Soft Decoding Research Articles

An Image-Retrieval Method Based on Cross-Hardware Platform Features

Artificial intelligence (AI) models have already achieved great success in fields such as computer vision and natural language processing. However, deploying AI models based on heterogeneous hardware is difficult to ensure accuracy consistency, especially for precision sensitive feature-based image retrieval. In this article, we realize an image-retrieval method based on cross-hardware platform features, aiming to prove that the features of heterogeneous hardware platforms can be mixed, in which the Huawei Atlas 300V and NVIDIA TeslaT4 are used for experiments. First, we compared the decoding differences of heterogeneous hardware, and used CPU software decoding to help hardware decoding improve the decoding success rate. Then, we compared the difference between the Atlas 300V and TeslaT4 chip architectures and tested the differences between the two platform features by calculating feature similarity. In addition, the scaling mode in the pre-processing process was also compared to further analyze the factors affecting feature consistency. Next, the consistency of capture and correlation based on video structure were verified. Finally, the experimental results reveal that the feature results from the TeslaT4 and Atlas 300V can be mixed for image retrieval based on cross-hardware platform features. Consequently, cross-platform image retrieval with low error is realized. Specifically, compared with the Atlas 300V hard and CPU soft decoding, the TeslaT4 hard decoded more than 99% of the image with a decoding pixel maximum difference of +1/−1. From the average of feature similarity, the feature similarity between the Atlas 300V and TeslaT4 exceeds 99%. The difference between the TeslaT4 and Atlas 300V in recall and mAP in feature retrieval is less than 0.1%.

Design of DNA Storage Coding Scheme With LDPC Codes and Interleaving.

In this paper, we propose a new coding scheme for DNA storage using low-density parity-check (LDPC) codes and interleaving techniques. While conventional coding schemes generally employ error correcting codes in both inter and intra-oligo directions, we show that inter-oligo LDPC codes, optimized by differential evolution, are sufficient in ensuring the reliability of DNA storage due to the powerful soft decoding of LDPC codes. In addition, we apply interleaving techniques for handling non-uniform error characteristics of DNA storage to enhance the decoding performance. Consequently, the proposed coding scheme reduces the required number of oligo reads for perfect recovery by 26.25% ~ 38.5% compared to existing state-of-the-art coding schemes. Moreover, we develop an analytical DNA channel model in terms of non-uniform binary symmetric channels. This mathematical model allows us to demonstrate the superiority of the proposed coding scheme while isolating the experimental variation, as well as confirm the independent effects of LDPC codes and interleaving techniques.

Fault tolerant micro-programmed control unit for SEU and MBU mitigation in space based digital systems

The Micro-Programmed Control Unit (MPCU) offers an alternative to conventional hardwired control circuits by employing a control store for transmitting control signals through stored control bits. Traditionally, Hamming codes are used for Single-Event Upset (SEU) protection in memory components. However, these Error Control Codes (ECC) encounter challenges, such as inconsistent code rates and limited adaptability for Multiple Bit Upsets (MBU). To address MBU protection, ECC codes with soft decoding logic are commonly utilized, leading to increased hardware complexity. This paper introduces a lightweight encoding and hard-decoding logic capable of detecting and correcting up to n2∗ upsets, achieving a code rate of ≈ 0.37. Through evaluations against both traditional and recent ECC methods, the proposed approach demonstrates enhanced fault correction capabilities compared to Hamming codes. Furthermore, it maintains a consistent code rate, incurs lower area overhead, and exhibits less hardware complexity than MBU codes by employing hard decoding logic. The suggested methodology effectively mitigates both MBU and SEUs in control stores, providing reduced delay and making it well-suited for ensuring reliability in high-speed memory applications like MPCUs.

Iterative Soft Decoding Algorithm for DNA Storage Using Quality Score and Redecoding.

Ever since deoxyribonucleic acid (DNA) was considered as a next-generation data-storage medium, lots of research efforts have been made to correct errors occurred during the synthesis, storage, and sequencing processes using error correcting codes (ECCs). Previous works on recovering the data from the sequenced DNA pool with errors have utilized hard decoding algorithms based on a majority decision rule. To improve the correction capability of ECCs and robustness of the DNA storage system, we propose a new iterative soft decoding algorithm, where soft information is obtained from FASTQ files and channel statistics. In particular, we propose a new formula for log-likelihood ratio (LLR) calculation using quality scores (Q-scores) and a redecoding method which may be suitable for the error correction and detection in the DNA sequencing area. Based on the widely adopted encoding scheme of the fountain code structure proposed by Erlich et al., we use three different sets of sequenced data to show consistency for the performance evaluation. The proposed soft decoding algorithm gives 2.3% ∼ 7.0% improvement of the reading number reduction compared to the state-of-the-art decoding method and it is shown that it can deal with erroneous sequenced oligo reads with insertion and deletion errors.

ELDPC: An Efficient LDPC Coding Scheme for Phase-Change Memory

Low read latency, long lifetime, and high storage density have all been demonstrated in phase-change memory (PCM), making it an attractive contender for main memory. However, due to resistance drift per cell caused by long-term storage, data reliability becomes a major challenge. Low-density parity-check (LDPC) codes with improved error correction capability can be used in PCM to reduce bit error rates and thus improve data reliability. More interestingly, when the raw bit error rates (RBER) of various pages in PCM is compared at the same storage time, a considerable gap appears, resulting in high sensing and decoding latency. We propose eLDPC, an efficient LDPC coding scheme for reducing sensing and decoding latency, in this paper. We start with a preliminary experiment, which reveals that there is a significant variation in resistance drifts between adjacent distributions, resulting in a large RBER gap for different pages. Then, using a submatrix of the parity-check matrix to shorten the codeword length, eLDPC is inspired to encode pages with lower RBER. The original bit sequence is separated into even bit sequence (EBS) and odd bit sequence (OBS) for pages with higher RBER. eLDPC is used to encode EBS and OBS independently. By utilizing optimized soft information, EBS and OBS are eLDPC decoded. eLDPC can significantly improve error correction capability of LDPC hard decoding, effectively eliminating soft decoding processes and lowering decoding latency. The results of simulations show that eLDPC can greatly decrease decoding iterations and time.

Efficient Massive Machine Type Communication (mMTC) via AMP

We propose efficient and low-complexity multiuser detection (MUD) algorithms for Gaussian multiple access channel (G-MAC) for short-packet transmission in massive machine type communications. To do so, we first formulate the G-MAC MUD problem as a sparse signal recovery problem and obtain the exact and approximate joint prior distribution of the sparse vector to be recovered. Then, we employ the Bayesian approximate message passing (AMP) algorithms with the optimal separable and nonseparable minimum mean squared error (MMSE) denoisers for soft decoding of the sparse vector. The effectiveness of the proposed MUD algorithms for a large number of devices is supported by simulation results. For packets of 8 information bits, while the state-of-the-art AMP with soft-threshold denoising achieves 8/100 of the upper bound at Eb/N0 = 4 dB, the proposed algorithms reach 4/7 and 1/2 of the upper bound.

A Detection Scheme for Integrated SWIPT Receivers With Rectenna Arrays

Smart devices and their applications led to a rapid evolution of the Internet of Things. As a result, there is an emerging need on energizing those low-power devices, while at the same time achieving information transfer. In this context, simultaneous wireless information and power transfer (SWIPT) is considered as an appealing technology, since it can power wirelessly and remotely such devices, simultaneously with information transfer. Motivated by this, we herein propose a system model with an integrated SWIPT receiver, succeeding enhanced performance in terms of information decoding (ID) and energy harvested (EH). Specifically, we adopt a single input multiple output topology, where at the receiver’s side we examine two antenna architectures, one based on the direct-current (DC) combining and the other on the radio-frequency (RF) combining. For both schemes, we consider multiple receive antennas and rectifiers, comparing their performances. Furthermore, we adopt a piece-wise linear EH model, which takes into account the sensitivity and saturation effects of the rectifiers. By using a maximum likelihood-based soft decoding, a non-coherent energy detection is achieved. Finally, an asymptotic analysis is conducted, providing upper and lower bounds for ID and EH, respectively. Simulation results along with theoretical analysis, validate the enhanced performance both in terms of ID and EH.

Research on Particle Swarm Fusion Sliding Mode Tracking Decoding Technology for Rotary Transformer

A sliding mode tracking fusion particle swarm optimization (PSO) was proposed to solve the soft decoding problem of the rotary transformer. Firstly, the principle of soft decoding of the rotary transformer was expounded and analyzed. Then the mathematical model of the sliding mode tracking and decoding system was established. Based on sliding mode tracking and decoding, the parameters in the model were selected iteratively by using a particle swarm optimization algorithm according to the fitness value function. Finally, the simulation environment was built by Simulink and S-function for simulation verification. The simulation results show that the decoding scheme based on particle swarm optimization and sliding mode tracking can track and decode the rotating transformers at different speeds, such as uniform speed, acceleration speed, and sines/cosines speed, without steady-state error and with certain anti-interference.

Study of permutation decoding algorithms in UAV control systems

Relevance. This paper presents relevant topic of the use of unmanned aerial vehicles (UAV) in the agroindustrial complex (AIC) to obtain timely reliable information about the state of agricultural land for the entire period. In modern infocommunication technologies and communication systems, high requirements are placed on the reliability of information transmission. In mobile data exchange systems, compositions of noise-resistant codes are used in the form of cascade designs or turbo codes, and in order to increase their efficiency, it is necessary to make the fullest use of the redundancy introduced at each stage of data processing. To solve such a problem, it is advisable to apply a decoding method based on the ordering of the character confidence indices (CCI) of the code combination.Methods. When determining the encoding-decoding systems, the Mathcad application software was used. Mathematical studies have shown that binary block codes for reaching the specified boundary can be decoded not only on the basis of ordered statistics, but also using the method of dividing the space of allowed code combinations into clusters. Based on this method, a block diagram of the algorithm of the simulation model of the operation of the block code decoder based on cluster calculation is compiled.Results. The application of the proposed method of soft decoding of systematic block codes reduces the processing time of code combinations in the decoder by reducing the dimension of the square matrix to determine the nonlinearity property of the rows of the rearranged generating matrix after ordering the columns of the original generating matrix.

Adding RLL Properties to Four CCSDS LDPC Codes without Increasing Their Redundancy

This paper presents the construction of Run Length Limited (RLL) Error Control Codes (ECCs) from four Low Density Parity Check (LDPC) Codes specified by Consultative Committee for Space Data Systems (CCSDS). The obtained RLL-ECCs present a practical alternative to the CCSDS codes with pseudo-randomizers. Their advantage is that the maximal runlengths of equal symbols in their codeword sequences are guaranteed, which is not the case if the common approach with pseudo-randomizers is used. The other advantages are that no additional redundancy is introduced into encoded codewords and that the encoding and decoding procedures of the original error control CCSDS codes do not have to be modified in the following cases. In the first case if hard decoding is used and the transmission channel can be modeled as a Binary Symmetric Channel (BSC) or in the second case if soft decoding and coherent Binary Phase Shift Keying (BPSK) modulation is used and the appropriate transmission channel model is an Additive White Gaussian Noise (AWGN) channel.

AllSpark: Enabling Long-Range Backscatter for Vehicle-to-Infrastructure Communication

Long-range backscatter communication has the potential to provide enough time and space for vehicles to detect traffic information, which is an attractive solution for Vehicle-to-Infrastructure (V2I) communication. However, existing backscatter studies either require the tag to be close to the carrier source (ambient backscatter) or have poor receiver sensitivity (RFID), making it challenging to satisfy the high range requirements of V2I communication. In this article, we develop AllSpark to investigate the feasibility of long-range communication enabling backscatter to be applied to V2I. Specifically, to increase RSS to compensate for the enormous dual-path loss in backscatter systems, we first redesign the tag’s radio frequency (RF) front-end to amplify the incident signals, and then we design high-gain directional antennas for the tag and reader. Second, we present EC-Net, an end-to-end demodulator that selectively enhances signal features (denoising) and is tuned to lower classification (demodulation) error to maximize demodulation accuracy. Furthermore, we adopt convolutional encoding for tag data and use the output probability of EC-Net to design an effective soft decoder to resist occasional interference and noise, improving communication robustness. Our prototype and experiments outdoors verify the effectiveness of AllSpark which can provide a communication range of 700 m. Even in a moving scene, it can achieve a communication range of 600 m, demonstrating that AllSpark has the potential to be applied for autonomous driving and low-flying drones to detect traffic conditions.

Run length limited CCSDS convolutional codes for optical communications

Abstract This paper presents the construction of RLL-ECCs (run length limited error control codes) from three selected ECCs specified by Consultative Committee for Space Data Systems (CCSDS) for optical communications. The RLL-ECCs obtained present a practical alternative to CCSDS codes with pseudo-randomizers. Their advantage is that the maximal run lengths of equal symbols in their codeword sequences are guaranteed, which is not the case if the common approach with pseudo-randomizers is used. The other advantages are that no additional redundancy is introduced into encoded codewords and that the encoding and decoding procedures of the original error control CCSDS codes do not have to be modified in the following cases: Firstly, if hard decoding is used and the transmission channel can be modeled as a BSC (binary symmetric channel) and secondly, if soft decoding and coherent BPSK (binary phase shift keying) modulation is used and the appropriate transmission channel model is an AWGN (additive white Gaussian noise) channel.

Hadamard–Viterbi Joint Soft Decoding for MFSK Underwater Acoustic Communications

Multiple Frequency-Shift Keying (MFSK) has been used widely for underwater acoustic communications due to its low complexity and channel robustness. However, the traditional MFSK has the limitation of a low bit rate compared with coherent acoustic communication. To increase the bit rate, this study designs a new MFSK with the concept of orthogonal frequency division multiplexing (OFDM). We also adopt a channel-concatenated coding to resist the multipath interference and design the iterative joint decoding. The channel-concatenated coding consists of a Hadamard code and a convolutional code. Correspondingly, the iterative joint decoding uses the Hadamard–Viterbi joint soft decoding framework with a newly designed branch metric, which uses the Hadamard structure. As an important preprocessing link for a received signal, frame synchronization and Doppler compensation are also described in detail in this study. Simulations and experiments are conducted to show the effectiveness of the proposed MFSK underwater acoustic communications.

Low-complexity hybrid interference cancellation for sparse code multiple access

Sparse Code Multiple Access (SCMA) is one of Non Orthogonal Multiple Access (NOMA) candidates for 5G, the fifth generation mobile network. Multiple overlapped blocks of SCMA code are transmitted per frequency layer which compels the receiver to cancel unwanted SCMA code blocks. Thanks to the sparsity of SCMA, Message Passing Algorithm (MPA) achieves near-optimal performance. Nevertheless, this process is time-consuming and has a high degree of complexity even if Max-Log Algorithm is used. The major contribution of this paper is to propose a new decoding scheme called Hybrid Interference Cancellation (HIC) to reduce time processing without degrading a target Bit Error Rate (BER). The HIC decodes and removes the strongest user, reducing the complexity of Log Domain MPA decoding of remaining users, followed by a Low Density Parity Code (LDPC) decoder. The interference cancellation decoder is either a hard interference cancellation or a soft interference cancellation. The originality of the paper is to calculate a threshold value based on power measurements to decide whether hard decoder or soft decoder should be used to reach the BER target with a lower complexity. Results show a significant reduction in terms of complexity as compared to the traditional MPA process for the same values of BER.

Beyond 100 Gbit/s Pipeline Decoders for Spatially Coupled LDPC Codes

Low-density parity-check (LDPC) codes are a well-established class of forward error correction codes that provide excellent error correction performance for large code block sizes. However, for throughputs toward 1 Tbit/s, as expected for B5G systems, state-of-the-art LDPC soft decoders are restricted to short code block sizes of several hundred to thousands of bits due to routing congestion challenges, limiting the overall communications performance of the transmission system. Spatially coupled LDPC (SC-LDPC) codes and respective sliding window decoding methods show the potential to overcome these block size restrictions. However, in contrast to conventional LDPC codes little literature exists on the efficient hardware implementation of respective high-throughput decoders. In this work, we present the first in-depth investigation on the implementation of SC-LDPC decoders for throughputs beyond 100 Gbit/s. For an N=51328, R=0.8 terminated SC-LDPC code with sub-block size c=640 and coupling width m_s=1, we explore various design trade-offs, including row- and column-wise decoding, non-overlapping and overlapping window scheduling, and processor pipelining. To the best of our knowledge, this is the first description of a column-wise SC-LDPC decoding architecture in the literature. We complement the algorithmic investigation with the virtual silicon implementation of all presented decoders in a 22nm FD-SOI technology.

Performance Enhanced of Geometrically Shaped 64-QAM With LDPC in UOWC System

In this letter, to mitigate the decrease of the received optical power caused by scattering, attenuation and reflection in underwater optical wireless communication (UOWC) system, geometrically shaped (GS) 64-ary quadrature amplitude modulation (QAM) based orthogonal frequency division multiplexing (OFDM) is proposed and experimentally demonstrated in UOWC system. And low-density parity-check codes (LDPC) are employed. By using soft decoding algorithm of LDPC, the complexity of demodulation for GS 64-QAM is the same as the uniform 64-QAM. The experimental results show that, the data rate of GS 64-QAM OFDM signal can achieve 3.33Gbps after transmission over 4.2m underwater and 5m free space channel. Compared with the uniform 64-QAM scheme, the proposed scheme can improve the receiver sensitivity of 0.8dB at the bit-error rate (BER) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.8\times 10^{-3}$ </tex-math></inline-formula> .

A Viterbi Decoder under Class A Modeled Noise in Shallow Water

A traditional Viterbi decoder is primarily optimized for additive white Gaussian noise (AWGN). With the AWGN channel, it offers good decoding performance. However, the underwater acoustic communication (UAC) channel is extremely complicated. In addition to white noise, there are a variety of artificial and natural impulse noise that occur suddenly. The traditional Viterbi decoder cannot obtain the optimum performance under this case. In order to solve this problem, this paper introduces a novel Viterbi decoder with the impulsive noise, which is considered to be subjected to Middleton Class A distribution in shallow ocean. Since Middleton Class A noise is very complicated, a simplified model is first introduced. Then, the error analysis of simplified model under various parameters is discussed in detail. The analysis shows that the simplified one just leads to slight error. Hereafter, a novel Veterbi decoder using the simplified model is discussed. Compared to a traditional decoder, a preprocessing is just required. The performance of soft decision-based decoder in the Middleton Class A noise channel (MAIN) and AWGN are further compared. Based on our simulations, the new decoder can significantly improve the performance in comparison with conventional one, which further validates our presented method.

Sparse Superimposed Coding for Short-Packet URLLC

Sparse vector coding (SVC) is emerging as a key enabler for short packet ultra-reliable and low latency communications (URLLC), since it displays good block error rate (BLER) performance and can achieve low transmission latency. In this paper, we propose a SVC-based sparse superimposed transmission (SVC-ST) coding scheme to further enhance the BLER performance of SVC scheme. At the encoding side, a portion of transmission bits is represented by non-zero position indices of sparse vector. The remaining bits are equally split into multiple streams and then mapped into the non-zero positions of sparse vector via quadrature amplitude modulation (QAM) with constellation rotation (CR). We afterwards adopt the multipath matching pursuit-based soft decoding (MMP-SD) to recover the transmission packet. The BLER and bit error rate (BER) analysis of SVC-ST scheme demonstrate the validity and rationality of our study. Moreover, we find from the simulation results that the proposed SVC-ST scheme outperforms SVC and its enhanced version (ESVC) schemes in terms of BLER and latency performance.

Implementation of Soft Decoding Mechanism for Addressing Nonlinearities in Long-Distance Optical Transmission System

A soft decoding technique is discussed in this paper to improve the performance of long-distance optical networks (LDOTNs). LDOTNs are affected by phase noise and nonlinearities generated inside the fiber. The investigations of the proposed LDOTN were carried out by dual-polarization 16-quadrature amplitude modulation (DP-16QAM), DP-64QAM over single-mode fiber (SMF) and digital signal processing (DSP) methodologies. The improved performance of the presented mechanism is discussed over SMF based on constellation shaping (CS). The CS of the presented LDOTN is then compared to the standard 16-QAM and 64-QAM using international telecommunication union-telecommunication (ITU-T) standard G-652.D and G-657.A1 SMF. The soft detecting procedure enables the LDOTNs to attain significant outcomes.

Hybrid neural coded modulation: Design and training methods

We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.