LDPC-based iterative joint source-channel decoding for JPEG2000.

A framework is proposed for iterative joint source-channel decoding of JPEG2000 codestreams. At the encoder, JPEG2000 is used to perform source coding with certain error-resilience (ER) modes, and LDPC codes are used to perform channel coding. During decoding, the source decoder uses the ER modes to identify corrupt sections of the codestream and provides this information to the channel decoder. Decoding is carried out jointly in an iterative fashion. Experimental results indicate that the proposed method requires fewer iterations and improves overall system performance.     

Low-complexity iterative joint source-channel decoding for variable-length encoded Markov sources

In this paper, we present a novel packetized bit-level decoding algorithm for variable-length encoded Markov sources, which calculates reliability information for the decoded bits in the form of a posteriori probabilities (APPs). An interesting feature of the proposed approach is that symbol-based source statistics in the form of the transition probabilities of the Markov source are exploited as a priori information on a bit-level trellis. This method is especially well-suited for long input blocks, since in contrast to other symbol-based APP decoding approaches, the number of trellis states does not depend on the packet length. When additionally the variable-length encoded source data is protected by channel codes, an iterative source-channel decoding scheme can be obtained in the same way as for serially concatenated codes. Furthermore, based on an analysis of the iterative decoder via extrinsic information transfer charts, it can be shown that by using reversible variable-length codes with a free distance of two, in combination with rate-1 channel codes and residual source redundancy, a reliable transmission is possible even for highly corrupted channels. This justifies a new source-channel encoding technique where explicit redundancy for error protection is only added in the source encoder.     

Turbo decodulation: iterative combined demodulation and source-channel decoding

We propose the combination of iterative demodulation and iterative source-channel decoding as a multiple turbo process. The receiver structures of bit-interleaved coded modulation with iterative decoding (BICM-ID), iterative source-channel decoding (ISCD), and iterative source coded modulation (ISCM) are merged to one novel turbo system, in which in two iterative loops reliability information is exchanged between the three single components, demodulator, channel decoder and (softbit) source decoder. Simulations show quality improvements compared to the different previously known systems, which use iterative processing only for two components of the receiver.     

Joint source-channel coding using combined TCQ/CPM: iterative decoding

An iterative decoding approach to joint source and channel coding (JSCC) using combined trellis-coded quantization (TCQ) and continuous phase modulation (CPM) is proposed. The channel is assumed to be the additive white Gaussian noise channel. This iterative procedure exploits the structure of the TCQ encoder and the continuous phase modulator. The performance in terms of the signal-to-distortion ratio (SDR) is compared with that of a combined TCQ/trellis-coded modulation (TCM) system. It is shown that the combined TCQ/CPM systems are both power- and bandwidth-efficient, compared with the combined TCQ/TCM system. For source encoding rate R=2 b/sample, it is observed that the combined TCQ/CPM systems with iterative decoding working at symbol level converge faster than the systems working at bit level. The novelty of this work is the use of a soft decoder and an iterative decoding algorithm for TCQ-based JSCC systems. The combined TCQ/CPM with iterative decoding is considered for the first time.     

A generalized framework for hybrid simulation of multi-component structures using iterative field refinement

When applying computational simulation techniques to scattering or radiation problems, it is often possible to decompose a complicated geometry into simpler elemental structures (i.e., a helicopter rotor system into its individual blades). By then simulating each component separately, a given problem can be decomposed into smaller and more manageable components, as long as account is taken of the coupling between each component. To implement such coupling, this paper describes a generalized iterative field refinement (IFR) framework, and demonstrates how it can be used as a basis for many hybrid approaches. Within this framework, IFR can also be used to accelerate simulation of geometries made up of rotated, translated, reflected, or replicated versions of a given structure. Several examples are given to show that an approach built around IFR reduces total computation time while allowing the combination of different analysis methods in treating each of the separate components comprising the structure.     

Joint source-channel and multiuser decoding for Rayleigh fadingCDMA channels

We consider joint source-channel and multiuser decoding for frequency selective Rayleigh fading code-division multiple-access channels. The block source-channel encoder is defined by a vector quantizer. We investigate optimal (minimum mean-square error) decoding and “user-separated” decoding of lower complexity. The studied decoders are soft in the sense that they utilize all soft information available at the receiver. Simulations indicate significant performance gains of the introduced decoders compared with a tandem approach that uses maximum-likelihood multiuser detection plus table-lookup decoding     

Joint source-channel turbo decoding of entropy-coded sources

We analyze the dependencies between the variables involved in the source and channel coding chain. This analysis is carried out in the framework of Bayesian networks, which provide both an intuitive representation for the global model of the coding chain and a way of deriving joint (soft) decoding algorithms. Three sources of dependencies are involved in the chain: (1) the source model, a Markov chain of symbols; (2) the source coder model, based on a variable length code (VLC), for example a Huffman code; and (3) the channel coder, based on a convolutional error correcting code. Joint decoding relying on the hidden Markov model (HMM) of the global coding chain is intractable, except in trivial cases. We advocate instead an iterative procedure inspired from serial turbo codes, in which the three models of the coding chain are used alternately. This idea of using separately each factor of a big product model inside an iterative procedure usually requires the presence of an interleaver between successive components. We show that only one interleaver is necessary here, placed between the source coder and the channel coder. The decoding scheme we propose can be viewed as a turbo algorithm using alternately the intersymbol correlation due to the Markov source and the redundancy introduced by the channel code. The intermediary element, the source coder model, is used as a translator of soft information from the bit clock to the symbol clock     

Shuffled iterative decoding

Shuffled versions of iterative decoding of low-density parity-check codes and turbo codes are presented. The proposed schemes have about the same computational complexity as the standard versions, and converge faster. Simulations show that the new schedules offer better performance/complexity tradeoffs, especially when the maximum number of iterations has to remain small.     

Analysis and optimization of irregular LDPC codes for joint source-channel decoding

In this paper, we present a characterization, through its convergence analysis, and an optimisation of a joint source-channel receiver composed of a LDPC decoder and a Soft Input Soft Output (SISO) source decoder. Under Gaussian approximation, assuming the knowledge of the extrinsic mutual information transfer function (EXIT chart) of the source decoder, we derive the Mutual Information evolution equations, that semianalytically describe the convergence of the iterative system behavior and, to complete the study, the stability condition at the convergence fixed point is derived for the joint receiver. From this analysis, a general optimisation method of the irregularity of the LDPC codes is proposed, which can be reduced to a linear programming optimisation problem. Simulation results show improved performance when compared to an AWGN optimized LDPC code.     

A soft-input soft-output APP module for iterative decoding ofconcatenated codes

Concatenated coding schemes consist of the combination of two or more simple constituent encoders and interleavers. The parallel concatenation known as “turbo code” has been shown to yield remarkable coding gains close to theoretical limits, yet admitting a relatively simple iterative decoding technique. The recently proposed serial concatenation of interleaved codes may offer superior performance to that of turbo codes. In both coding schemes, the core of the iterative decoding structure is a soft-input soft-output (SISO) a posteriori probability (APP) module. In this letter, we describe the SISO APP module that updates the APP's corresponding to the input and the output bits, of a code, and show how to embed it into an iterative decoder for a new hybrid concatenation of three codes, to fully exploit the benefits of the proposed SISO APP module     

一种新的Turbo码译码的迭代停止判决

Turbo码译码由于是迭代译码,因此带来了译码延时大的问题,运用动态的迭代译码算法为了来解决这个延时大的问题,首先对CRC,HDA,SCR,SDR和IHDA这五种动态停止迭代判决进行了讨论,并且对它们进行了分析和比较。其次在基于文献[1]提出来的迭代停止判决的思想上,即就是根据信道条件好坏的情况下,提出了CRC-SDR迭代停止判决。最后对所提出的CRC-SDR停止迭代判决进行了仿真并且和其他译码迭代停止判决的仿真结果相比,可知CRC-SDR的译码平均迭代次数明显降低,而且译码的性能并没有下降,因此可知提出来的CRC-SDR改进算法是可行的和有效的。     

乘积码基于相关运算的迭代译码

乘积码是一种能以Turbo码的思想实现译码的级联码，具有一般编码无法达到的纠错能力。本文提出一种新的乘积码迭代译码算法，其核心思想是通过输出软信息与接收软信息进行线性迭加的方式来实现反馈，此时只须提供-1和1组成的软输出矩阵就能获得很高的编码增益，仿真表明，将子译码器译码后的结果再进行一次相关运算作为软输出，译码性能可以得到进一步的提高。     

Multiuser demodulation and iterative decoding for frequency-hoppednetworks

Demodulation and decoding for frequency-hopped spread-spectrum multiple-access (FH/SSMA) systems have been traditionally conducted by conventional single-user (noncollaborative) demodulation and error- and erasure-correcting decoding techniques. In this paper, we study the demodulation and decoding aspects of collaborative multiuser reception for FH/SSMA and propose methods which increase the number of users the system can support. In particular, we propose and analyze the optimum maximum a priori probability demodulation of multiple symbols or type, and the use of iterative multiuser decoding after the demodulation. Since hits from one or two other users are the most likely hit events in FH/SSMA, the joint demodulation of two or of three users is performed based on likelihood ratio tests. M-ary frequency-shift keying modulation with noncoherent demodulation and Reed-Solomon codes with hard-decision minimum distance decoding are used in the FH/SSMA system. Results are derived for both synchronous and asynchronous frequency-hop systems. The performance of the proposed multiuser detector in additive white Gaussian noise and flat Rayleigh fading channels is evaluated. Scenarios when all simultaneous users or only a subset of them are collaboratively demodulated and decoded are simulated     

Graph-based iterative decoding algorithms for parity-concatenatedtrellis codes

We construct parity-concatenated trellis codes in which a trellis code is used as the inner code and a simple parity-check code is used as the outer code. From the Tanner-Wiberg-Loeliger (1981, 1996) graph representation, several iterative decoding algorithms can be derived. However, since the graph of the parity-concatenated code contains many short cycles, the conventional min-sum and sum-product algorithms cannot achieve near-optimal decoding. After some simple modifications, we obtain near-optimal iterative decoders. The modifications include either (a) introducing a normalization operation in the min-sum and sum-product algorithms or (b) cutting the short cycles which arise in the iterative Viterbi algorithm (IVA). After modification, all three algorithms can achieve near-optimal performance, but the IVA has the least average complexity. We also show that asymptotically maximum-likelihood (ML) decoding and a posteriori probability (APP) decoding can be achieved using iterative decoders with only two iterations. Unfortunately, this asymptotic behavior is only exhibited when the bit-energy-to-noise ratio is above the cutoff rate. Simulation results show that with trellis shaping, iterative decoding can perform within 1.2 dB of the Shannon limit at a bit error rate (BER) of 4×10^-5 for a block size of 20000 symbols. For a block size of 200 symbols, iterative decoding can perform within 2.1 dB of the Shannon limit     

Codes for iterative decoding from partial geometries

This paper develops codes suitable for iterative decoding using the sum-product algorithm. By considering a large class of combinatorial structures, known as partial geometries, we are able to define classes of low-density parity-check (LDPC) codes, which include several previously known families of codes as special cases. The existing range of algebraic LDPC codes is limited, so the new families of codes obtained by generalizing to partial geometries significantly increase the range of choice of available code lengths and rates. We derive bounds on minimum distance, rank, and girth for all the codes from partial geometries, and present constructions and performance results for the classes of partial geometries which have not previously been proposed for use with iterative decoding. We show that these new codes can achieve improved error-correction performance over randomly constructed LDPC codes and, in some cases, achieve this with a significant decrease in decoding complexity.     

Joint source-channel decoding of variable-length codes for convolutional codes and turbo codes

Several recent publications have shown that joint source-channel decoding could be a powerful technique to take advantage of residual source redundancy for fixed- and variable-length source codes. This letter gives an in-depth analysis of a low-complexity method recently proposed by Guivarch et al., where the redundancy left by a Huffman encoder is used at a bit level in the channel decoder to improve its performance. Several simulation results are presented, showing for two first-order Markov sources of different sizes that using a priori knowledge of the source statistics yields a significant improvement, either with a Viterbi channel decoder or with a turbo decoder.     

Joint source-channel coding and guessing with application tosequential decoding

We extend our earlier work on guessing subject to distortion to the joint source-channel coding context. We consider a system in which there is a source connected to a destination via a channel and the goal is to reconstruct the source output at the destination within a prescribed distortion level with respect to (w.r.t.) some distortion measure. The decoder is a guessing decoder in the sense that it is allowed to generate successive estimates of the source output until the distortion criterion is met. The problem is to design the encoder and the decoder so as to minimize the average number of estimates until successful reconstruction. We derive estimates on nonnegative moments of the number of guesses, which are asymptotically tight as the length of the source block goes to infinity. Using the close relationship between guessing and sequential decoding, we give a tight lower bound to the complexity of sequential decoding in joint source-channel coding systems, complementing earlier works by Koshelev (1973) and Hellman (1975). Another topic explored here is the probability of error for list decoders with exponential list sizes for joint source-channel coding systems, for which we obtain tight bounds as well. It is noteworthy that optimal performance w.r.t. the performance measures considered here can be achieved in a manner that separates source coding and channel coding     

Early stopping for the iterative decoding for Q-LDPC

Q-ary low-density parity-check （Q-LDPC） codes have a better performance than those of the binary low-density parity-check （B-LDPC） codes, at short and medium block lengths, but the decoder of Q-LDPC has more complexity. In this article, a new stop criterion is proposed. By analyzing the changes of the maximum posteriori probability of the variable node, the criterion decides whether the iteration of the decoder must be stopped. The simulation results show that the stop criterion can effectively reduce the computation complexity of the Q-LDPC decoder with negligible performance loss.     

A modified medium access control algorithm for systems with iterative decoding

Efficient transmission methods for fading radio channels often require an iterative decoder. This is for example the case for systems using turbo codes. Receiver decoder iterations could potentially lead to a latency problem which impacts the performance of the medium access control protocol. In this paper, we present modifications based on the carrier sense multiple access with collision avoidance (CSMA/CA) medium access control (MAC) protocol to accommodate the increased latency in the iterative processing. One area of applications is wireless local area networks (WLANs) with high data rate. The simulation results performed in the IEEE 802.11a WLAN environment by replacing the 802:11a's convolutional coding with turbo coding demonstrate that the proposed algorithm provides a throughput gain over the conventional method.     

Multiuser SCCPM with iterative decoding

The recent technique of serially concatenated continuous phase modulation (SCCPM) is extended to a multiuser system with a carrier frequency offset between the users. Simulation results for up to five users with an iterative decoder show that the performance loss is small considering the substantial spectral overlap. Consequently, power/bandwidth efficiencies which are better than for SCCPM are achievable