Iterative Decoding Scheme Research Articles

HIDE: Hierarchical iterative decoding enhancement for multi‐view 3D human parameter regression

AbstractParametric human modeling are limited to either single‐view frameworks or simple multi‐view frameworks, failing to fully leverage the advantages of easily trainable single‐view networks and the occlusion‐resistant capabilities of multi‐view images. The prevalent presence of object occlusion and self‐occlusion in real‐world scenarios leads to issues of robustness and accuracy in predicting human body parameters. Additionally, many methods overlook the spatial connectivity of human joints in the global estimation of model pose parameters, resulting in cumulative errors in continuous joint parameters.To address these challenges, we propose a flexible and efficient iterative decoding strategy. By extending from single‐view images to multi‐view video inputs, we achieve local‐to‐global optimization. We utilize attention mechanisms to capture the rotational dependencies between any node in the human body and all its ancestor nodes, thereby enhancing pose decoding capability. We employ a parameter‐level iterative fusion of multi‐view image data to achieve flexible integration of global pose information, rapidly obtaining appropriate projection features from different viewpoints, ultimately resulting in precise parameter estimation. Through experiments, we validate the effectiveness of the HIDE method on the Human3.6M and 3DPW datasets, demonstrating significantly improved visualization results compared to previous methods.

On Grounded Planning for Embodied Tasks with Language Models

Language models (LMs) have demonstrated their capability in possessing commonsense knowledge of the physical world, a crucial aspect of performing tasks in everyday life. However, it remains unclear whether they have the capacity to generate grounded, executable plans for embodied tasks. This is a challenging task as LMs lack the ability to perceive the environment through vision and feedback from the physical environment. In this paper, we address this important research question and present the first investigation into the topic. Our novel problem formulation, named G-PlanET, inputs a high-level goal and a data table about objects in a specific environment, and then outputs a step-by-step actionable plan for a robotic agent to follow. To facilitate the study, we establish an evaluation protocol and design a dedicated metric, KAS, to assess the quality of the plans. Our experiments demonstrate that the use of tables for encoding the environment and an iterative decoding strategy can significantly enhance the LMs' ability in grounded planning. Our analysis also reveals interesting and non-trivial findings.

Symbol-level iterative information set decoding of RS codes

This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by introducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol-level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given (n,k) Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when compared to other symbol-level iterative decoders while working at a relatively lower complexity.

Iterative EP Detection and Decoding of Polar-Coded MIMO Systems

Channel coding and multiple-input multiple-output (MIMO) antennas are two key technologies in the modern communication system. For MIMO detection, the expectation propagation (EP) detector has been proved to outperform classical and advanced solutions. Merging with the parity constraints of channel coding, a novel EP detection with linear programming (LP), named EPLP, is proposed to change the common initialization that the symbols are equiprobable by formulating an LP optimization problem. Furthermore, we apply the EPLP detector to a polar-coded MIMO system with an iterative detection and decoding (IDD) scheme. For a (128, 64) polar-coded 16 × 16 QPSK configuration, the EPLP outperforms the state-of-the-art detectors by 1 dB at the bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> and the IDD scheme with the EPLP achieves 0.5 dB gain at the frame error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> with a reduced number of EP iterations.

Kalman Combining Based Iterative Detection and Decoding for MIMO Systems With Hybrid ARQ

This study proposes Kalman combining-based iterative detection and decoding (KC-IDD) schemes for multiple-input multiple-output (MIMO) systems with hybrid automatic repeat request (HARQ). The conventional Kalman filtering (KF) operation for Kalman combining performs the linear minimum mean-square error (LMMSE) detection with symbol-level combining (SLC), but it is unable to utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information of retransmitted symbols. Therefore, a new KF operation is derived, wherein an observation adjustment is employed to adjust the observation for a given state of the state-space model with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information instead of directly utilizing it into the KF operation. Based on the modified KF operation, two KC-IDD schemes are developed: i) KC-IDD with the single-state observation adjustment, i.e., the observation adjustment for the current HARQ round, and ii) KC-IDD with multi-state observation adjustment (KC-IDD-MS), i.e., the observation adjustments throughout the HARQ rounds of a packet. Therefore, the proposed schemes can perform SLC-based LMMSE-IDD, where the complexity for a given number of turbo iterations is similar or smaller to that of the conventional LMMSE-IDD scheme with bit-level combining (BLC). Furthermore, the simulation results show that regardless of the retransmission strategy, the proposed schemes, especially the KC-IDD-MS scheme, outperformed the conventional LMMSE-IDD scheme in terms of error performance and decoding convergence speed for retransmissions.

A Very Fast Joint Detection for Polar-Coded SCMA

We propose a very fast-convergence joint iterative detection and decoding (JIDD) scheme for channel-coded sparse code multiple access (SCMA). In the conventional JIDD, all users’ channel decoding iterations are performed in parallel after all users’ variable nodes in the SCMA factor graph are updated. The proposed JIDD scheme, however, slices all channel decoding iterations into per-user channel decoding, and inserts them deeply into the factor graph. By doing this, message enhancement by channel decoding immediately propagates to the connected users’ messages in the factor graph, even within one message passing algorithm iteration, while maintaining the same total computational complexity per JIDD iteration. Numerical results confirm that in various link scenarios, the proposed scheme requires only two or three iterations for BER convergence, while the conventional JIDD scheme requires more than six iterations. In downlink scenarios, the proposed scheme achieves an even faster convergence rate. Moreover, in a uplink scenario with perfect power control, the converged BER of the proposed scheme is quite a bit lower than the conventional scheme, and the proposed scheme requires only two iterations to get the same BER level as the conventional scheme. Consequently, thanks to fast convergence, the proposed scheme dramatically reduces the overall computational complexity for achieving BER convergence. In addition, the fast convergence rate compensates for the multi-user detection latency issue, which is inherent in sequential algorithms, and the issue is further overcome by employing the group-wise sequential version of the proposed scheme.

Turbo Modulation Identification Algorithm for OFDM Software-Defined Radios

The problem of automatic modulation identification (AMI) is one of the main challenges that faces orthogonal frequency division multiplexing (OFDM) software-defined radio (SDR) systems. The previously reported studies of this aspect have limited to uncoded transmission; however, practical systems typically employ error-correcting codes. In this work, we exploit the properties of bit-interleaved coded modulation iterative decoding (BICM-ID) schemes, which are widely used in wireless communications systems, to develop a novel AMI algorithm for OFDM-SDR systems. Since the exact maximum-likelihood (ML) solution is intractable in practice, we resort to an expectation-maximization (EM) procedure as a statistical analysis tool. The proposed identification algorithm works in an iterative fashion, in which the soft information provided by the channel decoder of BICM-ID scheme is utilized as a priori information to the proposed identifier. The results indicate that the identification performance of the proposed algorithm achieves a significant improvement with iterations and outperforms that of the conventional uncoded algorithms. These favorable results are achieved with reduced processing time and acceptable sensitivity to transmission impairments, such as clock timing offset, frequency offset, Doppler shift, and phase noise.

Lightweight hamming product code based multiple bit error correction coding scheme using shared resources for on chip interconnects

In this paper, we present multiple bit error correction coding scheme based on extended Hamming product code combined with type II HARQ using shared resources for on chip interconnect. The shared resources reduce the hardware complexity of the encoder and decoder compared to the existing three stages iterative decoding method for on chip interconnects. The proposed method of decoding achieves 20% and 28% reduction in area and power consumption respectively, with only small increase in decoder delay compared to the existing three stage iterative decoding scheme for multiple bit error correction. The proposed code also achieves excellent improvement in residual flit error rate and up to 58% of total power consumption compared to the other error control schemes. The low complexity and excellent residual flit error rate make the proposed code suitable for on chip interconnection links.

Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation

Low-density parity-check (LDPC) codes have the potential for applications in future high throughput optical communications due to their significant error correction capability and the parallel decoding. However, they are not able to satisfy the very low bit error rate (BER) requirement due to the error floor phenomenon. In this paper, we propose a low-complexity iterative decoding scheme for product codes consisting of very high rate outer codes and LDPC codes. The outer codes aim at eliminating the residual error floor of LDPC codes with quite low implementation costs. Furthermore, considering the long simulation time of computer simulation for evaluating very low BER, the hardware platform is built to accelerate the evaluation of the proposed iterative decoding methods. Simultaneously, the fixed-point effects of the decoding algorithms are also be evaluated. The experimental results show that the iterative decoding of the product codes can achieve a quite low bit error rate. The evaluation using field programmable gate array (FPGA) also proves that product codes with LDPC codes and high-rate algebraic codes can achieve a good trade-off between complexity and throughput.

Iterative and Adjustable Soft List Decoding for Polar Codes

List decoding of polar codes is an outstanding decoding method, in which a fixed-size list is applied to retain the most probable paths. In general, it is preferable to use a large-size list in the decoding to achieve excellent error-control performance, but this can lead to high complexity. Moreover, the list decoding is still a one-time-pass algorithm with hard-decision outputs, which is not well suited for advanced concatenated coding systems. In this paper, a novel adjustable list decoding is proposed, in which the list size can be adjusted appropriately. Based on the reliability analyses of the decoding list, dynamic thresholds are designed to precisely guide the adjustments. The adjustable list decoding can also achieve excellent error-control performance, but the complexity is significantly reduced. Moreover, we propose an iterative adjustable list decoding scheme with soft-decision outputs for concatenated polar-coding systems, in which a log-likelihood-ratio update strategy is elaborately designed. It can achieve much better error-control performance compared with the existing iterative decoding schemes, such as the belief propagation (BP) decoding, and soft cancellation (SCAN) decoding. As the number of iterations increases, the decoding performance gradually improves, but the complexity increases little due to efficient complexity control designs.

Analysis of Near-Capacity Iterative Decoding Schemes for Wireless Communication Using EXIT Charts

This article proposes and compares the performance of three flexible and bandwidth efficient transceivers. The terminology of Over-Complete Mapping (OCM) is introduced in the first two schemes. All of the schemes, namely Non-Convergent Serial Concatenated OCM Coding (NCSCOC), Convergent Serial Concatenated OCM Coding (CSCOC) and Self-Concatenated Convolutional Coding (SECCC); are simulated for the Rayleigh channel and employing iterative decoding to attain the refined output stream for feeding the video decoder. Specifically, the iterative decoding is beneficial in acquiring the convergence of EXtrinsic Information Transfer (EXIT) curves by repeatedly sharing of the mutual information. The difference between the NCSCOC and CSCOC schemes lies in the inner and outer rates. This change reflects an improvement in the Bit-Error Rate (BER) and improved EXIT convergence of CSCOC scheme, with reference to NCSCOC scheme. Results show that NCSCOC scheme never reaches the point of perfect convergence despite iterative decoding. However, CSCOC and SECCC schemes succeed in securing perfect convergence. Investigating the BER curves, it is deducible that SECCC is the most desirable transceiver system, having the least BER. Furthermore, it is plausible that the overall performance of SECCC is much better than the preceding schemes. Explicitly, our experimental results show that the proposed CSCOC scheme outperforms its identical code rate counterpart NCSCOC scheme by about 3 dB at the Peak Signal-to-Noise Ratio (PSNR) degradation point of 1 dB. Additionally, an E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> gain of 20 dB is attained using SECCC scheme relative to identical code rate NCSCOC benchmarked scheme.

Hamming–Luby rateless codes for molecular erasure channels

Nano-scale molecular communications encode digital information into discrete macro-molecules. In many nano-scale systems, due to limited molecular energy, each information symbol is encoded into a small number of molecules. As such, information may be lost in the process of diffusion–advection propagation through complex topologies and membranes. Existing Hamming-distance codes for additive counting noise are not well suited to combat the aforementioned erasure errors. Rateless Luby-Transform (LT) code and cascaded Hamming-LT (Raptor) are suitable for information-loss, however may consume substantially computational energy due to the repeated uses of random number generator and exclusive OR (XOR). In this paper, we design a novel low-complexity erasure combating encoding scheme: the rateless Hamming–Luby Transform code. The proposed rateless code combines the superior efficiency of Hamming codes with the performance guarantee advantage of Luby Transform (LT) codes, therefore can reduce the number of random number generator utilizations. We design an iterative soft decoding scheme via successive cancelation to further improve the performance. Numerical simulations show this new rateless code can provide comparable performance comparing with both standard LT and Raptor codes, while incurring a lower decoder computational complexity, which is useful for the envisaged resources constrained nano-machines.

Universal Sparse Superposition Codes With Spatial Coupling and GAMP Decoding

Sparse superposition codes, or sparse regression codes, constitute a new class of codes, which was first introduced for communication over the additive white Gaussian noise (AWGN) channel. It has been shown that such codes are capacity-achieving over the AWGN channel under optimal maximum-likelihood decoding as well as under various efficient iterative decoding schemes equipped with power allocation or spatially coupled constructions. Here, we generalize the analysis of these codes to a much broader setting that includes all memoryless channels. We show, for a large class of memoryless channels, that spatial coupling allows an efficient decoder, based on the generalized approximate message-passing (GAMP) algorithm, to reach the potential (or Bayes optimal) threshold of the underlying (or uncoupled) code ensemble. Moreover, we argue that spatially coupled sparse superposition codes universally achieve capacity under GAMP decoding by showing, through analytical computations, that the error floor vanishes and the potential threshold tends to capacity, as one of the code parameters goes to infinity. Furthermore, we provide a closed-form formula for the algorithmic threshold of the underlying code ensemble in terms of Fisher information. Relating an algorithmic threshold to a Fisher information has theoretical as well as practical importance. Our proof relies on the state evolution analysis and uses the potential method developed in the theory of low-density parity-check (LDPC) codes and compressed sensing.

Iterative demodulation and decoding of polar coded BICM with bit-flipping

A new bit-interleaved coded modulation with iterative decoding (BICM-ID) scheme for polar codes is proposed, in which a flipping-based successive cancellation list (SCL) decoder is employed. This enhanced decoder can provide a large number of hard-decision outputs, from which only a few paths are selected and re-encoded to obtain a candidate set. Combined with the received signals and Pyndiah’s approximation, the candidate set generates extrinsic information which will be transmitted to the demodulator as the priori information after being interleaved. With the iterative decoding, the error performance of polar coded BICM scheme can be significantly improved. Simulation results show that the proposed flipping-based SCL decoder not only performs much better than the conventional SCL decoder with the same list size, but also shows competitiveness with some existing improved approaches of SCL decoding. More importantly, it generates efficient soft information which greatly improve the performance after iterative demodulation and decoding. Furthermore, the average complexity of the proposed BICM-ID scheme is acceptable and is similar to an SCL decoder at medium to high signal to noise ratio (SNR).

An Iterative Decoding Scheme for CPM-QC-LDPC Codes Based on Matrix Transform

An Iterative Decoding Scheme for CPM-QC-LDPC Codes Based on Matrix Transform

An iterative decoding scheme for Davey-MacKay construction

In the Davey-MacKay (DM) construction, the inner decoder treats unknown transmitted bits as random independent substitution errors. It limits the synchronization capability of the inner decoder, and thus weakens the error-correcting capability of the DM construction. In order to improve the performance of the DM construction, an iterative decoding scheme is proposed, which iteratively utilizes the more accurate estimates of transmitted codewords. In the proposed scheme, the estimated average bit error rates and the estimated low-density parity-check (LDPC) codewords from the outer decoder are fed back into the inner decoder to update the synchronization process. Simulation results show that the proposed iterative decoding scheme significantly outperforms the traditional DM construction.

Construction and Decoding of Rate‐Compatible Globally Coupled LDPC Codes

This paper presents a family of rate‐compatible (RC) globally coupled low‐density parity‐check (GC‐LDPC) codes, which is constructed by combining algebraic construction method and graph extension. Specifically, the highest rate code is constructed using the algebraic method and the codes of lower rates are formed by successively extending the graph of the higher rate codes. The proposed rate‐compatible codes provide more flexibility in code rate and guarantee the structural property of algebraic construction. It is confirmed, by numerical simulations over the AWGN channel, that the proposed codes have better performances than their counterpart GC‐LDPC codes formed by the classical method and exhibit an approximately uniform gap to the capacity over a wide range of rates. Furthermore, a modified two-phase local/global iterative decoding scheme for GC‐LDPC codes is proposed. Numerical results show that the proposed decoding scheme can reduce the unnecessary cost of local decoder at low and moderate SNRs, without any increase in the number of decoding iterations in the global decoder at high SNRs.

A novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization

In this paper, a novel soft reliability-based iterative majority-logic decoding algorithm with uniform quantization is proposed for regularly structured low density parity-check (LDPC) codes. A weighted measure is introduced for each check-sum of the parity-check matrix and a scaling factor is used to weaken the overestimation of extrinsic information. Furthermore, the updating process of the reliability measure takes advantage of turbo-like iterative decoding strategy. The main computational complexity of the proposed algorithm only includes logical and integer operations with the bit uniform quantization criterion. Simulation results show that the novel decoding algorithm can achieve excellent error-correction performance and a fast decoding convergence speed.

Optimal rate profile for multi‐user multi‐rate transmission systems by bivariate fixed‐point analysis

A K-user multi-rate code is proposed for a Gaussian multiple access channel with binary inputs, equal-power, and symbol synchronisation. In this multi-rate transmission, K users are equally divided into M groups. For each user in the mth group, a rate- 1 / q m regular repeat-accumulate code serially concatenated with a length- L m spreading is employed. The transmitted rate of each user in the mth group is 1 / ( q m L m ) . At the receiver, iterative joint decoding (IJD) and hybrid interference cancellation (HIC) schemes are considered. For each decoding scheme, a bivariate fixed-point analysis is applied to explicitly represent ( q m , L m ) as a function of mutual information outputs. On the basis of these basic explicit representations, a united unreliable region is given, where users in at least one group are undecodable. The complementary set of the united unreliable region gives an optimal rate profile that achieves the maximum sum rate. Numerical results show that, for the IJD scheme with M increments, the maximum sum rate increases, approaches the Shannon limit, and exceeds that in conventional equal rate transmission. The maximum sum rate of the HIC scheme, which provides much lower decoding complexity than the IJD scheme, is superior to the conventional successive interference cancellation scheme.

Ring-type Magnitude Modulation for LINC: a pragmatic approach to the efficiency challenge

This paper considers the use of the linear amplification with nonlinear components (LINC) technique for the power amplification of spectrally compact offset quadrature phase shift keying (OQPSK) signals allowing the use of highly efficient, low cost, and strongly nonlinear high power amplifiers (HPAs). However, the performance of the LINC signal separation and power combining procedures decreases with the rise of the signal’s peak-to-average power ratio (PAPR). A new ring-type magnitude modulation (RMM) method is proposed for OQPSK signals that limits both its maximum and minimum complex envelope excursions avoiding zero crossings, without spreading the transmitted signal’s spectrum. The performance results show that band-limited OQPSK signals whose envelope has low fluctuations produce LINC components with a narrower spectrum, with a considerable impact on the LINC transmitter regardless of the type of combiner chosen: when using a passive/matched combiner, the transmitter’s power efficiency is significantly increased without spreading the combined signal’s spectrum; for the highly efficient non-linear Chireix combiner, there is a reduction of the amount of spectral leakage produced by nonlinearly combining the LINC signal components. Finally, an iterative decoding scheme is also proposed, which employs estimates of the received symbols’ RMM coefficients to compensate the RMM distortion.