Turbo decoder

We propose an adaptive channel SNR estimation algorithm required for the iterative MAP decoding of turbo decoders. The proposed algorithm uses the extrinsic values generated within the iterative MAP decoder to update the channel SNR estimate toward its optimum value per each decoder iteration or per each turbo code frame     

Performance of a turbo‐coded CDMA system in a mobile satellite channel

The bit error rate (BER) performance of a turbo‐coded code‐division multiple‐access (CDMA) system operating in a satellite channel is analysed and simulated. The system performance is compared for various constituent decoders, including maximum a posteriori probability (MAP) and Max‐Log‐MAP algorithms, and the soft‐output Viterbi algorithm. The simulation results indicate that the Max‐Log‐MAP algorithm is the most promising among these three algorithms in overall terms of performance and complexity. It is also shown that, for fixed code rate, the BER performance is improved substantially by increasing the number of iterations in the turbo decoder, or by increasing the interleaver length in the turbo encoder. The results in this paper are of interest in CDMA‐based satellite communications applications. Copyright © 2005 John Wiley & Sons, Ltd.     

Optimal soft decoding for combined trellis-codedquantization/modulation

The maximum a posteriori (MAP) algorithm is used as a minimum mean-squared-error decoder for combined trellis-coded quantization and modulation. The optimal trellis-coded quantizer is derived when the soft-decoding MAP algorithm is used as a decoder. The trellis-coded quantizer and the soft decoder are optimized iteratively for minimum overall distortion from transmitter input to receiver output. Significant performance improvement is achieved for both memoryless Gaussian and uniform source, especially for very noisy channels     

Near-optimum decoding of product codes: block turbo codes

This paper describes an iterative decoding algorithm for any product code built using linear block codes. It is based on soft-input/soft-output decoders for decoding the component codes so that near-optimum performance is obtained at each iteration. This soft-input/soft-output decoder is a Chase decoder which delivers soft outputs instead of binary decisions. The soft output of the decoder is an estimation of the log-likelihood ratio (LLR) of the binary decisions given by the Chase decoder. The theoretical justifications of this algorithm are developed and the method used for computing the soft output is fully described. The iterative decoding of product codes is also known as the block turbo code (BTC) because the concept is quite similar to turbo codes based on iterative decoding of concatenated recursive convolutional codes. The performance of different Bose-Chaudhuri-Hocquenghem (BCH)-BTCs are given for the Gaussian and the Rayleigh channel. Performance on the Gaussian channel indicates that data transmission at 0.8 dB of Shannon's limit or more than 98% (R/C>0.98) of channel capacity can be achieved with high-code-rate BTC using only four iterations. For the Rayleigh channel, the slope of the bit-error rate (BER) curve is as steep as for the Gaussian channel without using channel state information     

An iterative receiver for turbo-coded pilot-assisted modulation infading channels

We introduce an iterative joint channel and data estimation receiver that exploits both the power of pilot-symbol assisted modulation (PSAM) and turbo coding for fading channels. The key innovation is a low-complexity soft channel estimator which divides a processing block into overlapped cells and performs maximum a posteriori (MAP) sequence estimation and MMSE filtering based on the received signal and extrinsic information delivered by the soft channel decoder. Simulation results show that for turbo-coded PSAM systems under time-variant fading the proposed receiver offers significant performance gains over a non-iterative receiver and two other cancellation schemes     

COVQ for MAP hard-decision demodulated channels

We design a channel optimized vector quantizer (COVQ) for symbol-by-symbol maximum a posteriori (MAP) hard-decision demodulated channels. The main objective is to exploit the non-uniformity of the indices representing the quantized source via the MAP decoder and iteratively optimize the overall discrete channel (at the symbol level) jointly with the quantizer. We consider memoryless Gaussian and Gauss-Markov sources transmitted over a binary phase-shift keying modulated Rayleigh fading channel. Our scheme has less encoding computational and storage complexity (particularly for noisy channel conditions) than both conventional and soft-decision COVQ systems, which use hard-decision and soft-decision maximum likelihood demodulation, respectively. Furthermore, it provides a notable signal-to-distortion ratio gain over the former system, and in some cases it matches or outperforms the latter one.     

The max-log list algorithm (MLLA)-a list-sequence decoding algorithm that provides soft-symbol output

We present a soft decoding algorithm for convolutional codes that simultaneously yields soft-sequence output, i.e., list sequence (LS) decoding, and soft-symbol output. The max-log list algorithm (MLLA) introduced in this paper provides near-optimum soft-symbol output equal to that of the max-log maximum a posteriori (MAP) probability algorithm. Simultaneously, the algorithm produces an ordered list containing LS-MAP estimates. The MLLA exists in an optimum and a suboptimum version that are different in that the optimum version produces optimum LS-MAP decoding for arbitrary list lengths, while the suboptimum low-complexity version only provides the MAP, the second-order MAP, and the third-order MAP sequence estimates. For lists with more than three elements, MAP decoding is not guaranteed, but the LS decoding is close to the optimal. It is demonstrated that the suboptimum/optimum MLLA can be used to obtain the combination of soft-symbol and soft-sequence outputs at lower complexity than a previously published algorithm. Furthermore, the suboptimum MLLA is well suited for operation in an iterative list (turbo) decoder, since it is obtained by only minor modifications of the well-known Max-Log-MAP algorithm frequently used for decoding of the component codes of turbo codes. Another potential area of application for the suboptimum/optimum MLLA is joint source-channel LS decoding. Estimates of complexity and memory use, as well as performance evaluations of the suboptimum/optimum MLLA, are provided in this paper.     

Performance of Quantized Turbo Decoding for Decoders with Maximum a Postriori Algorithm

This paper investigates Bit Error Performance for quantized turbo code over Gaussian channels. The turbo encoder is built using a parallel concatenation of two recursive systematic convolution encoders and a random interleaver. The turbo decoder employs' two iterative symbol by symbol MAP based algorithm decoders for decoding the received data. For a given quantization level, the Bit Error Rates are examined by considering the influence of the code structure, the block size, and the number of decoding steps. Finally, to measure loss of power efficiency due to quantization, the results are compared to previously published results for the unquantized case under the same Bit to noise ratio and the code structure. Omar M. Hasan Was born in Amman, Jordan, in 1964. He received the MS and the Ph.D degrees in Communications Engineering from New Mexico State University, Las Cruces, New Mexico, USA, in 1990 and 1996. Currently, he is the chairman of the Communications Engineering department at Princess Sumaya University in Amman, Jordan. During 1991–1992, he works at the RF and the digital system divisions at the Physical Science Laboratory, Las Cruces, New Mexico. During 1996, he worked as a researcher at the Manuel Lujan, Jr. Center for Space Telemetering and Telecommunications. Las Cruces, New Mexico, USA. During the year 2000, he was the head of research and development group at Cardiac Teleccommunications, Webster, TX, USA. His main research interests include turbo coding, narrow-band FSK systems, telemedicince, and mobile aided learning.     

Memory-Reduced Maximum A Posteriori Probability Decoding for High-Throughput Parallel Turbo Decoders

Wireless communication standards make use of parallel turbo decoder for higher data rate at the cost of large hardware resources. This paper presents a memory-reduced back-trace technique, which is based on a new method of estimating backward-recursion factors, for the maximum a posteriori probability (MAP) decoding. Mathematical reformulations of branch-metric equations are performed to reduce the memory requirement of branch metrics for each trellis stage. Subsequently, an architecture of MAP decoder and its scheduling based on the proposed back trace as well as branch-metric reformulation are presented in this work. Comparative analysis of bit-error-rate (BER) performances in additive white Gaussian noise channel environment for MAP as well as parallel turbo decoders are carried out. It has shown that a MAP decoder with a code rate of 1/2 and a parallel turbo decoder with a code rate of 1/3 have achieved coding gains of 1.28 dB at a BER of 10\(^{-5}\) and of 0.4 dB at a BER of 10\(^{-4}\), respectively. In order to meet high-data-rate benchmarks of recently deployed wireless communication standards, very large scale integration implementations of parallel turbo decoder with 8–64 MAP decoders have been reported. Thereby, savings of hardware resources by such parallel turbo decoders based on the suggested memory-reduced techniques are accounted in terms of complementary metal oxide semiconductor transistor count. It has shown that the parallel turbo decoder with 32 and 64 MAP decoders has shown hardware savings of 34 and 44 % respectively.     

Hadamard-based soft decoding for vector quantization over noisychannels

We present an estimator-based, or soft, vector quantizer decoder for communication over a noisy channel. The decoder is optimal according to the mean-square error criterion, and Hadamard-based in the sense that a Hadamard transform representation of the vector quantizer is utilized in the implementation of the decoder. An efficient algorithm for optimal decoding is derived. We furthermore investigate suboptimal versions of the decoder, providing good performance at lower complexity. The issue of joint encoder-decoder design is considered both for optimal and suboptimal decoding. Results regarding the channel distortion and the structure of a channel robust code are also provided. Through numerical simulations, soft decoding is demonstrated to outperform hard decoding in several aspects     

New error bounds and optimum quantization for multisensordistributed signal detection

The binary signal detection problem is considered, when a distributed system of sensors operates in a decentralized fashion. Local processing at each sensor is performed. Using Chernoff's large deviation theorems, the author considers as a criterion the rate of convergence of the error probability to zero. It is shown that the optimum quantizer of blocks of data under the above criterion is the likelihood ratio quantizer. A lower bound to the error probability is also developed. The question of how many coarsely quantized sensors can replace the infinitely quantized one is also answered. The main result given is the structure of the optimum quantizer, consisting of the calculation of the likelihood ratio concatenated by a scalar quantizer     

Quantization in multisensor random signal detection

Optimum detection schemes based on quantized data are of great interest in radar and sonar applications. The design and properties of multisensor schemes are considered here for detection of weak random signals in additive, possibly non-Gaussian, noise. Signal-to-noise ratios are assumed unknown and the signals at the different sensors may be statistically dependent. Analytical expressions describing the best way to fuse the quantized observations for cases with any given observation sample size are provided. The best schemes for originally quantizing the observations are also studied for the case of asymptotically large observation sample sizes. These schemes are shown to minimize the mean-squared error between the best weak-signal test statistic based on unquantized observations and the best weak-signal test statistic based on quantized observations (under signal absent). Numerical results indicate it is sometimes best for each quantizer to use different size alphabets when a quantizer is located at each sensor     

用于Turbo迭代译码的近似Log-MAP算法研究

本文运用逼近论,研究Log-MAP算法的近似算法.本文提出了校正函数的一阶、二阶和三阶逼近多项式,并对近似式的逼近精度进行了分析和比较.本文将近似Log-MAP算法用于WCDMA turbo译码器中,对译码器在AWGN信道和平坦慢衰落信道上的纠错性能进行了仿真.仿真结果表明：一阶近似Log-MAP算法将Max-Log-MAP turbo译码器的纠错性能改进了0.2~0.3dB,二阶及三阶近似Log-MAP算法与原Log-MAP算法性能等价,优于Max-Log-MAP 算法0.3~0.5dB.     

Complexity Reduction in SISO Decoding of Block Codes

SISO decoding for block codes can be carried out based on a trellis representation of the code. However, the complexity entailed by such decoding is most often prohibitive and thus prevents practical implementation. This paper examines a new decoding scheme based on the soft-output Viterbi algorithm (SOVA) applied to a sectionalized trellis for linear block codes. The computational complexities of the new SOVA decoder and of the conventional SOVA decoder, based on a bit-level trellis, are theoretically analyzed and derived for different linear block codes. These results are used to obtain optimum sectionalizations of a trellis for SOVA. For comparisons, the optimum sectionalizations for Maximum A Posteriori (MAP) and Maximum Logarithm MAP (Max-Log-MAP) algorithms, and their corresponding computational complexities are included. The results confirm that the new SOVA decoder is the most computationally efficient SISO decoder, in comparisons to MAP and Max-Log-MAP algorithms. The simulation results of the bit error rate (BER) performance, assuming binary phase -- shift keying (BPSK) and additive white Gaussian noise (AWGN) channel, demonstrate that the performance of the new decoding scheme is not degraded. The BER performance of iterative SOVA decoding of serially concatenated block codes shows no difference in the quality of the soft outputs of the new decoding scheme and of the conventional SOVA.     

Turbo decoder SNR estimation with RAKE reception

The joint performance of a turbo decoder and RAKE receiver using the MAP algorithm depends on the accuracy of the channel reliability factor. In a high data rate/low processing gain environment, inherent interference that results from non-idealities of the RAKE receiver complicate the estimation of the channel reliability factor. The combined performance of a turbo decoder and RAKE receiver is analyzed in a timedispersive and time-varying channel with distinct multipath components. Approaches are examined for estimating the channel reliability factor using the limited information that is known by the RAKE receiver. The sensitivity of performance to SNR mismatches is computed. The impact of the processing gain and the number of multipath components on BER performance is analyzed along with the effect of the channel time coherence. By accounting for the non-ideal RAKE interference effects, improvements in the channel reliability factor calculation result in BER performance improvements on the order of 0.5-2 dB.     

Layered MAP algorithm for MIMO ISI channels

The layered maximum a posteriori （L-MAP） algorithm has been proposed to detect signals under frequency selective fading multiple input multiple output （MIMO） channels. Compared to the optimum MAP detector, the L-MAP algorithm can efficiently identify signal bits, and the complexity grows linearly with the number of input antennas. The basic idea of L-MAP is to operate on each input sub-stream with an optimum MAP sequential detector separately by assuming the other streams are Gaussian noise. The soft output can also be forwarded to outer channel decoder for iterative decoding. Simulation results show that the proposed method can converge with a small number of iterations under different channel conditions and outperforms other sub-optimum detectors for rank-deficient channels.     

Turbo equalization of serially concatenated turbo codes using a predictive DFE-based receiver

This paper investigates the performance of various “turbo” receivers for serially concatenated turbo codes transmitted through intersymbol interference (ISI) channels. Both the inner and outer codes are assumed to be recursive systematic convolutional (RSC) codes. The optimum turbo receiver consists of an (inner) channel maximum a posteriori (MAP) decoder and a MAP decoder for the outer code. The channel MAP decoder operates on a “supertrellis” which incorporates the channel trellis and the trellis for the inner error-correcting code. This is referred to as the MAP receiver employing a SuperTrellis (STMAP). Since the complexity of the supertrellis in the STMAP receiver increases exponentially with the channel length, we propose a simpler but suboptimal receiver that employs the predictive decision feedback equalizer (PDFE). The key idea in this paper is to have the feedforward part of the PDFE outside the iterative loop and incorporate only the feedback part inside the loop. We refer to this receiver as the PDFE-STMAP. The complexity of the supertrellis in the PDFE-STMAP receiver depends on the inner code and the length of the feedback part. Investigations with Proakis B, Proakis C (both channels have spectral nulls with all zeros on the unit circle and hence cannot be converted to a minimum phase channel) and a minimum phase channel reveal that at most two feedback taps are sufficient to get the best performance. A reduced-state STMAP (RS-STMAP) receiver is also derived which employs a smaller supertrellis at the cost of performance.     

Low complexity turbo equalization based on soft feedback interference cancellation

This letter presents a new soft feedback interference cancellation (SFIC) based equalizer suitable for iterative receivers applying turbo equalization. SFIC offers a very low computational complexity depending only linearly on the channel memory length. Despite its low complexity, SFIC shows a very good BER performance. Simulation results for the severely intersymbol interference distorted Proakis C channel show, that our approach performs within 0.5 dB to the powerful turbo equalization scheme based on MMSE linear filtering with time-varying coefficients and fails the mathematical optimum maximum a-posteriori (MAP) equalizer only by 1.2 dB.     

Turbo codes for image transmission-a joint channel and sourcedecoding approach

This paper studies an application of turbo codes to compressed image/video transmission and presents an approach to improving error control performance through joint channel and source decoding (JCSD). The proposed approach to JCSD includes error-free source information feedback, error-detected source information feedback, and the use of channel soft values (CSV) for source signal postprocessing. These feedback schemes are based on a modification of the extrinsic information passed between the constituent maximum a posteriori probability (MAP) decoders in a turbo decoder. The modification is made according to the source information obtained from the source signal processor. The CSVs are considered as reliability information on the hard decisions and are further used for error recovery in the reconstructed signals. Applications of this joint decoding technique to different visual source coding schemes, such as spatial vector quantization, JPEG coding, and MPEG coding, are examined. Experimental results show that up to 0.6 dB of channel SNR reduction can be achieved by the joint decoder without increasing computational cost for various channel coding rates     

Comparative study of turbo decoding techniques: an overview

We provide an overview of the novel class of channel codes referred to as turbo codes, which have been shown to be capable of performing close to the Shannon limit. We commence with a discussion on turbo encoding, and then move on to describing the form of the iterative decoder most commonly used to decode turbo codes. We then elaborate on various decoding algorithms that can be used in an iterative decoder, and give an example of the operation of such a decoder using the so-called soft output Viterbi (1996) algorithm (SOVA). Lastly, the effect of a range of system parameters is investigated in a systematic fashion, in order to gauge their performance ramifications