# Reduced complexity Log-MAP algorithm with Jensen inequality based non-recursive max∗ operator for turbo TCM decoding

- Michal Sybis
^{1}and - Piotr Tyczka
^{1}Email author

**2013**:238

https://doi.org/10.1186/1687-1499-2013-238

© Sybis and Tyczka; licensee Springer. 2013

**Received: **22 February 2013

**Accepted: **16 September 2013

**Published: **26 September 2013

## Abstract

In this paper, a reduced complexity Log-MAP algorithm based on a non-recursive approximation of the max∗ operator is presented and studied for turbo trellis-coded modulation (TTCM) systems. In the algorithm, denoted as AvN Log-MAP, the max∗ operation is generalized and performed on *n*≥2 arguments. The approximation is derived from the Jensen inequality. The non-recursive form of the max∗ calculations allows to achieve significant reduction in the decoding effort in comparison to the conventional Log-MAP algorithm. Bit-error rate performance simulation results for serial and parallel TTCM schemes in the additive white Gaussian noise and uncorrelated Rayleigh fading channels show that the AvN Log-MAP algorithm performs close to the Log-MAP. Performance and complexity comparisons of the AvN Log-MAP algorithm against the Log-MAP and several relevant reduced complexity turbo decoding algorithms proposed in the literature reveal, that it offers favorable low computational effort for the price of small performance degradation.

### Keywords

Decoding algorithms Log-MAP algorithm Reduced-complexity algorithms max∗ operator Jacobian logarithm Turbo TCM## 1 Introduction

The invention of turbo codes and their iterative (turbo) decoding principle[1, 2] has opened new perspectives on digital transmission and receiver design. Owing to the excellent performance, turbo codes have been extensively studied and have found applications in various wireless communication systems. Moreover, several enhanced turbo-like schemes have been proposed in recent years, with the aim of improving the overall bandwidth efficiency. On the other hand, the idea of iterative processing has found widespread applications not only in error control coding but also in other areas of digital communications, such as detection, interference suppression, equalization and synchronization. Nowadays, iterative processing has become prevalent in state-of-the-art receiver design.

It is known that in turbo decoding, the optimal algorithm for the soft-input/soft-output (SISO) component decoders is the *maximum a posteriori* (MAP) probability algorithm[3]. In practice, in order to reduce numerical computation problems, the MAP algorithm is implemented in the logarithmic domain that results in the so-called Log-MAP algorithm[4, 5]. The core operation of the Log-MAP is the calculation of the logarithm of the sum of exponential terms, denoted as max∗ operator, using the so-called Jacobian logarithm[6]. In recent years, several algorithmic approaches have been proposed aiming for a simplification of the max∗ operator and thus reducing the implementation complexity of the SISO decoders without a substantial loss of decoding performance (e.g.,[7–13]). In all these algorithms, a conventional max∗ operator, i.e., defined for *n*=2 input values, is modified, and for *n*>2, these approximations are applied recursively *n*-1 times. Generalized non-recursive approximation methods for max∗ operator with *n*>2 arguments have been recently presented in[14] and[15].

In this paper, we revisit a reduced-complexity Log-MAP algorithm based on a non-recursive approximation of the max∗ operator with *n*≥2 input values, which has been recently proposed in[16]. The novel approximation given in[16], is derived from the Jensen inequality[17] and allows to achieve significant reduction in the decoding effort in comparison to the conventional Log-MAP algorithm. The purpose of this paper is to expand the initial work reported in[16] and to present comprehensive performance and complexity results and analysis of the algorithm, hereafter called the AvN Log-MAP algorithm (AvN is the acronym of 'Average with the parameter *N*’), for turbo trellis-coded modulation (TTCM) schemes[18, 19]. Performance evaluation results of the AvN Log-MAP algorithm are presented for both parallel and serial TTCM schemes in the additive white Gaussian noise (AWGN) and uncorrelated (i.e., fully interleaved) Rayleigh fading channels. For TTCM, the trellises are non-binary and hence, in the Log-MAP operation all variables are calculated by means of the max∗ operator with *n*>2 arguments. Bit-error rate (BER) results of the AvN Log-MAP algorithm are compared with the results of the Log-MAP and some relevant reduced complexity decoding algorithms, namely Linear Log-MAP[8], Average Log-MAP[9], Shift Log-MAP[10] and Max-Log-MAP[4]. Complexity comparison of the AvN Log-MAP algorithm against those algorithms for the investigated TTCM schemes is also given. The paper is organized as follows. In section 2, for the sake of completeness, we review the derivation of the non-recursive max∗ approximation for the AvN Log-MAP algorithm, given in[16]. Section 3 presents the performance evaluation results and their discussion. Complexity comparison of the algorithms is covered in section 4. Finally, section 5 contains concluding remarks.

## 2 Non-recursive max∗ approximation based on the Jensen inequality

*f*

_{ c }(.) is a correction function, usually implemented with an eight-element look-up table (LUT)[5]. To obtain the max∗ operator for more than two arguments, i.e.,

*n*>2, the Jacobian logarithm (2) is applied recursively

*n*-1 times. For example, assuming

*n*=3, it yields

In the approach taken in[16], in order to reduce the computational effort, the max∗ operator with *n*≥2 arguments is approximated directly, that is, without recursive computations required for the Jacobian logarithm, as it is shown in (3). The motivation for such a treatment comes from the observation that the recursion performed in the Log-MAP to calculate the Jacobian logarithm for *n*>2 arguments has a major influence on the complexity of this algorithm.

*x*

_{1}= max(

*x*

_{1},

*x*

_{2},…,

*x*

_{ n }), we have

Note that the equality in (5) corresponds to the Max- Log-MAP algorithm.

*n*) in (9) is a positive constant value that does not depend on

*x*

_{ i }, it can be omitted and hence, we obtain the final inequality

It can be easily noticed that the RHS in (10) is the average value of input values of the max∗ operator.

*x*

_{ i }by the part that will compute the sum of all

*x*

_{ i }and then divide it by a certain parameter

*N*, suitably selected so as to optimize the performance of the algorithm. Thus, the new approximation that is expected to have a better performance than the Max-Log-MAP algorithm can be formulated as follows[16]:

where *N* is the parameter of the approximation. For a given transmission scheme, an optimal value of *N* minimizing BER performance at the assumed signal-to-noise ratio (SNR) level can be found by means of computer simulations. The Log-MAP algorithm with the approximation of the max∗ operator given in (13) is referred to as the AvN Log-MAP algorithm.

## 3 Performance evaluation results

In this section, we evaluate performance of the AvN Log-MAP decoding algorithm by means of computer simulations for parallel and serial concatenated TTCM schemes. In simulations, the AWGN channel and the uncorrelated, i.e., fully interleaved, Rayleigh fading channel with perfect channel state information (CSI) were considered. Furthermore, the block sizes of *K*=684 and *K*=5,000 symbols with the *S*-random interleavers with the spreading factors *S*=7 and *S*=13, respectively, were assumed. For comparison purposes, the BER performance curves for the conventional Log-MAP (with a LUT storing eight values), Linear Log-MAP, Average Log-MAP, Shift Log-MAP, and Max-Log-MAP algorithms were also evaluated. At the receiver, eight decoding iterations for all algorithms were performed.

### 3.1 Parallel TTCM scheme

*N*of the approximation (13) for parallel turbo TCM schemes with

*K*=684 and

*K*=5,000 are depicted in Figures2 and3, respectively. Figures2 and3 show the BER performance curves versus parameter

*N*for the AvN Log-MAP algorithm in AWGN channel at several values of SNR. As it can be seen in Figures2 and3, for the values of SNR that correspond to the waterfall region of performance curves, the value of

*N*has an influence on the error performance. It can be concluded from these figures that for both block sizes, the optimal value of the parameter

*N*, which minimizes BER is 18.5. It should also be noted that the search for the optimal value of

*N*for serial turbo TCM schemes investigated in section 3.2, gives a very similar result. Thus, in all simulations the AvN Log-MAP algorithm uses

*N*=18.5 in the approximation (13). One may comment at this point that the same optimal value of

*N*has been obtained for TTCM systems, which differ in a structure, overall coding rate, and block size, yet having component codes with an equal number of states (i.e., eight states). This may suggest that the trellis structure of component codes is the key factor that has an influence on the value of the parameter

*N*. Nevertheless, it remains an open problem for further studies.

In terms of hardware or digital signal processor (DSP) implementation of decoding algorithms, it is desirable to avoid multiplications, which are rather complex operations. Therefore, the number of multiplications required by the algorithm can be regarded as one of the crucial factors in determining its complexity. As it will be shown in section 4, the AvN Log-MAP algorithm requires some multiplications. These multiplications are due to the division by parameter *N* in (13). It can be noticed, however, that if we select *N*=2^{
m
},*m*=1,2,…, then the multiplications can be replaced by bit shifts which simplifies the implementation. In order to obtain such a simplified algorithm for parallel TTCM, based on the results from Figures2 and3, we may select *N*=16 although this value of parameter *N* is not optimal but close to the optimal *N*=18.5. Hence, one may expect only a small performance degradation at the gain of further reduction of the algorithm complexity when *N*=16 is selected. In the remainder of this paper, the AvN Log-MAP algorithm with *N*=16 will be denoted as the AvN Log-MAP hardware efficient (HE).

*K*=684 symbols) and large (

*K*=5,000 symbols) interleaver sizes are given in Figures4 and5, respectively. As it can be seen, the AvN Log-MAP algorithm outperforms the Max-Log-MAP by 0.15 dB and is less than 0.2 dB inferior to the Log-MAP at BER = 10

^{-4}. Its loss to other algorithms is in the range of 0.1 to 0.2 dB. The AvNLog-MAP HE algorithm is merely less than 0.1 dB worse than the AvN Log-MAP at the same BER level. It can be noted that both novel algorithms outperform the Max-Log-MAP in the whole range of simulated SNRs. BER performance results of the AvN Log-MAP algorithm for various number of iterations are presented in Figure6. As expected, the error performance improvement with the increasing number of iterations is seen.

### 3.2 Serial TTCM scheme

*h*

_{0}=13,

*h*

_{1}=15, and

*h*

_{2}=17 was applied as an outer encoder. For the inner encoder, we used the same encoder as in the parallel TTCM scheme. Hence, the overall code rate of the serial TTCM scheme is

*R*

_{ c }=1/2.

^{-4}for both block lengths. Comparing to the Max- Log-MAP algorithm, the performance of the AvN Log-MAP is improved by almost 0.5 dB. Similar to the results for a parallel TTCM, the performance loss of the AvN Log-MAP HE algorithm against the AvN Log-MAP is less than 0.1 dB. In the case of transmission over a Rayleigh fading channel, the AvN Log-MAP outperforms the Max-Log-MAP by 0.3 dB and reveals a loss of 0.2 dB to the Log-MAP at BER 10

^{-4}. Insignificant performance degradation of the AvN Log-MAP HE algorithm with respect to the AvN Log-MAP, which uses the optimal value of

*N*can be also noted.

## 4 Complexity comparison of the algorithms

From the implementation point of view, the key aspect of the AvN Log-MAP algorithm is its complexity compared against the Log-MAP and the reduced complexity algorithms previously proposed in the literature. Complexity comparison of the algorithms has been performed for both TTCM schemes from section 3 in software (i.e., computer based) and DSP implementations. DSP evaluation was accomplished on an Ultra Low Power TMS320VC5510 processor that uses a fixed-point number representation[20].

**Complexity comparison of decoding algorithms per decoding step for parallel TTCM scheme in software implementation**

Algorithm | Log-MAP | AvN Log-MAP | AvN Log-MAP HE | Linear Log-MAP | Average Log-MAP | Shift Log-MAP | Max-Log-MAP |
---|---|---|---|---|---|---|---|

Additions | 680 | 512 | 512 | 848 | 680 | 764 | 344 |

Multiplications | 0 | 24 | 0 | 0 | 0 | 0 | 0 |

Comparisons | 357 | 213 | 213 | 357 | 357 | 441 | 189 |

Bit shifts | 168 | 0 | 24 | 168 | 84 | 126 | 0 |

Conversion to integer | 168 | 0 | 0 | 0 | 0 | 0 | 0 |

Assignment | 233 | 197 | 197 | 0 | 233 | 233 | 173 |

Overall | 1,606 | 946 | 946 | 1,606 | 1,354 | 1,564 | 706 |

**Complexity comparison of decoding algorithms per decoding step for serial TTCM scheme in software implementation**

Algorithm | Log-MAP | AvN Log-MAP | AvN Log-MAP HE | Linear Log-MAP | Average Log-MAP | Shift Log-MAP | Max-Log-MAP |
---|---|---|---|---|---|---|---|

Additions | 492 | 432 | 432 | 606 | 492 | 549 | 264 |

Multiplications | 0 | 24 | 0 | 0 | 0 | 0 | 0 |

Comparisons | 249 | 165 | 165 | 249 | 249 | 306 | 141 |

Bit shifts | 114 | 0 | 24 | 114 | 57 | 86 | 0 |

Conversion to integer | 114 | 0 | 0 | 0 | 0 | 0 | 0 |

Assignment | 185 | 173 | 173 | 185 | 185 | 185 | 149 |

Overall | 1,154 | 794 | 794 | 1,154 | 983 | 1,126 | 554 |

According to Table2, the reduction in complexity of the AvN Log-MAP algorithm against the Log-MAP is also significant in serial TTCM scheme and amounts to 31.2%. Comparison to the simple Max-Log-MAP algorithm reveals that the AvN Log-MAP requires 43.3% more operations, but according to the BER results presented in the previous section, it exchanges for a performance improvement of the proposed algorithm by 0.3 to 0.5 dB.

It can also be observed in Tables1 and2 that the AvN Log-MAP HE algorithm performs the same overall number of operations as the AvN Log-MAP but instead of multiplications it realizes bit shifts. This feature of the AvN Log-MAP HE is favorable in hardware implementation of the algorithm and can be considered as an additional complexity reduction.

**Complexity comparison of decoding algorithms per decoding step**

Algorithm | Overall number | Reduction wrt |
---|---|---|

of operations | Log-MAP | |

Log-MAP | 1,606 | — |

AvN Log-MAP | 946 | 41.1% |

Linear Log-MAP | 1,606 | 0% |

Average Log-MAP | 1,354 | 15.7% |

Shift Log-MAP | 1,564 | 2.6% |

Max-Log-MAP | 706 | 56% |

**Complexity comparison of decoding algorithms per decoding step**

Algorithm | Overall number | Reduction wrt |
---|---|---|

of operations | Log-MAP | |

Log-MAP | 1,154 | — |

AvN Log-MAP | 794 | 31.2% |

Linear Log-MAP | 1,154 | 0% |

Average Log-MAP | 983 | 14.8% |

Shift Log-MAP | 1,126 | 2.5% |

Max-Log-MAP | 554 | 52% |

**Complexity comparison of decoding algorithms per decoding step for parallel TTCM scheme in DSP implementation**

Algorithm | Cycles | Reduction wrt Log-MAP |
---|---|---|

Log-MAP | 12,236 | — |

AvN Log-MAP | 6,253 | 48.9% |

Linear Log-MAP | 10,088 | 17.6% |

Average Log-MAP | 10,581 | 13.5% |

Shift Log-MAP | 11,816 | 3.4% |

Max-Log-MAP | 5,790 | 52.7% |

**Complexity comparison of AvN Log-MAP, Log-MAP, and Max-Log-MAP decoding algorithms per decoding step**

Algorithm | Cycles | Reduction wrt Log-MAP |
---|---|---|

Log-MAP | 9,902 | — |

AvN Log-MAP | 5,780 | 41.6% |

Max-Log-MAP | 4,519 | 54.4% |

## 5 Conclusions

A low complexity AvN Log-MAP algorithm has been presented and investigated for turbo TCM schemes. It is based on the approximation of the max∗ operator with *n*≥2 values, derived from the Jensen inequality. As is apparent from the simulation results obtained for AWGN and uncorrelated Rayleigh fading channels, the performance of the proposed algorithm is slightly inferior to that of the Log-MAP but its computational requirements are substantially lower. In particular, we have found that the AvN Log-MAP algorithm performs about 0.1 to 0.4 dB worse than the Log-MAP at BER of 10^{-4}, depending on TTCM scheme and radio channel, while it is also as high as 31.2% to 48.9% much simpler. Performance and complexity comparisons with some relevant reduced complexity decoding algorithms proposed in the literature, i.e., Linear Log-MAP, Average Log-MAP, and Shift Log-MAP, have shown that the AvN Log-MAP algorithm reveals significant decrease in decoding complexity and its computational requirements are much lower than those of these algorithms. The penalty paid for this favorable low complexity of the AvN Log-MAP algorithm is a small performance degradation with respect to those algorithms. Moreover, proper selection of parameter *N* in the approximation will offer further simplification of the algorithm by means of replacing multiplication operations with simple bit shifts. Finally, it should be emphasized that the AvN Log-MAP algorithm is also suitable for implementation in SISO modules for iterative scenarios other than turbo TCM.

## Declarations

## Authors’ Affiliations

## References

- Berrou C, Glavieux A, Thitimajshima P: Near Shannon limit error-correcting coding and decoding. In
*Proc. IEEE International Conference on Communications (ICC)*. Geneva, Switzerland; 1993:1064-1070.View ArticleGoogle Scholar - Berrou C, Glavieux A: Near optimum error-correcting coding and decoding: Turbo-codes.
*IEEE Trans. Commun*1996, 44(10):1261-1271. 10.1109/26.539767View ArticleGoogle Scholar - Bahl LR, Cocke J, Jelinek F, Raviv J: Optimal decoding of linear codes for minimizing symbol error rate.
*IEEE Trans. Inform. Theory*1974, IT-20(2):284-287.MathSciNetView ArticleMATHGoogle Scholar - Robertson P, Villebrun E, Hoeher P: A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain. In
*Proc. IEEE International Conference on Communications (ICC)*. Seattle, USA; 1995:1009-1013.View ArticleGoogle Scholar - Robertson P, Hoeher P, Villebrun E: Optimal and sub-optimal maximum a posteriori algorithms suitable for turbo decoding.
*ETT*1997, 8(2):119-125.Google Scholar - Viterbi AJ: An intuitive justification and a simplified implementation of the MAP decoder for convolutional codes.
*IEEE J. Sel. Areas Commun*1998, 16(2):260-264. 10.1109/49.661114View ArticleGoogle Scholar - Gross WJ, Gulak PG: Simplified MAP algorithm suitable for implementation of turbo decoders.
*Electron. Lett*1998, 34(16):1577-1578. 10.1049/el:19981120View ArticleGoogle Scholar - Cheng JF, Ottosson T: Linearly approximated Log-MAP algorithms for turbo decoding. In
*Proc. 51st IEEE Vehicular Technology Conference (VTC 2000-Spring)*. Tokyo, Japan; 2000:2252-2256.View ArticleGoogle Scholar - Classon B, Blankenship K, Desai V: Channel coding for 4G systems with adaptive modulation and coding.
*IEEE Wireless Commun. Mag*2002, 9(2):8-13.View ArticleGoogle Scholar - Xi-Zhong L, Zhi-Gang M, Yi-Zheng Y, Yan-Min C: A simplification of the Log-MAP algorithm for turbo decoding. In
*Proc. of IEEE Asia-Pacific Conference on Circuits and Systems, Volume 2*. Tainan, Taiwan; 2004:1057-1060.Google Scholar - Wang H, Yang H, Yang D: Improved Log-MAP decoding algorithm for turbo-like codes.
*IEEE Commun. Lett*2006, 10(3):186-188. 10.1109/LCOMM.2006.1603379View ArticleGoogle Scholar - Talakoub S, Sabeti L, Shahrrava B, Ahmadi M: An improved Max-Log-MAP algorithm for turbo decoding and turbo equalization.
*IEEE Trans. Instrum. Meas*2007, 56(3):1058-1063.View ArticleGoogle Scholar - Papaharalabos S, Mathiopoulos PT, Masera G, Martina M: On optimal and near-optimal turbo decoding using generalized max* operator.
*IEEE Commun. Lett*2009, 13(7):522-524.View ArticleGoogle Scholar - Sybis M: Log-MAP equivalent Chebyshev inequality based algorithm for turbo TCM decoding.
*Electron. Lett*2011, 47(18):1049-1050. 10.1049/el.2011.2141View ArticleGoogle Scholar - Papaharalabos S, Sybis M, Tyczka P, Mathiopoulos PT: Novel generalized max* approximation method for simplified decoding of turbo and TTCM codes.
*Wireless Personal Commun*2013, 69: 373-386. 10.1007/s11277-012-0578-6View ArticleGoogle Scholar - Sybis M, Tyczka P, Papaharalabos S, Mathiopoulos PT: Simplified Log-MAP decoding with max* approximation based on the Jensen inequality. In
*Proc. 21st IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2010)*. Istanbul, Turkey; 2010:283-287.Google Scholar - Rudin W:
*Real and Complex Analysis*. New York: McGraw-Hill; 1986.MATHGoogle Scholar - Robertson P, Wörz T: Bandwidth-efficient turbo trellis-coded modulation using punctured component codes.
*IEEE J. Select. Areas Commun*1998, 16(2):206-218. 10.1109/49.661109View ArticleGoogle Scholar - Schlegel CB, Perez LC:
*Trellis and Turbo Coding*. New York: Wiley; 2004.View ArticleGoogle Scholar - Texas Instruments:
*TMS320VC5510/5510A Fixed-point Digital Signal Processors. Data Manual*. Dallas: Texas Instruments; 2007.Google Scholar

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