A simple stopping criterion for turbo decoding

Wu, Yufei; Woerner, B.D.; Ebel, W.J.

doi:10.1109/4234.864187

Cited by 131 publications

(67 citation statements)

References 3 publications

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“…The following contributions further developed the ES philosophy based on the polarity changes of the LLRs [15], [16] during iterative decoding, since the hard decision output only depends on the polarity of the LLRs. The techniques proposed in [15] observed the relative frequency of polarity changes at the ith iteration and introduced the Sign Change Ratio (SCR) as another stopping criterion.…”

Section: A Turbo Code Complexitymentioning

confidence: 99%

A Survey and Tutorial on Low-Complexity Turbo Coding Techniques and a Holistic Hybrid ARQ Design Example

Chen

Maunder

Hanzo

2013

IEEE Commun. Surv. Tutorials

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Abstract-Hybrid Automatic Repeat reQuest (HARQ) has become an essential error control technique in communication networks, which relies on a combination of arbitrary error correction codes and retransmissions. When combining turbo codes with HARQ, the associated complexity becomes a critical issue, since conventionally iterative decoding is immediately activated after each transmission, even though the iterative decoder might fail in delivering an error-free codeword even after a high number of iterations. In this scenario, precious battery-power would be wasted. In order to reduce the associated complexity, we will present design examples based on Multiple Components Turbo Codes (MCTCs) and demonstrate that they are capable of achieving an excellent performance based on the lowest possible memory octally represented generator polynomial (2, 3)o. In addition to using low-complexity generator polynomials, we detail two further techniques conceived for reducing the complexity. Firstly, an Early Stopping (ES) strategy is invoked for curtailing iterative decoding, when its Mutual Information (MI) improvements become less than a given threshold. Secondly, a novel Deferred Iteration (DI) strategy is advocated for the sake of delaying iterative decoding, until the receiver confidently estimates that it has received sufficient information for successful decoding. Our simulation results demonstrate that the MCTC aided HARQ schemes are capable of significantly reducing the complexity of the appropriately selected benchmarkers, which is achieved without degrading the Packet Loss Ratio (PLR) and throughput. codes. Figure 1.1 of [1] outlines the brief history of FEC codes. The ratio of the number of information bits to the total number of information and parity bits defines the normalized throughput or the coding rate. Shannon's channel capacity determines the upper bound of the coding rate that any FEC code may be able to achieve at a certain Signal Noise Ratio (SNR). Since the transmission of these parity bits requires an increased bandwidth, the maximum coding rate that an FEC code can have, while still recovering the information bits becomes a useful criterion for quantifying the capability of FEC codes. Their decoding complexity is also a critical factor in the evaluation of FEC codes. Researchers have studied the associated tradeoff between these two aspects, when choosing a specific FEC code for a communication system. I. TURBO CODING COMPLEXITY A. Turbo Code ComplexityAs one of the most powerful codes in the FEC family, turbo codes [4] have shown capacity-achieving capability by combining two parallel concatenated Recursive Systematic Convolutional (RSC) codes at the transmitter. At the receiver, iterative exchange of soft information is carried out between the two so-called Bahl, Cocke, Jelinek and Raviv (BCJR) decoders [5]. The BCJR decoder is also often referred to as the Maximum A posteriori (MAP) algorithm, which estimates a decoded bit by selecting the specific transition path having the maximum a posteriori pr...

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Section: A Turbo Code Complexitymentioning

confidence: 99%

A Survey and Tutorial on Low-Complexity Turbo Coding Techniques and a Holistic Hybrid ARQ Design Example

Chen

Maunder

Hanzo

2013

IEEE Commun. Surv. Tutorials

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“…Similar to M-SCR, in addition to the condition of SDR in [10]), we add anther condition of stopping in low SNR that…”

Section: Modified-sdr (M-sdr)mentioning

confidence: 99%

“…The other class is based on hard-bit decisions, such as Sign-Change-Ratio (SCR) [9], Hard-Decision-Aided (HDA) [9], Sign-DifferenceRatio (SDR) [10], and Improved-HDA (IHDA) [11]. In addition, some methods are based on extra checking policies, such as Cyclic-Redundancy-Check (CRC) [12] and Valid-Code-Word check (VCW) [13].…”

Section: Introductionmentioning

confidence: 99%

A New Early Termination Scheme of Iterative Turbo Decoding Using Decoding Threshold

Lin

2006

2006 IEEE Workshop on Signal Processing Systems Design and Implementation

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Although many stopping methods of iterative decoding have been discussed in the literatures extensively, many of them only focus on the solvable decoding. In this paper, we propose a new early termination (ET) scheme based on the decoding threshold. Moreover, we proposed the cross-correlation measurement to predict the decoding threshold. The iterative decoding can stop in either high-SNR situation where the decoded bits are highly reliable (solvable decoding), or in low-SNR situation where the decoder already has no capability to decode (unsolvable decoding). At the same time, the receiver can send an automatic-repeat-request (ARQ) message to the transmitter in noise-seriously-coupling situation. Through simulations, the reduced iterations due to the ET scheme almost will not affect the SNR performance. Based on our analysis and simulation results, we can further modify the conventional GENIE chart. The ET property can help to either reduce the computational complexity or power consumptions in implementing the iterative decoding in DSP processors or VLSIs in mobile handsets.

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“…Up to now, many SCs have been proposed to terminate the iteration early to prevent unnecessary iterations of the iterative decoding, such as the cross entropy (CE) criterion [13], the sign-change-ratio (SCR) criterion [14], the sign difference ratio (SDR) criterion [15] and the mean-estimate (ME) criterion [16]. All these criteria were proposed for turbo coded systems and have been proven to be able to reduce the iteration number significantly with little performance loss by comparing a predefined threshold at high SNRs, where successful decodings are usually achieved in the first a few iterations.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Iterative Decoding of Finite-Length Differentially Encoded LDPC Coded Systems with Multiple-Symbol Differential Detection

Handa

Sasamori

et al. 2013

IEICE Trans. Commun.

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SUMMARYIn this paper, through extrinsic information transfer (EXIT) band chart analysis, an adaptive iterative decoding approach (AIDA) is proposed to reduce the iterative decoding complexity and delay for finite-length differentially encoded Low-density parity-check (DE-LDPC) coded systems with multiple-symbol differential detection (MSDD). The proposed AIDA can adaptively adjust the observation window size (OWS) of the MSDD soft-input soft-output demodulator (SISOD) and the outer iteration number of the iterative decoder (consisting of the MSDD SISOD and the LDPC decoder) instead of setting fixed values for the two parameters of the considered systems. The performance of AIDA depends on its stopping criterion (SC) which is used to terminate the iterative decoding before reaching the maximum outer iteration number. Many SCs have been proposed; however, these approaches focus on turbo coded systems, and it has been proven that they do not well suit for LDPC coded systems. To solve this problem, a new SC called differential mutual information (DMI) criterion, which can track the convergence status of the iterative decoding, is proposed; it is based on tracking the difference of the output mutual information of the LDPC decoder between two consecutive outer iterations of the considered systems. AIDA using the DMI criterion can adaptively adjust the out iteration number and OWS according to the convergence situation of the iterative decoding. Simulation results show that compared with using the existing SCs, AIDA using the DMI criterion can further reduce the decoding complexity and delay, and its performance is not affected by a change in the LDPC code and transmission channel parameters. key words: adaptive iterative decoding, low-density parity-check (LDPC) codes, differential encoded LDPC coded systems, multiple-symbol differential detection, extrinsic information transfer (EXIT) band chart

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A simple stopping criterion for turbo decoding

Cited by 131 publications

References 3 publications

A Survey and Tutorial on Low-Complexity Turbo Coding Techniques and a Holistic Hybrid ARQ Design Example

A Survey and Tutorial on Low-Complexity Turbo Coding Techniques and a Holistic Hybrid ARQ Design Example

A New Early Termination Scheme of Iterative Turbo Decoding Using Decoding Threshold

Adaptive Iterative Decoding of Finite-Length Differentially Encoded LDPC Coded Systems with Multiple-Symbol Differential Detection

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