Convolutional Codes Research Articles

DESIGN OF SPACE TIME TRELLIS CODED OFDM

Space Time Trellis Code (STTC) is a technique that can be used to improve the performance ofthe Orthogonal Frequency Division Multiplexing (OFDM) system over wireless channels byproviding both coding gain and diversity gain. STTC is combined from two codes, Trellis CodeModulation (TCM) as an inner code and Space Time Block Code (STBC) as an outer code. TCMwhich combines the choice of a modulation scheme with that of a convolutional code provides thecoding gain, and the other code provides the diversity gain. Simulation is done over flat fading andfrequency selective Rayleigh fading channel for Eight Phase Shift Keying (8-PSK) TCM. It foundthat the best results are obtained for the case of two transmitters and three receivers which have again of about 18dB over that without STBC.

A comparative analysis of convolutional coding and block coding techniques over satellite communication

Reliable data transmission is crucial in the field of satellite communication, and many coding schemes are used to improve this reliability. Two popular coding techniques: convolutional codes and block codes were investigated and compared in this paper. Utilizing a simulated environment, the author investigates the bit error rate (BER) performance of both coding strategies along with a baseline case of no coding. The analysis reveals distinct characteristics and performance attributes of the two coding techniques across a range of signal-to-noise rate (SNR) values. Convolutional codes, with their inherent memory, offer error protection across sequences, while block codes, particularly a Hamming-like 7,3 implementation, provide localized protection for individual blocks. The findings of this study contribute to the understanding of error control strategies in satellite communication, guiding the selection and design of coding methods suitable for various operational scenarios and requirements.

Study of the block and convolutional code decoding efficiency using a turbo code decoder

An urgent task is to correct errors that occur during the transmission and storage of information. To solve this problem, various noise-resistant codes have recently been increasingly used. Turbo codes have the best correcting ability in data transmission channels with a high probability of an erroneous bit. To obtain the necessary characteristics, various combinations of codes and encoding and decoding methods are used. If another block of information bits is added to the codeword of a systematic block or systematic convolutional code, then it will be possible to implement decoding of such a codeword by a turbo code decoder. The disadvantage of the resulting combined code is its extremely low correcting ability in the presence of errors in the check bits of the codeword. The article shows that the correcting ability of the presented code can be significantly increased by multiplying the decoded values of the check bits by reduction factors.

Unsupervised Energy Disaggregation Via Convolutional Sparse Coding

Unsupervised Energy Disaggregation Via Convolutional Sparse Coding

The model of a discrete communication channel in the control channel of satellite communication systems

The control channels used in modern satellite communication systems (CCC) for multiplexers used in modern satellite communication systems are designed to transmit control messages regarding changes in the frame structures of organized compressed digital streams. Increased requirements are placed on the reliability of messages transmitted in these channels compared to messages transmitted in sealed channels. In the presence of phase flickering, as well as with a decrease in the signal-to-noise ratio (SNR) in communication channels formed on the basis of convolutional codes, the effect of grouping errors occurs at the input of decoders of noise-resistant codes in control channels. Therefore, information about the statistical properties of messages received at the input of a decoder designed to ensure the reliability of control channel messages is important in the synthesis of decoding algorithms capable of taking into account these statistical properties.

Self-Dual Convolutional Codes

Self-Dual Convolutional Codes

On the construction of MRD convolutional codes

The problem of building optimal block codes, such as MDS codes, over small fields has been an active area of research that led to several interesting conjectures. In the context of convolutional codes, optimal constructions, such as MDS or MDP, are very rare and all require very large finite fields. In this work, we focus on the problem of constructing optimal convolutional codes with respect to the rank distance, i.e. we study the construction of Maximum Rank Distance (MRD) convolutional codes. Considering convolutional codes within a very general framework, we present concrete novel classes of MRD convolutional codes for a large set of given parameters.

A Method for Calculating of the Efficiency of Communication Systems with Multiple Modulation Types and Multi-Stage Composite Coding

Analytical relations and a methodology for calculating the performance indicators of communication and multimedia broadcasting systems using multi-position modulation types (QAM-M, FM-M, FM-M, CHMS-M, AM-M, OFM-M) and multi-stage coding based on non-systematic convolutional codes and non-binary block Reed-Solomon codes are proposed. The relations obtained are common for studies of noise immunity, corrective capacity, energy and information efficiency of systems, depending on the requirements for reception reliability, modulation format, type of codes used and their parameters. The calculation of the characteristics of systems with cascade composite coding is carried out in a closed form and does not require the use of complex computer simulation procedures.

Convolutional sparse coding for compressed sensing photoacoustic CT reconstruction with partially known support.

In photoacoustic tomography (PAT), imaging speed is an essential metric that is restricted by the pulse laser repetition rate and the number of channels on the data acquisition card (DAQ). Reconstructing the initial sound pressure distribution with fewer elements can significantly reduce hardware costs and back-end acquisition pressure. However, undersampling will result in artefacts in the photoacoustic image, degrading its quality. Dictionary learning (DL) has been utilised for various image reconstruction techniques, but they disregard the uniformity of pixels in overlapping blocks. Therefore, we propose a compressive sensing (CS) reconstruction algorithm for circular array PAT based on gradient domain convolutional sparse coding (CSCGR). A small number of non-zero signal positions in the sparsely encoded feature map are used as partially known support (PKS) in the reconstruction procedure. The CS-CSCGR-PKS-based reconstruction algorithm can use fewer ultrasound transducers for signal acquisition while maintaining image fidelity. We demonstrated the effectiveness of this algorithm in sparse imaging through imaging experiments on the mouse torso, brain, and human fingers. Reducing the number of array elements while ensuring imaging quality effectively reduces equipment hardware costs and improves imaging speed.

Design and Development of a CCSDS 131.2-B Software-Defined Radio Receiver Based on Graphics Processing Unit Accelerators

In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers.

An Approach to Constructing Convolutional Codes With Moderate Density and Quasi-Cyclic Structure

There exist in the literature two natural generalizations of low density parity check (LDPC) codes: 1) LDPC convolutional codes or sometimes also called spatially coupled LDPC codes which have shown to be able to reach Shannon capacity in a practical way. 2) Moderate density parity check (MDPC) codes which are linear codes possessing a parity check matrix whose row weight is not more than O(√n), still allowing efficient decoding with high probability. MDPC codes became a highly interesting class of block codes for the purpose of doing code-based cryptography.In this paper we study MDPC convolutional codes and some of their basic properties. At the end of the paper we explain how one can construct a new code-based cryptographic system. This new system can be seen as a convolutional version of the famous BIKE system currently evaluated for possible standardization by the National Institute for Standards and Technology (NIST).

Design of Autoconfigurable Random Access NOMA for URLLC Industrial IoT Networking

Low power devices are massively deployed to facilitate real time sensing and data transmission requested in low power wide-area network (LPWAN). However, the dominating signaling, i.e. continuous phase modulation (CPM), barely gains attention in the context of supporting massive connectivity, not to mention ultra-reliable low-latency communications (URLLC), a primary concern in industrial network automation. To this end, auto-configurable nonorthogonal multiple access (AC-NOMA) based on CPM signaling is proposed. The term auto-configuration is used since each device selects a setup from a pool of configurations whenever connecting to the access point in a random and distributive way. It is proven, using ideal power allocation and phase shaping technique, AC-NOMA offers drastically improved user load and near capacity performance even in finite blocklength regime. Moreover, to enable massive yet sporadic access, slotted ALOHA is combined with AC-NOMA. It is proven that the resultant scheme outperforms power-domain NOMA in terms of throughput even with reduced transmit power and simple forward error correction schemes such as repetition and convolutional coding. The throughput is further improved using semi AC-NOMA with slightly increased latency. It is demonstrated that both designs can support very high user load while enabling URLLC in finite blocklength regime, where the packet size is merely 256 bits while the error rate is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10^{-5}$</tex-math></inline-formula> , which are also desirable in a number of applications including satellite communications, visible light communications, etc.

Analysing the Performance of OFDM-SPM With and Without channel Coding to Enhance the Spectral Efficiency and BER

A modulation technique has recently developed known as “OFDM with Subcarrier Power Modulation (OFDM-SPM)”. This technique utilizes two types of modulation. The first one called power modulation, where the power level of each subcarrier changes depending on the bit stream while transmit uses another type of conventional modulation such as a QPSK. The results shows that the adoption of OFDM with SPM improve the throughput, save the power, reduce the complexity, and so on. On the negative side, it shows degradation in the “Bit Error Rate” (BER) performance. In this paper, the convolutional code is the proposed to improve the BER while paired with the vitribi hard decision decoder which is considered easy to implement of the hardware and costs. the suggested methods will be discussed in the terms of throughput and BER with the three power reallocation policies ) PRPs).

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

CSC-Unet: A Novel Convolutional Sparse Coding Strategy Based Neural Network for Semantic Segmentation

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration

Towards Robustness and Efficiency of Coherence-Guided Complex Convolutional Sparse Coding for Interferometric Phase Restoration

Integrating Convolution and Sparse Coding for Learning Low-Dimensional Discriminative Image Representations.

This work investigates the problem of efficiently learning discriminative low-dimensional (LD) representations of multiclass image objects. We propose a generic end-to-end approach that jointly optimizes sparse dictionary and convolutions for learning LOW-dimensional discriminative image representations, named SparConvLow, taking advantage of convolutional neural networks (CNNs), dictionary learning, and orthogonal projections. The whole learning process can be summarized as follows. First, a CNN module is employed to extract high-dimensional (HD) preliminary convolutional features. Second, to avoid the high computational cost of direct sparse coding on HD CNN features, we learn sparse representation (SR) over a task-driven dictionary in the space with the feature being orthogonally projected. We then exploit the discriminative projection on SR. The whole learning process is consistently treated as an end-to-end joint optimization problem of trace quotient maximization. The cost function is well-defined on the product of the CNN parameters space, the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. By using the explicit gradient delivery, the cost function is optimized via a geometrical stochastic gradient descent (SGD) algorithm along with the chain rule and the backpropagation. The experimental results show that the proposed method can achieve a highly competitive performance with the state-of-the-art (SOTA) image classification, object categorization, and face recognition methods, under both supervised and semi-supervised settings. The code is available at https://github.com/MVPR-Group/SparConvLow.

Evaluation of Polar-Coded Noncoherent Acoustic Underwater Communication

This article investigates several variants of noncoherent multiple-frequency-shift keying (MFSK) that uses Polar coding and evaluates their applicability to robust underwater acoustic underwater communication. A simplified data model is assumed, yielding a low-complexity MFSK symbol estimator, and symbol likelihoods are calculated using sparse pilot symbols. Results presented from field tests in the Stockholm archipelago indicate that a Polar code will decode with a low frame error rate (FER) if not signal-to-noise ratio (SNR) limited and that its performance is superior to convolutional coding. The reliability of short frames, with 0.16–0.5 s transmission time, and long frames, with spectral efficiency up to 0.1 b/s/Hz, are evaluated in the five replay channels of the Watermark benchmark over varying SNR and SNR per bit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$E_{b}/N_{0}$</tex-math></inline-formula> . Synchronization is solved with a sliding hypothesis approach and the results show a FER <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lesssim 1 \%$</tex-math></inline-formula> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> Watermark channels, where the robustness is significantly improved by either increasing the frequency separation of tones or lowering the code rate and extending the frame length. The performance compares favorably to methods found in the literature, particularly for the less-benign channels of the benchmark.

CRC-Aided High-Rate Convolutional Codes With Short Blocklengths for List Decoding

CRC-Aided High-Rate Convolutional Codes With Short Blocklengths for List Decoding

Criteria for the construction of MDS convolutional codes with good column distances

Criteria for the construction of MDS convolutional codes with good column distances

Selection and justification of polynomial generators for encoding telemetric information in Autonomous well systems

In this paper, the issue of effective implementation of wireless packet data transmission for oil production systems is raised in order to form a competence-based approach to solving key problems of field development in conditions of disequilibrium and heterogeneity of geological and field data. Using the proof of the theorem on the definition of the convolutional code over the Galois field, boundary conditions are obtained, the creation of which is necessary in order to implement high noise immunity when transmitting telemetric signals from wells to the “day surface”. The algorithm presented in the study is based on the application of equations of various orders to level out the significant heterogeneity of field data, which, in conditions of a widespread increase in the number of wells, has a significant impact on making effective and high-quality management decisions. The results of the study can also serve to determine the correction capabilities provided by digital signal processing computing systems in autonomous downhole telemetry modules.