) estimation problem. The residual redundancy is utilized both at the source and channel decoders in [20] which demonstrates an ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

....[2] This algorithm operates in the same way as the SLVA [3] except that it uses a different transition metric that has been modified to take into account a priori probabilities of the information bits. The same technique has previously been applied to the soft output Viterbi algorithm (SOVA) [4] as well as sequential decoding [5] The focus on LS MAP decoding is motivated in part by recent research interest in joint source channel decoding [4,6] Due to delay and complexity constraints, the output from many practical source coding schemes still contains a significant amount of ....

....account a priori probabilities of the information bits. The same technique has previously been applied to the soft output Viterbi algorithm (SOVA) 4] as well as sequential decoding [5] The focus on LS MAP decoding is motivated in part by recent research interest in joint source channel decoding [4,6]. Due to delay and complexity constraints, the output from many practical source coding schemes still contains a significant amount of redundancy. This implies that system improvements may be at hand by exploiting this redundancy to improve the performance of the channel decoder. In this paper, we ....

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J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449-2457, May 1995.

....was carried out in part with support from the Commonwealth of Australia through the Cooperative Research Centre for Satellite Systems. signal to noise ratios (SNRs) although they provide excellent results for moderately distorted channels. On the other hand, joint source channel decoding (JSCD) [2] is another approach to combat channel noise by exploiting the residual redundancy of the source encoder at the receiver side. The source redundancy can be utilised as a priori information of the source statistics to help decoding. To further exploit the turbo principle [3] to joint ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449 2457, Sept. 1995.

....P (i ) Fig. 4. 1) Channel decoding aided by residual redundancies and (2) estimation based source decoding residual bit error rate. This concept can be implemented very e#cient for the decoding of convolutional codes; moreover, the idea can also be extended to soft output decoders (APRI SOVA, [5]) which supply reliability information and not only hard decisions for the data bits. These reliabilities can be exploited by estimation based source decoders described below. For a correct weighting of the a priori information and the soft channeloutputs, a channel state information (CSI) ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449--2457, Sept. 1995.

....a lot of interest due to substantial performance improvements in practical communication systems with constraints on delay and complexity. Many authors have considered the design of joint decoders, which exploit residual redundancies in the outputs of the source encoder for channel decoding, e.g. [1, 2]. Another path of joint sourcechannel coding is channel optimized vector quantization [3] The basic idea is to include the probabilities of index permutations due to channel errors into the codebook design and into the distance measure for VQ encoding. The adaptation of the modulation signal ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449-- 2457, Sept. 1995.

....In the past, two main approaches could be distinguished: In the first type, the residual redundancies in the data bits are used as a priori information in channel decoding in order to reduce the bit error rate after decoding. One example is source controlled channel decoding stated in [4] for the decoding of binary convolutional channel codes. The idea has been extended in [5] and [6] for the use of non binary a priori information. The problem of these approaches is, that the actual quality criterion in the transmission of waveform signals (e.g. speech, audio) is not the ....

....of the indices, which usually results from imperfect source encoding. The drawback of this approach is, that a possibly required channel code is decoded independently of this estimation procedure. Although both methods could be combined, e.g. by application of the APRI SOVA algorithm [4] for channel decoding followed by the soft bit source decoding [9] there is still a separation between source and channel decoding, since the channel decoder does not take advantage of the results of soft output source decoding. In [13] an optimal algorithm for joint source channel decoding ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449 2457, Sept. 1995.

....decoding [2] 3] or without any channel coding at all. The estimation is carried out for parameters of the source codec rather than for single bits of the parameter indices since the dependencies of the indices are stronger than the correlations of the index bits. The algorithm introduced in [4] processes the received soft values at the channel output but also the a priori knowledge on the source encoder index bits for soft output decoding of a convolutional channel code. In [5] a related algorithm is described that also exploits mutual dependencies of the index bits. In [6] multilevel ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449-2457, Sept. 1995.

....to remove all the redundancy from a correlated source signal if the coding delay is limited, adjacent source encoder indices and their bits are more or less correlated. This correlation is exploited to improve the performance of the transmission system in presence of noise on the channel. In [5] an algorithm was stated that accepts soft inputvalues for soft output decoding of a convolutional channel code but also a priori knowledge about the sourceencoder index bits: Their dependencies are modeled by Markov models. The algorithm described in this paper is a trial to close a part of the ....

J. Hagenauer, "Source Controlled Channel Decoding ", IEEE Trans. on Communications, Vol. 43, No. 9, Sept. 1995, pp. 2449--2457

....of a (non binary) chnel code could be adapted to the number N1. In this ce no iterations would be necessy since l the information due to the index correlation could be forwded to the channel decoder in a single step, which would result in a non biny version of source controlled channel decoding [4]. One problem of this approh is the tremendous complexity which is required for the decoding of non binary channel codes with code symbols consisting of 8 bits for instance. The latter is a usual number of index bits e.g. in speech coding. Another problem is due to the fact that only one type of ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449-2457, Sept. 1995.

.... transition metric modified to take into account a priori probabilities of the information bits was used in the last decoding step of an iterative Turbo decoder [3] Single sequence decoding with the same metric modification has previously been applied to the soft output Viterbi algo rithm (SOVA) [4] as well as sequential decoding [5] Single sequence MAP decoding of trellis codes has been studied in [6] However, to our knowledge, little effort has been made to evaluate the performance of LS MAP decoding of convolutional codes. In this paper, we study LS MAP decoding of binary convolutional ....

....source channel SOURCE CRC CONV. ENCODER ENCODER ENCODER SOURCE DECODER A PRIORI VALUES ESTIMATOR [ SOFT SYMBOL VALUES CRC DECODER ERROR FLAG k CHANNEL Fig. 1. Concatenated system with convolutional code and CRC for protection of source coded frames. decoding with application to GSM [4, 8]. We propose a joint source channel LS MAP decoding scheme. The scheme is based on the Max Log List Al gorithm (MLLA) 9] for simultaneous soft symbol and LS decoding of convolutional codes. In systems where a cyclic redundancy check (CRC) for error mitigation is concate nated with a ....

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J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449-2457, May 1995.

....at time k l given y j#k l , is from (4) P (correct) 1 ) p(path i # l , y = exp[M k l (s = exp(# k ) 1 exp(# . 6) Therefore, the likelihood ratio or soft value of this binary path decision is # k , because ln = # k . 7) Furthermore, it was shown in [15] that the soft output of the VA is the decision u k times the soft value of the errors and can finally be approximated by L(u k ) k # j=k time index j k l k # M M u u k ML Path Nonsurviving Path l l=# relative time index l i i Fig. 2. Trellis diagram of SOVA The sum and ....

.... OOK, we obtain M k (s k ) M k 1 (s k 1 ) v (12) where x k is the received sequence of systematic bits, x k is the received sequence of parity bits, and L ) is the log a priori information for OOK defined by = ln 2 ) 13) Furthermore, it was shown as [15] that the soft output of the VA is the decision v k times the soft value of the errors and can finally be approximated by k . 14) j k ) M k j k 1 ) L c w k (v v (L k ) v ( v k ) 15) Using (14) and (15) we obtain the output of SOVA for the Turbo ....

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol.43, no.9, pp. 2449--2457, Sept.

....to the first stage and a second iteration of processing is initiated. Several iterations of turbo processing can be executed, although as with turbo codes, a law of diminishing returns limits the maximum processing gain. Turbo processing can be used to combine channel decoding with source decoding [58], symbol detection [57] equalization [59] or multiuser detection [60] An interesting example of turbo processing is turbo equalization , which is a method of combining equalization with channel decoding [59] An equalizer is a signal processing subsystem that compensates for the intersymbol ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449 2457, Sep. 1995.

....blind equalization is the possibility to make use of a priori information. If a priori information is available, we can replace the ML sequence estimator assumed so far by a maximum a posteriori (MAP) sequence estimator. Then, the branch metrics of the DPSK ISI super trellis are modi ed as [28], 26] 1 (k l) h l (k 1) log P (d (k) 13) where S denotes a state transition and n is the noise variance. Moreover, P (d (k) denotes the a priori probability of the state transition . For binary systems, 13) can be rewritten as ....

J. Hagenauer, \Source controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449-2457, Sept. 1995.

....to the first stage and a second iteration of processing is initiated. Several iterations of turbo processing can be executed, although as with turbo codes, a law of diminishing returns limits the maximum processing gain. Turbo processing can be used to combine channel decoding with source decoding [49], symbol detection [48] equalization [50] or multiuser detection [51] An interesting example of turbo processing is turbo equalization , which is a method of combining equalization with channel decoding [50] An equalizer is a signal processing subsystem that compensates for the intersymbol ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449-2457, Sep. 1995.

....that d was transmitted under the condition that e was received, and P( d) is the a priori probability of d. Since the CMAPSD exploits the a priori probability of d it is perfectly suited for iterative detection decoding receivers, which operate similar to source controlled channel decoders [14]. The CMAPSD can be realized recursively by appropriately modifying the metric calculation used in a traditional MLSD. In the case of the ML criterion, the a priori probability of d is not taken into account. It is assumed that all possible sequences d are equally probable, instead. Hence, the ....

J. Hagenauer, "Source controlled channel decoding," IEEE Transactions on Commu- nications, accepted for publication.

.... one frame but also from frame to frame (time correlation) There are publications focusing on using the source redundancy for source decoding, e.g. 1 3] In [4] and [5] methods are described to exploit the distribution of parameters as a priori information on bit level of the channel decoden In [6] the time correlation on bit level was used to improve channel decoding. In a new approach we use time correlation on a parameter level. Especially in flat fading channels it is known that the time correlation improves channel decoding [ 1] In [7] the combination of the two kinds of a priori ....

J. Hagenauer, "Source- Controlled Channel Decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449457, Sept. 1995.

....source redundancy. Bahl et al. laid the foundations of symbol by symbol channel decoding that is able to exploit a priori knowledge about the source bits [1] Hagenauer investigated sequence estimating channel decoding algorithms and proposed the source controlled channel decoding technique [2]. Further work focused on channel decoding exploiting source statistics to reduce the residual path, symbol, or bit error rate is [3 5] Alternatively, the source redundancy can of course be used in the source decoding process [6 14] often referred to as soft decision or softbit decoding. In ....

J. Hagenauer, "Source- Controlled Channel Decoding, " IEEE Transactions on Communications, vol. 43, pp. 2449 2457, Sept. 1995.

....were up to 80 redundant due to non uniformity. Similar results were obtained in [13] After the information is transmitted over a noisy channel, its redundancy can be appropriately exploited by using a maximum a posteriori (MAP) detector instead of a maximumlikelihood (ML) detector (e.g. 1] [10], 12] 15] 17] hence reducing the error rate of the communication system. A simple example is shown in Fig. 1, in which the use of MAP 2 decoding improves system performance over ML decoding for a highly non uniform source, and in which the gain obtained from non uniformity in the source and ....

J. Hagenauer, \Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449-2457, Sept. 1995.

....forward recursions on a trellis of at most min(2 K ; 2 N GammaK ) states. Suboptimal decoders with reduced complexity have also been considered, most notably are the modifications of the above optimal decoders to reduce complexity [2] and the soft input soft output Viterbi algorithm (SOVA) [3]. 1 After the initial approach of using convolutional codes in iterative decoding several authors have considered block codes as component codes, for example [2, 5] Generally, block codes as component codes perform better than convolutional codes for high rates and vice versa for low rates. In ....

J. Hagenauer, "Source controlled channel decoding," IEEE Trans. Commun., vol IT-43, pp. 2449--2457, Sept. 1995.

....algorithms designed for noiseless transmission [28] Also, there is generally no convenient method for adjusting the level of source redundancy to better match channel conditions. Source redundancy has also been used to modify the a priori bit probabilities in the channel decoding algorithm in [29, 30, 31, 32]. However, it is often not practical to accurately estimate 5 the probabilities of source coder output sequences. Also, in sequence decoding there may be a significant delay before incorrect candidate sequences can be eliminated by the channel decoding algorithm (because they are improbable ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, Sept. 1995, pp. 2449--2457.

....which is due to their sub optimality caused by e.g. a constraint on complexity or delay. As Shannon stated, this redundancy can be used at the receiver to enhance the performance of the system [1] Recently, researchers have used the residual redundancy for enhanced channel decoding e.g. 5] and [6] or for e#ective source decoding e.g. 7] 9] The problem is formulated in the form of a Maximum A Posteriori detection e.g. 3] and [5] or a Minimum Mean Squared Error estimation problem e.g. 4] Several speech error concealment solutions based on MMSE source decoding are presented in [8] and ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

....as 80 of redundancy in the form of non uniformity (e.g. 7] 14] this corresponds to the a priori probability P rfd k = 0g = 0:97. Therefore, transmission of sources with a considerable amount of residual or natural redundancy is an important practical issue. Several studies (e.g. 1] 4] [11], 16] and [19] etc. have shown that appropriate use of the source redundancy can signi cantly improve the system performance. Turbo codes [6] have demonstrated excellent performance for uniform i.i.d. sources over additive white Gaussian noise (AWGN) channels; to the best of our knowledge, the ....

J. Hagenauer, \Source controlled channel decoding," IEEE Trans. Commun., Vol. 43, pp. 2449-2457, Sept. 1995.

....performed. As mentioned before, a nice feature of trellis based blind equalization is the possibility to make use of a priori information. If a priori information is available, we replace the ML sequence estimator assumed so far by a MAP sequence estimator, i.e. the branch metrics are modified as [24], 23] fl Gamma S i (k Gamma 1) S j (k) Delta = Gamma 1 2oe 2 n Delta fi fi fi fi fi y(k) Gamma L X l=0 x i j (k Gamma l) Delta h l (k Gamma 1) fi fi fi fi fi 2 log p(d i j (k) 19) where Gamma S i (k Gamma 1) S j (k) Delta ....

J. Hagenauer, "Source controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449-2457, Sept. 1995.

....speech transmission was chosen, but the presented method could also be applied to any other system which leaves a certain amount of redundancy in the speech coded signal. 1. INTRODUCTION A lot of theoretical investigations about exploiting source residual redundancy have been done in the past [4][5] 6] 7] 8] In this work, the attempt was made to apply the idea of exploiting this redundancy to a current mobile communication system. The GSM Full Rate codec was chosen for various reason: It is a wide spread system and the speech coder uses scalar quantization that leaves a fair amount of ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol.43, pp.2449-2457, September 1995

....algorithm (SOVA) must takeaccountofthe probability that thepathsmergingwiththeML path from stage to stage in the trellis were incorrectly discarded. This is done by considering the values of the metric difference for all states alongtheMLpathfrom trellis stage to . It is shownbyHagenauer in [29] that this LLR can be approximated by (36) where is the value of the bit given by the ML path, and is the value of this bit for the path which merged with the ML path and was discarded at trellis stage . Thus the minimization in (36) is carried out only for those paths merging with the ML path ....

....the metrics of the surviving and the discarded paths is stored, together with a binary vector containing bits, which indicate whether or not the discarded path would have given the same series of bits for back to as the surviving path does. This series of bits is called the update sequence in [29], and as noted by Hagenauer it is given by the result of a modulo two addition (i.e. an exclusive or operation) between the previous decoded bits along the surviving and discarded paths. When the SOVA has identified the ML path, the stored update sequences and metric differences along this path ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449--2457, Sept. 1995.

.... reason for this may be attributable to Shannon s work in information theory which shows that channel and source coding can be treated separately [9] In practice, however, it has been found that considering these two parts jointly will help improve the error control performance of the whole system [4]. Generally speaking, joint channel and source decoding involves an interaction between a channel decoder and a source decoder. Soft channel information can be used to improve source decoding performance. On the other hand, source information can also be used to assist channel decoding. This ....

J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, pp. 2449--2457, Sept. 1995.

....Shannon s ndings are asymptotic in nature assuming no constraints on complexity or delay. Recently, systems with jointly designed source and channel coding operations have been shown to outperform tandem systems under practical limitations such as nite block lengths (e.g. 1] 5] 9] 10] [13], 15] 21] 25] 26] In this work, we consider joint source channel coding methods for the robust communication of Federal Standard CELP 1016 encoded speech [6, 18] More speci cally, we propose and implement unequal error protection (UEP) and source optimized channel coding schemes for the ....

J. Hagenauer, \Source Controlled Channel Decoding, " IEEE Trans. on Commun., Vol. 43, No. 9, pp. 2449-2457, September 1995.

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J. Hagenauer, "Source--controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449--2457, September 1995.

....arises from the fact that the systematic bits are transmitted over the channel and hence all information bits are available in the received bit stream before channel decoding. So, an a priori information for each bit can be calculated if there is redundancy left in the source coded bit stream [5 7] and added to a soft input outside the Viterbi decoder. That means, any Viterbi decoder can exploit a priori knowledge to improve the decoding result. Fig. 3 shows an example for the realization of an RSC code with shift registers. The RSC code uses the generator poly nomials Go = 1 D a D 4 ....

....of information bits. The free distance of two code words in the standardized short block in TCH AFS code is 5. For a rate 1 3 constraint length 7 (5) convolutional code it is 14 (12) and for rate 1 4 it is 20 (15) 9] The complexity increase is modest, e.g. if a soft output Viterbi algorithm [5] is used, merely the decision feed back has to be done twice for the first 23 bits. This feedback is much less complex than the forward trellis construction which runs through at once. In this example, the hierarchical system was integrated in the standard with small modifications. Remember that ....

J. Hagenauer, "Source-Controlled Channel Decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 24494457, Sept. 1995.

.... to frame (time correlation) There are publications focusing on using the source redundancy for source decoding, e.g. 4, 7, 16] Some methods are described to exploit the distribution of parameters as a priori information for channel coding, on bit level in [1] and on parameter level in [2] In [3] the time correlation on bit level was used to improve channel decoding. In a new approach we use time correlation on parameter level. Especially in flat fading channels it is known that the time correlation improves channel decoding. In [ 10] the combination of the two kinds of a priori knowledge ....

J. Hagenauer, "Source controlled channel decoding", IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449457, Sept. 1995

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J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, no. 9, pp. 2449--2457, 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

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J. Hagenauer, \Source-Controlled Channel Decoding," IEEE Trans. Comm., vol. 43, pp. 2449-2457, Nov. 1995.

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J. Hagenauer, "Source-Controlled Channel Decoding," IEEE Trans. on Communications, pp. 2449--2457, Sep. 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Transactions on Communications, vol. 43, pp. 2449--2457, Sept. 1995.

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J. Hagenauer. Source-controlled channel decoding. IEEE Trans. on Comm., 43(9):2449--2457, September 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. on Comm., vol. 43, no. 9, pp. 2449--2457, Sept. 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. Commun., vol. 43, No. 9, Sept. 1995.

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J. Hagenauer, "Source controlled channel decoding," IEEE Trans. Commun., Vol. 43, pp. 2449--2457, Sept. 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. on Comm., vol. 43, no. 9, pp. 2449--2457, Sept. 1995.

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J. Hagenauer. Source-controlled channel decoding. IEEE Trans. on Comm., 43(9):2449--2457, Sept. 1995.

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J. Hagenauer, "Source-controlled channel decoding," IEEE Trans. on Comm., vol. 43, no. 9, pp. 2449--2457, Sept. 1995.

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J. Hagenauer. Source-controlled channel decoding. IEEE Trans. on Comm., 43(9):2449--2457, Sept. 1995.

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