Network Coding Research Articles

Optimization of Hierarchical-Modulated and LDPC-Coded BICM With Physical-Layer Network Coding

Optimization of Hierarchical-Modulated and LDPC-Coded BICM With Physical-Layer Network Coding

Predictive Modeling of the Hydrate Formation Temperature in Highly Pressurized Natural Gas Pipelines

In this study, we aim to develop advanced machine learning regression models for the prediction of hydrate temperature based on the chemical composition of sweet gas mixtures. Data were collected in accordance with the BOTAS Gas Network Code specifications, approved by the Turkish Energy Market Regulatory Authority (EMRA), and generated using DNV GasVLe v3.10 software, which predicts the phase behavior and properties of hydrocarbon-based mixtures under various pressure and temperature conditions. We employed linear regression, decision tree regression, random forest regression, generalized additive models, and artificial neural networks to create prediction models for hydrate formation temperature (HFT). The performance of these models was evaluated using the hold-out cross-validation technique to ensure unbiased results. This study demonstrates the efficacy of ensemble learning methods, particularly random forest with an R2 and Adj. R2 of 0.998, for predicting hydrate formation conditions, thereby enhancing the safety and efficiency of gas transport and processing. This research illustrates the potential of machine learning techniques in advancing the predictive accuracy for hydrate formations in natural gas pipelines and suggests avenues for future optimizations through hybrid modeling approaches.

Corrections of the Distribution of Nuclei Due to Neutron Degeneracy and Their Effects in the R-Process in Neutron Stars—Black Hole Mergers

The r-process is one of the processes that produces heavy elements in the Universe. One of its possible astrophysical sites is the neutron star–black hole (NS-BH) merger. We first show that the neutrons can degenerate before and during the r-process in these mergers. Previous studies assumed neutrons were non-degenerate and the related rates were calculated under Maxwell–Boltzmann approximations. Hence, we corrected the related rates with neutron degeneracy put in the network code and calculated with the trajectories of NS-BH mergers. We show that there are differences in the nuclei distributions. The heating rates and the temperature at most can be two times larger. The change in heating rates and temperature can affect the light curves of the kilonovae. However, this has little effect on the final abundances.

Lifted Codes with Construction of Echelon-Ferrers for Constant Dimension Codes

Finding the highest possible cardinality, Aq(n,d;k), of the set of k-dimensional subspaces in Fqn, also known as codewords, is a fundamental problem in constant dimension codes (CDCs). CDCs find applications in a number of domains, including distributed storage, cryptography, and random linear network coding. The goal of recent research papers has been to establish Aq(n,d;k). We further improved the echelon-Ferrers construction with an algorithm, and enhanced the inserting construction by swapping specified columns of the generator matrix to obtain new lower bounds.

Impact of Network Coding on Age of Information in Multisource Multihop IoT Networks

Impact of Network Coding on Age of Information in Multisource Multihop IoT Networks

The impact of functional correlations on task information coding

ABSTRACT State-dependent neural correlations can be understood from a neural coding framework. Noise correlations—trial-to-trial or moment-to-moment covariability—can be interpreted only if the underlying signal correlation—similarity of task selectivity between pairs of neural units—is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.

Joint Decoding Technique for Collision Resolution in Non-orthogonal Multiple Access Environment

Multiple access technologies have grown hand in hand from the first generation to the 5th Generation (5G) with both performance and quality improvement. Non-Orthogonal Multiple Access (NOMA) is the recent multiple access technology adopted in the 5G communication technology. Capacity requirements of wireless networks have grown to a large extent with the penetration of ultra-high-definition video transmission, Internet of Things (IoT), and virtual reality applications taking ground in the recent future. This paper develops the Physical Layer Network Coding (PNC) for collision resolution in a NOMA environment with two users. Traditionally NOMA uses Successive Interference Cancellation (SIC) for collision resolution. While additionally a decoding algorithm is added along with SIC to improve the performance of the collision resolution. MATLAB-based simulation is developed on the NOMA environment with two users using Viterbi coding, Low-Density Parity Check (LDPC), and Turbo coding. Performance parameters of Bit Error Rate (BER) and throughput are compared for these three algorithms. It is observed that the Turbo coding performed better among these three algorithms both in the BER and throughput. The BER obtained from the SIC- Turbo is found to be performing well with an increase of about 14% from the ordinary SIC implementation. The performance of the collision resolution has increased by 13% to 14% when joint decoding techniques are used and thus increasing the throughput of the NOMA paradigm.

Improving the security of NOMA cognitive networks

Aiming at the scenario where the eavesdropper try to eavesdrop on the confidential messages of secondary trusted users, two physical-layer network coding (PNC) strategies, namely dual power superposition (PS-PS) encoding strategy and bit-level exclusive-or power superposition (XOR-PS) encoding strategy, are proposed to improve the security performance of cognitive non-orthogonal multiple access (NOMA) networks with the help of the data transmitted by the primary user. Considering the overlay spectrum sharing mechanism and the imperfect successive interference cancellation (SIC) technology, we first derive the closed-form accurate expressions of the outage probability and intercept probability of the secondary network with these two schemes over Rayleigh fading, and also give the asymptotic outage probability in the high signal-to-noise ratio (SNR) regime. Then, the correctness of the theoretical results is verified by simulation. For comparison, we also use the scheme without the assistance of the primary user as the benchmark scheme, which is represented by NPS. Finally, the simulation results show that the security of the proposed PS-PS and XOR-PS schemes is much better than that of the NPS scheme in the case of without reducing the outage performance.

In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays

Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.

On generalized Sidon spaces

Sidon spaces have been introduced by Bachoc, Serra and Zémor as the q-analogue of Sidon sets, classical combinatorial objects introduced by Simon Szidon. In 2018 Roth, Raviv and Tamo introduced the notion of r-Sidon spaces, as an extension of Sidon spaces, which may be seen as the q-analogue of Br-sets, a generalization of classical Sidon sets. Thanks to their work, the interest on Sidon spaces has increased quickly because of their connection with cyclic subspace codes they pointed out. This class of codes turned out to be of interest since they can be used in random linear network coding. In this work we focus on a particular class of them, the one-orbit cyclic subspace codes, through the investigation of some properties of Sidon spaces and r-Sidon spaces, providing some upper and lower bounds on the possible dimension of their r-span and showing explicit constructions in the case in which the upper bound is achieved. Moreover, we provide further constructions of r-Sidon spaces, arising from algebraic and combinatorial objects, and we show examples of Br-sets constructed by means of them.

Modeling the Process of Ultrafiltration to Concentrate Protein Solutions by Neural Network Coding

Modeling the Process of Ultrafiltration to Concentrate Protein Solutions by Neural Network Coding

On Matrix Representation of Extension Field GF(pL) and Its Application in Vector Linear Network Coding.

For a finite field GF(pL) with prime p and L>1, one of the standard representations is L×L matrices over GF(p) so that the arithmetic of GF(pL) can be realized by the arithmetic among these matrices over GF(p). Based on the matrix representation of GF(pL), a conventional linear network coding scheme over GF(pL) can be transformed to an L-dimensional vector LNC scheme over GF(p). Recently, a few real implementations of coding schemes over GF(2L), such as the Reed-Solomon (RS) codes in the ISA-L library and the Cauchy-RS codes in the Longhair library, are built upon the classical result to achieve matrix representation, which focuses more on the structure of every individual matrix but does not shed light on the inherent correlation among matrices which corresponds to different elements. In this paper, we first generalize this classical result from over GF(2L) to over GF(pL) and paraphrase it from the perspective of matrices with different powers to make the inherent correlation among these matrices more transparent. Moreover, motivated by this correlation, we can devise a lookup table to pre-store the matrix representation with a smaller size than the one utilized in current implementations. In addition, this correlation also implies useful theoretical results which can be adopted to further demonstrate the advantages of binary matrix representation in vector LNC. In the following part of this paper, we focus on the study of vector LNC and investigate the applications of matrix representation related to the aspects of random and deterministic vector LNC.

Improving bidirectional controlled remote preparation of arbitrary number of qudits in noisy environment

By elaborately selecting entangled channel, we put forward a universal scheme to achieve bidirectional remote preparation of qudit states with arbitrary numbers of particles under the control of the supervisor. Each sender needs to perform a positive-operator-value measurement and a projective measurement, while the supervisor requires to execute a projective measurement. Based on their measurement outcomes, the receivers can recover the desired states concurrently by carrying out single-qudit generalized Pauli operations which are given by general formulas. A point to highlight is that classical communication cost is greatly reduced at the supervisor’s broadcast channel by taking advantage of network coding. In addition, we analyze the influence of amplitude-damping and phase-damping noises, and utilize weak measurement and environment-assisted measurement to suppress noise.

Neural Network SNR Prediction for Improved Spectral Efficiency in Land Mobile Satellite Networks

The use of satellites to cover remote areas is a promising approach for increasing communication availability and reliability. The satellite resources, however, can be quite costly, and developing ways to optimize their usage is of great interest. Optimizing spectral efficiency while keeping the transmission error rate above a certain threshold represents one of the crucial aspects of resource optimization. This paper provides a novel strategy for adaptive coding and modulation (ACM) employment in land mobile satellite networks. The proposed solution incorporates machine learning techniques to predict channel state information and subsequently increase the overall spectral efficiency of the network. The Digital Video Broadcasting Satellite Second Generation (DVB-S2X) satellite protocol is considered as the use case, and by using the developed channel simulator, this paper performs an evaluation of the proposed machine learning solutions for channels with various characteristics, with a total of 90 different observed channels. The results show that a convolutional neural network with a modified loss function consistently achieves an improvement (over 100% in some scenarios) of spectral efficiency compared to the state-of-the-art ACM implementation while keeping the transmission error rate under 0.01 for single channel evaluation. When observing two channels, an improvement of more than 300% compared to the outdated information spectral efficiency was obtained in multiple scenarios, showing the effectiveness of the proposed approach and allowing optimization of the handover strategy in satellite networks that allow user-centric handover executions.

A Secure Cooperative Image Super-Resolution Transmission with Decode-and-Forward Relaying over Rayleigh Fading Channels

In addition to susceptibility to performance degradation due to hardware malfunctions and environmental influences, wireless image transmission poses risks of information exposure to eavesdroppers. This paper delves into the image communications within wireless relay networks (WRNs) and proposes a secure cooperative relaying (SCR) protocol over Rayleigh fading channels. In this protocol, a source node (referred to as Alice) transmits superior-resolution (SR) images to a destination node (referred to as Bob) with the assistance of a mediating node (referred to as Relay) operating in decode-and-forward mode, all while contending with the presence of an eavesdropper (referred to as Eve). In order to conserve transmission bandwidth, Alice firstly reduces the size of the original SR images before transmitting them to Relay and Bob. Subsequently, random linear network coding (RLNC) is employed by both Alice and Relay on the downscaled poor-resolution (PR) images to obscure the original images from Eve, thereby bolstering the security of the image communications. Simulation results demonstrate that the proposed SCR protocol surpasses both secure relaying transmission without a direct link and secure direct transmission without relaying links. Additionally, a slight reduction in image quality can be achieved by increasing the scaling factor for saving transmission bandwidth. Furthermore, the results highlight the SCR protocol’s superior effectiveness at Bob’s end when compared to Eve’s, which is due to Eve’s lack of access to the RLNC coefficient matrices and reference images utilised by Alice and Relay in the RLNC process. Finally, the evaluation of reference images, relay allocations and diversity reception over Rayleigh fading channels confirms the effectiveness of the SCR protocol for secure image communications in the WRNs.

Operational Technology resilience in the 2023 draft delegated act on cybersecurity for the power sector—An EU policy process analysis

The EU’s 2020 Cybersecurity Strategy promotes cybersecurity as essential for building a resilient, green, and digital Europe. Cleaner energy sources such as wind and solar are more volatile and thus need digital integration with Industrial Control Systems (ICS) for grid balancing. However, the digitization and the properties of cyberspace provide the ability to coordinate disruptive cyberattacks against power grid infrastructures. Digital weapons may be launched against ICS to start multiple cascading outages with a keystroke, causing large-scale blackouts we have never seen before. To reduce risk, the EU’s Strategy describes three objectives for ICS: Secure-by-design, resilient, and timely patched. In the strategy, the European Commission suggests a ”network code,” i.e. a delegated act for the electric power sector, setting rules for cybersecurity in cross-border electricity flows. The draft delegated act of November 2023 presents security requirements for Information and Communication Technology (ICT) and Network and Information Systems (NIS). Although ICS systems are used directly to manage electricity flows, ICS is only mentioned in one of the delegated act’s recitals as a subcategory of ICT products. Suppose Information Technology (IT) rather than Operational Technology (OT) is the focus of the delegated act. In that case, policymakers may not fulfill the EU cybersecurity strategy’s ICS objectives, thus failing to improve the resilience of power grid infrastructures and cross-border electricity flows. This study is a policy process analysis, and its contribution is threefold. First, a literature review is conducted to understand the extent to which the delegated act covers OT. Second, a framework condition analysis is applied to understand why the delegated act lacks OT-specific security requirements. Third, the analysis is extended to understand whether OT is sufficiently covered to achieve the EU strategy’s ICS objectives. In conclusion, our analysis shows a strong intention to include OT-specific security in the preparatory work of the delegated act, but that a stronger position of the IT communities forced OT onto the sideline. Further, the study shows weak fulfillment of general secure-by-design principles and security patch management. These results indicate that OT coverage in the delegated act is not in line with the expectations of the EU’s cybersecurity strategy and the delegated act’s early preparatory work. Therefore, we have suggested three measures to increase OT resilience focus in the act: (a) Define the expressions NIS, ICT services, ICT processes, and ICT in general as umbrella terms that include OT, (b) The foreseen minimum and advanced cybersecurity controls should require OT-specific measures, including holistic secure-by-design principles and patch management covering all patching phases, (c) Develop an OT implementation guide for the delegated act. Our work can be used by policymakers to optimize cybersecurity policy processes and by researchers studying socio-technical gaps in the cybersecurity domain.

Demonstration of teleportation across a quantum network code

Demonstration of teleportation across a quantum network code

On Lattice Network Coding Based Cell-Free MIMO With Uncoordinated Base Stations

On Lattice Network Coding Based Cell-Free MIMO With Uncoordinated Base Stations

SC-PNC: Semantic Communication-Empowered Physical-Layer Network Coding

SC-PNC: Semantic Communication-Empowered Physical-Layer Network Coding

On parallelisms of PG(3,4) with automorphisms of order 2

Let PG(n,q) be the n-dimensional projective space over the finite field Fq. A spread in PG(n,q) is a set of mutually skew lines which partition the point set. A parallelism is a partition of the set of lines by spreads. The classification of parallelisms in small finite projective spaces is of interest for problems from projective geometry, design theory, network coding, error-correcting codes, and cryptography. All parallelisms of PG(3,2) and PG(3,3) are known. Parallelisms of PG(3,4) which are invariant under automorphisms of odd prime orders have also been classified. The present paper contributes to the classification of parallelisms in PG(3,4) with automorphisms of even order. We focus on cyclic groups of order four and the group of order two generated by a Baer involution. We examine invariants of the parallelisms such as the full automorphism group, the type of spreads and questions of duality. The results given in this paper show that the number of parallelisms of PG(3,4) is at least 8675365. Some future directions of research are outlined.