Conventional Decoding Scheme Research Articles

Simplified model for non-line-of-sight optical camera communication with a simple patterned reflective surface.

Most previous studies on non-line-of-sight optical camera communication (NLOS-OCC) were conducted as practical experiments; few relevant mathematical models have been proposed for simulation. This paper presents a simplified model for NLOS-OCC with a simple patterned reflective surface and proposes to directly decode the raw pixel values to improve the system performance. The model combines the mathematical models of a diffuse reflection channel and rolling shutter to obtain a normalized raw pixel value matrix of the pixel plane corresponding to the camera output image and realize visualization. Based on this model, we analyze the threshold selection and anti-interference ability of the conventional decoding scheme, and propose the sorting pre-decoding scheme: first sorting the pixel values by rows before decoding can effectively reduce the impact of the reflective surface pattern and enhance the system's anti-interference ability.

Convolutional-Neural-Network-Based Hexagonal Quantum Error Correction Decoder

Topological quantum error-correcting codes are an important tool for realizing fault-tolerant quantum computers. Heavy hexagonal coding is a new class of quantum error-correcting coding that assigns physical and auxiliary qubits to the vertices and edges of a low-degree graph. The layout of heavy hexagonal codes is particularly suitable for superconducting qubit architectures to reduce frequency conflicts and crosstalk. Although various topological code decoders have been proposed, constructing the optimal decoder remains challenging. Machine learning is an effective decoding scheme for topological codes, and in this paper, we propose a machine learning heavy hexagonal decoder based on a convolutional neural network (CNN) to obtain the decoding threshold. We test our method on heavy hexagonal codes with code distance of three, five, and seven, and increase it to five, seven, and nine by optimizing the RestNet network architecture. Our results show that the decoder thresholding accuracies are about 0.57% and 0.65%, respectively, which are about 25% higher than the conventional decoding scheme under the depolarizing noise model. The proposed decoding architecture is also applicable to other topological code families.

An Improved Decoding Scheme for Emergency Alert Wake-Up Bits in ATSC 3.0

This paper proposes an improved decoding scheme of emergency alert wake-up bits in Advanced Television Systems Committee (ATSC) 3.0. When a broadcast service is being provided, a bootstrap signal of ATSC 3.0 includes the minimal essential information that does not change at least for two consecutive physical layer frames. Then, the received signal can be further assumed to be binary modulated according to each wake-up bit, so as to achieve the improved decoding performance as well as reduced decoding complexity. The simulation results show that the proposed decoding scheme of wake-up bits can obtain signal-to-noise ratio (SNR) gains of approximately 1.6 dB and 2.5 dB at bit error rate (BER) of 10−4 compared to the conventional decoding scheme under fixed and mobile reception environments, respectively. In addition, the analysis on the computational complexity of the proposed decoding scheme is provided.

Efficient decoding of block turbo codes

Block turbo codes (BTCs) under iterative decoding are product codes showing excellent performance with reasonable complexity, whose component codes are conventionally decoded in two stages. The Chase algorithm is employed in the first stage to make a list of candidate codewords from the received vector, while the extrinsic information for iterative decoding is generated in the second stage. In this paper, we propose an efficient decoding algorithm for BTCs. The proposed algorithm can avoid a number of unnecessary hard-decision decoding operations by imposing two conditions on the Chase algorithm. Also, it simply computes the extrinsic information for the decision codeword. Numerical results demonstrate that the proposed algorithm has not only much lower computational complexity, but also a little better performance than the conventional decoding scheme based on the Chase algorithm. Furthermore, it can provide a trade-off between the performance and the computational complexity of BTCs by properly selecting a decoding parameter.

Multilabel Prediction via Cross-View Search

Embedding methods have shown promising performance in multilabel prediction, as they are able to discover the label dependence. However, most methods ignore the correlations between the input and output, such that their learned embeddings are not well aligned, which leads to degradation in prediction performance. This paper presents a formulation for multilabel learning, from the perspective of cross-view learning, that explores the correlations between the input and the output. The proposed method, called Co-Embedding (CoE), jointly learns a semantic common subspace and view-specific mappings within one framework. The semantic similarity structure among the embeddings is further preserved, ensuring that close embeddings share similar labels. Additionally, CoE conducts multilabel prediction through the cross-view $k$ nearest neighborhood ( $k$ NN) search among the learned embeddings, which significantly reduces computational costs compared with conventional decoding schemes. A hashing-based model, i.e., Co-Hashing (CoH), is further proposed. CoH is based on CoE, and imposes the binary constraint on continuous latent embeddings. CoH aims to generate compact binary representations to improve the prediction efficiency by benefiting from the efficient $k$ NN search of multiple labels in the Hamming space. Extensive experiments on various real-world data sets demonstrate the superiority of the proposed methods over the state of the arts in terms of both prediction accuracy and efficiency.

Low-Complexity Decoding of Block Turbo Codes Based on the Chase Algorithm

Block turbo codes (BTCs) are constructed by serially concatenating linear block codes and iteratively decoded by letting each component code be decoded in two stages. The Chase algorithm is employed in the first stage to make a list of candidate codewords by generating a fixed number of test sequences (TSs) and algebraically decoding them, regardless of the signal-to-noise ratio or the iteration number. In the second stage, the extrinsic information is generated for iterative decoding. In this letter, we propose a low-complexity decoding algorithm for BTCs. The proposed algorithm first checks whether an algebraic hard-decision decoder outputs a codeword for a given decoder input vector, and then adaptively applies one of the two estimation rules. Based on these two rules, the number of TSs in the proposed algorithm can be made monotonically decreasing with iterations. Numerical results demonstrate that the proposed algorithm has much lower computational complexity with a negligible performance loss, compared with the conventional decoding scheme based on the Chase algorithm.

New decoding scheme for LDPC codes based on simple product code structure

In this paper, a new decoding scheme for low-density parity-check (LDPC) codes using the concept of simple product code structure is proposed based on combining two independently received soft-decision data for the same codeword. LDPC codes act as horizontal codes of the product codes and simple algebraic codes are used as vertical codes to help decoding of the LDPC codes. The decoding capability of the proposed decoding scheme is defined and analyzed using the paritycheck matrices of vertical codes and especially the combined-decodability is derived for the case of single parity-check (SPC) and Hamming codes being used as vertical codes. It is also shown that the proposed decoding scheme achieves much better error-correcting capability in high signal to noise ratio (SNR) region with low additional decoding complexity, compared with a conventional decoding scheme.

Iterative Symbol Decoding of Distributed Channel Encoded Fixed-Length Codes for Cooperative Communications

In this paper, we study the iterative decoding of distributed channel encoded fixed-length codes for power-efficient cooperative transmissions. By exploiting the technique of iterative source channel decoding (ISCD), the iterative decoding for cooperative transmissions exploits two types of the redundant information: the residual redundancy inherently remaining in the source codes, and the artificial redundant information provided by the distributed channel coder. To avoid the information loss on the bit-to-symbol conversion, we adopt symbol-level ISCD scheme consisting of the symbol-level channel decoder and the symbol-level source decoder. Our proposed iterative decoding can improve parameter signal-to-noise ratio (PSNR) performances by iteratively refining the extrinsic information for the source and the distributed channel decoders. Furthermore, the proposed source decoding algorithm based on the symbol-level BCJR algorithm enables the iterative decoding to efficiently exploit the extrinsic information conveyed by relay and source nodes. Simulation results show that the proposed iterative decoding scheme with the proposed source decoder and the symbol-level BCJR channel decoder delivers the robust power-efficient performance, and outperforms conventional decoding schemes.

A novel decoding scheme based on recalculation for double binary convolutional turbo code

AbstractTo decrease the storage complexity of a double binary convolutional turbo code (DB‐CTC) decoder, a novel decoding scheme is proposed in this paper. Different from the conventional decoding scheme, only a part of the state metrics is stored in the last‐in first‐out (LIFO) state metrics cache (SMC). Based on an improved maximum a posteriori probability (MAP) algorithm, we present a method to recalculate the unstored state metrics at the corresponding decoding time slot, and discuss in detail the procedures of the recalculation are discussed. Because of the compare–select–recalculate processing operations, compared to the classical decoding scheme, the proposed decoding scheme reduces the storage complexity of SMC and the amount of memory accesses by approximately 40% while limiting involved computational cost. Moreover, simulation results show that the proposed scheme achieves good decoding performance, which is close to that of the well‐known Log‐MAP algorithm. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Iterative Receiver Processing for OFDM Modulated Physical-Layer Network Coding in Underwater Acoustic Channels

Physical-layer network coding (PLNC) allows two remote terminals to exchange information in two time slots via a half-duplex relay. In this paper, we consider PLNC in underwater acoustic (UWA) two-way relay networks, with particular attention on the recovery of the network-coded codeword at the relay. Considering multiple paths in UWA channels with large delay spreads and temporal variations, orthogonal-frequency division-multiplexing (OFDM) is used as an underlying modulation. Distinct from existing works which mainly focus on the flat-fading channel and the non-iterative PLNC decoding, we investigate three iterative receivers at the relay in doubly-selective UWA channels. To estimate the network-coded codeword, the three receivers respectively adopt i) a conventional decoding scheme to recover the codewords from two terminals independently; ii) a decoding scheme to recover the network-coded codeword directly, and iii) a joint decoding scheme to recover a composite codeword formed by the codewords from the two terminals. In the time-varying channel, the intercarrier interference (ICI) in the received signal has been explicitly addressed in three receivers. Extensive simulations and experimental results reveal that the decoding scheme for direct recovery of the network-coded codeword cannot work well in the multipath channel, and that with a lower complexity the performance of the iterative separate-decoding scheme can catch up with that of the joint decoding scheme.

Forced-convergence decoding for LDPC-coded modulation

Low-density parity-check (LDPC)-coded modulation has attracted much attention for its outstanding performance in a bandwidth-limited channel. However, it involves relatively high decoding complexity. Owing to different convergence behaviors of variable nodes, the nodes detected early can be deleted from the Tanner graph, which reduces the size of the parity-check matrix iteration by iteration and thus the decoding complexity. In this paper, we propose a new decoding algorithm for LDPC-coded modulation with reduced complexity. In addition, by deleting the converged bits from the modulation constellation and updating the bit metrics of the demapper with the reduced constellation, more accurate estimation of the bits can be achieved. Simulation results show that, compared with conventional decoding schemes, the proposed method not only greatly reduces the overall decoding complexity for LDPC-coded modulation but also significantly improves decoding performance.

A dual combining based decoding scheme for two-branched mobile STBC

In this paper, a dual combining based decoding scheme is proposed to enhance the detection performance of space-time block coding with two transmit antennas in a highly time-varying mobile channel. The proposed scheme analyzes the signal-to-noise ratio and signal-to interference plus noise ratio of decision variables for a zero-forcing combiner (ZFC) and a matched filter combiner (MFC), respectively. Based on the analysis, we derive a sophisticated decoding scheme which utilizes tentative detection symbols of the ZFC based decoder to cancel inter-symbol interference from the decision variables of the MFC. Computer simulations show that the proposed scheme outperforms the conventional decoding scheme with the ZFC or the MFC.

Partial ASN.1/BER decoding scheme in a multiprocessor environment

In many cases, the most time-consuming part of the Open Systems Interconnection (OSI) protocol stack is the encoding and decoding of transfer syntax done in the presentation layer. The conventional ASN.1/BER decoding scheme runs on a whole Presentation Protocol Data Unit (PPDU). To overcome this problem, we present a new ASN.1/BER decoding scheme called Partial Decoding. The Partial Decoding scheme allows us to start decoding partially on each segment of a PPDU before a whole PPDU has been received. The measures show that the new scheme has a performance improvement as high as 40% over the conventional decoding scheme at large message size. Parallel processing for the network layer through the presentation layer is also used for a high performance protocol implementation on the basis of the proposed scheme.

An error-correcting syntactic decoder for computer networks

An information source that generates messages with formal syntactic structures is proposed. It is modeled as a context-free grammar in Chomsky Normal Form. The messages are binary coded with or without error-correction capabilities, and are transmitted over a memoryless symmetric noisy channel. At the receiver, an algorithmic procedure is proposed for the correction of syntactic errors in the incoming messages. This is similar to the Cocke-Younger-Kasami parser with the additional capability of detecting and correcting syntactic errors in the input strings. Simulation results are presented which show that the present syntactic decoding scheme has a lower probability of decoding errors than conventional algebraic decoding schemes. Its performance is also compared with that of an alternate syntactic decoder studied recently.