The emergence of sixth generation communication (6G) wireless networks is set to revolutionize vehicular communication by enabling ultra-reliable, low-latency, and high-capacity connectivity in cellular vehicle-to-everything (C-V2X) environments. This paper presents a theoretical framework on novel cooperative vehicular communication and information perception algorithms for large-scale 6G C-V2X networks while leveraging integrated space-air-ground communication system. Specifically, we address key challenges in real-time information exchange and fusion among multiple vehicles. Utilizing inequality theory and functional mapping theory, we derive an upper bound on channel capacity for a fixed number of relays and propose a low-complexity, multi-class relay selection algorithm. Furthermore, we introduce an optimal mobile edge computing (MEC) based correspondence strategy to improve vehicle-to-vehicle communication, alongside an efficient information estimation algorithm to facilitate real-time data sharing. Our simulation results confirm that the proposed algorithms significantly outperform existing cooperative vehicular schemes in terms of channel capacity, while the developed evaluation theory ensures accurate cooperative perception with reduced computational complexity. The proposed framework and theoretical contributions offer a foundational basis for 6G C-V2X networks.

This paper introduces a novel cooperative vehicular communication algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bound on channel capacity given a fixed number of relays, and propose a low-complexity relay selection heuristic algorithm. Simulation results verify that our proposed algorithm achieves superior channel capacities compared to existing cooperative vehicular communication approaches.

The Collective Perception Service (CPS) enables vehicles and infrastructure to cooperatively share local sensor measurements to extend their perception range for a more comprehensive understanding of their environment. Although CPS transmissions are useful, their frequency and redundancy can saturate the vehicular communication channel hindering transmissions of other critical messages. To mitigate this, ETSI has specified generation rules for invoking Collective Perception Messages (CPMs), which are mainly based on objects’ dynamics and frequency of reporting but do not account for the perceptual accuracy of measurements. This can lead to ETSI generation rules potentially being invoked more frequently for inaccurately sensed objects, unnecessarily consuming network resources. Furthermore, inaccurate sensor data is more likely to be matched to the incorrect object track, yielding information that can mislead decision-making. The CAR-2-CAR Communication Consortium (C2C-CC) has proposed an object quality pre-filter to remove poor-quality measurements before triggering the collective perception generation rules, but its complexity has hindered deployment. At the request of ETSI and to hasten deployment, this paper explores the feasibility of proposing a ’simple’ pre-filter that will attempt to discard measurements that would be falsely associated based on a position error measure-based threshold. We find that implementing such a filter is complex and highly dependent on the environmental topology, road user type, geo-spatial density and temporal factors. Specifically, we investigate false associations under different sensor error ranges for diverse road users across 4 urban intersections, detailing their causation. We discuss the challenges in setting a distance measure-based threshold and evaluate its implications as a pre-filter to reduce the number of false and weakly associated objects in a CPM.

While Time-Sensitive Networking (TSN) enhances the determinism, real-time capabilities, and reliability of Eth-ernet, future industrial networks will not only use wired but increasingly wireless communications. Wireless networks enable mobility, have lower costs, and are easier to deploy. However, for many industrial applications, wired connections remain the preferred choice, particularly those requiring strict latency bounds and ultra-reliable data flows, such as for controlling machinery or managing power electronics. The emergence of SG, with its Ultra-Reliable Low-Latency Communication (URLLC) promises to enable high data rates, ultra-low latency, and minimal jitter, presenting a new opportunity for wireless in-dustrial networks. However, as 5G networks include wired links from the base station towards the core network, a combination of 5G with time-sensitive networking is needed to guarantee stringent QoS requirements. In this paper, we evaluate SG-TSN performance for different indoor factory applications and environments through simulations. Our findings demonstrate that 5G- TSN can address latency-sensitive scenarios in indoor factory environments.

While technologies such as Time-Sensitive Networking (TSN) improve deterministic behaviour, real-time functionality, and robustness of Ethernet, future industrial networks aim to be increasingly wireless. While wireless networks facilitate mobility, reduce cost, and simplify deployment, they do not always provide stringent latency constraints and highly dependable data transmission as required by many manufacturing systems. The advent of 5G, with its Ultra-Reliable Low-Latency Communication (URLLC) capabilities, offers potential for wireless industrial networks. 5G offers elevated data throughput, very low latency, and negligible jitter. As 5G networks typically include wired connections from the base station to the core network, integration of 5G with time-sensitive networking is essential to provide rigorous QoS standards. This paper assesses the scalability of 5G-TSN for various indoor factory applications and conditions using OMNET++ simulation. Our research shows that 5G-TSN has the potential to provide bounded delay for latency-sensitive applications in scalable indoor factory settings.

Anytime code is a new class of error-correcting code that has very long coding memory and is decodable with flexible decoding window size (decoding delay). It is designed for real-time reliable applications, such as tracking and controlling unstable systems. In this paper, we construct a family of rate-compatible (RC) Anytime code ensembles with code rate from 0 to 1 based on spatially coupled repeat-accumulate (SC-RA) codes and apply them to the automatic-repeat-request (ARQ) communication with incremental redundancy. Using density evolution (DE) analysis, we prove that each element code within the RC family has superior Anytime-reliable properties over the binary erasure channel (BEC) and the binary-input additive white Gaussian noise (BI-AWGN) channel. In addition, we develop an expanding-window IR-HARQ (incremental redundancy hybrid automatic-repeat-request) scheme for the proposed RC Anytime codes. Simulation results testify that our proposed RC Anytime codes have better decoding performance than the prior-art RC Anytime codes. Furthermore, when combined with the proposed expanding-window IR-HARQ scheme, our proposed RC Anytime codes show significant advantages over the conventional RC low-density parity-check (LDPC) codes and RC polar codes in the conventional CRC-based IR-HARQ communication systems.

Vehicle-to-Vehicle (V2V) communication requires highly reliable wireless links to deliver safety messages effectively. Signal interference or blockages caused by big vehicles like buses and trucks can deteriorate communication quality, causing loss of packets containing safety information transmitted among vehicles. Hence big vehicle shadowing critically influences V2V communication quality. This paper investigates the impact of big vehicle shadowing on V2V communications, considering cellular network coverage of vehicles. Under big vehicle shadowing, we analyse and derive the reuse distance and outage probability of V2V communications. To mitigate the negative of big vehicle shadowing, beamforming-based reception and transmission along with relay approaches are shown. Simulation results show that the proposed approaches significantly improve the performance of V2V communications in the presence of big vehicles.

We are investigating the characteristics of wireless channels in a factory floor environment through simulation and experimentation. We simulate the communication coverage of 5G indoor factory (InF) networks using both, ray tracing and statistical channel models. A ray tracing model requires detailed models of the environment, including their properties and locations, whereas an empirical statistical model does not need such details. Through simulation, we compare the InF wireless channel characteristics obtained from ray tracing and statistical models. Different channel characterization metrics are considered in the comparison, including Reference Signal Received Power (RSRP), channel gain, and SNR. For the purpose of validating the simulation results, we have conducted experiments with a private 5G test-bed on a real-world factory floor. For our use case, we used a factory hall with two 5G radio heads in different positions. Our study demonstrates the viability of the 3GPP InF model, providing more accurate predictions than ray tracing while using less data and significantly quicker simulation run times.

Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.

In our early work, we proposed a class of joint source channel anytime coding (JSCAC) scheme based on spatially coupled repeat-accumulate (SC-RA) codes, in addition to an improved partial joint expanding window decoding (PJEWD) algorithm. In this paper, we extend our preliminary results and make a comprehensive theoretical analysis for this system. First, we propose an improved density evolution (DE) algorithm for the SC-RA based JSCAC with PJEWD scheme. Based on the proposed DE algorithm, we systematically analyze the anytime property, the belief propagation (BP) decoding threshold, and the decoding complexity of this system over binary additive white Gaussian noise channel (BIAWGN), as well as the effects of code parameters on performances of the above three aspects. Moreover, we explore some application scenarios for JSCAC schemes, where it could maximize its advantages over the prior-art JSCC schemes. Both numerical and simulation results demonstrate the potentials of the proposed JSCAC scheme for high reliability and low latency communications. The proposed DE algorithm could also provide a good reference for the analysis of other JSCAC systems.

We are on the cusp of a new era of connected autonomous vehicles with unprecedented user experiences, tremendously improved road safety and air quality, highly diverse transportation environments and use cases, and a plethora of advanced applications. Realizing this grand vision requires a significantly enhanced vehicle-to-everything (V2X) communication network that should be extremely intelligent and capable of concurrently supporting hyperfast, ultrareliable, and low-latency massive information exchange. It is anticipated that the sixth-generation (6G) communication systems will fulfill these requirements of the next-generation V2X. In this article, we outline a series of key enabling technologies from a range of domains, such as new materials, algorithms, and system architectures. Aiming for truly intelligent transportation systems, we envision that machine learning (ML) will play an instrumental role in advanced vehicular communication and networking. To this end, we provide an overview of the recent advances of ML in 6G vehicular networks. To stimulate future research in this area, we discuss the strength, open challenges, maturity, and enhancing areas of these technologies.

Vehicular networks, an enabling technology for Intelligent Transportation System (ITS), smart cities, and autonomous driving, can deliver numerous on-board data services, e.g., road-safety, easy navigation, traffic efficiency, comfort driving, infotainment, etc. Providing satisfactory Quality of Service (QoS) in vehicular networks, however, is a challenging task due to a number of limiting factors such as erroneous and congested wireless channels (due to high mobility or uncoordinated channel-access), increasingly fragmented and congested spectrum, hardware imperfections, and anticipated growth of vehicular communication devices. Therefore, it will be critical to allocate and utilize the available wireless network resources in an ultra-efficient manner. In this paper, we present a comprehensive survey on resource allocation schemes for the two dominant vehicular network technologies, e.g. Dedicated Short Range Communications (DSRC) and cellular based vehicular networks. We discuss the challenges and opportunities for resource allocations in modern vehicular networks and outline a number of promising future research directions.

Visible light communication (VLC) has recently emerged as an affordable and scalable technology supporting very high data rates for short range vehicle-to-vehicle (V2V) communication. In this work, we advocate the use of vehicular-VLC (V-VLC) for basic safety messages (BSMs) dissemination in lieu of conventional vehicular radio frequency (V-RF) communication in road intersection applications, where the reception performance is affected by interference from the concurrent transmissions of other vehicles. We make use of stochastic geometry to characterize the interference from the same lane as well as the perpendicular lane for various network configurations, i.e., standalone V-VLC, stand-alone V-RF and hybrid V-VLC/V-RF network. Specifically, by modelling the interfering vehicles’ locations as a spatial Poisson point process (PPP), we are able to capture a static two-dimensional road geometry as well as the impact of interference due to vehicles clustering in the vicinity of road intersection in terms of outage probability and throughput. In addition to above, the performance of spatial ALOHA and carrier sense multiple access with collision avoidance medium access control (CSMA/CA MAC) protocol for standalone V-VLC, standalone V-RF and hybrid V-VLC/V-RF network configuration for relaying BSMs at road intersection is also compared. The performance metrics such as delay outage rate (DOR) and information outage rate (IOR) are utilized to investigate the impact of latency associated with various network configurations. Our numerical results reveal that our proposed hybrid V-VLC/V-RF leads to significant improvement in terms of outage performance, throughput and latency as compared to stand-alone V-VLC or stand-alone V-RF network.

Joint source channel coding (JSCC) is an effective technique in the non-asymptotic and low latency regime, while suffers from high error floor for sequences with high source probabilities and short block-lengths (HSP-SB). Aiming to address this issue, a joint source channel anytime coding (JSCAC) based on the anytime spatially coupled repeat-accumulate (ASC-RA) codes is presented. In the proposed JSCAC scheme, the adopted exponential distributed coupling (EDC) and partial joint expanding window decoding (PJEWD) can efficiently recover the early transmitted HSP-SB messages that are not fully corrected. Meanwhile, the updating mechanisms in the proposed PJEWD mitigate the complexity of expanding window decoding and the error propagation between the source and channel decoders, attributing to a better error performance. The proposed JSCAC is suitable for HSP-SB source transmission, which is a competitive candidate for communications with high reliability and low delay demands, such as streaming source and control applications, etc.

DNA code design aims to generate a set of DNA sequences (codewords) with minimum likelihood of undesired hybridizations among sequences and their reverse-complement (RC) pairs (cross-hybridization). Inspired by the distinct hybridization affinities (or stabilities) of perfect double helix constructed by individual single-stranded DNA (ssDNA) and its RC pair, we propose a novel similarity significance (SS) model to measure the similarity between DNA sequences. Particularly, instead of directly measuring the similarity of two sequences by any metric/approach, the proposed SS works in a way to evaluate how more likely will the undesirable hybridizations occur over the desirable hybridizations in the presence of the two measured sequences and their RC pairs. With this SS model, we construct thermodynamically stable DNA codes subject to several combinatorial constraints using a sorting-based algorithm. The proposed scheme results in DNA codes with larger code sizes and wider free energy gaps (hence better cross-hybridization performance) compared to the existing methods.

DNA has become an attractive medium for long-term data archiving due to its extremely high storage density and longevity. Short single-stranded DNAs, called oligonucleotides (oligos), have been designed and synthesized to store digital data. Previous works designed the oligos with a pair of primer binding sites (PBSs) (each with a length of around 200) attached at the two ends of each basic readable data block. The addition of PBSs decreases the data density significantly because in the current DNA synthesis, the maximum length of a synthesized oligo in good quality is around 200. Furthermore, the maximum homopolymer run allowed by the existing experiments has been reported to be three nucleotides. In this work, to increase the data density, we have devised and tested an oligo design for DNA-based storage with the basic readable data block appended by a single PBS at one end only, while allowing the maximum homopolymer run to be increased to 4. We also present an oligo assembly algorithm that can reconstruct oligos with a single PBS from the error-prone raw readouts obtained from the sequencing process. We have conducted a wet lab experiment to validate the proposed design, where we tested with 398KB of data stored into 10,750 oligos. The experimental results show that it is possible to recover over 99 percent of the oligo sequences without error, which proves that one PBS is sufficient for implementing a DNA-based data storage system with maximum homopolymer run relaxed to 4. The use of single PBS leads to a significant data density gain from 14.3 to 140.2 percent over the existing short-strand DNA data storage schemes by reserving more nucleotides for storing information bits.

Multisensor fusion strategies have been widely applied in human activity recognition (HAR) in body sensor networks (BSNs). However, the sensory data collected by BSNs systems are often uncertain or even incomplete. Thus, designing a robust and intelligent sensor fusion strategy is necessary for high-quality activity recognition. In this article, Dezert-Smarandache theory (DSmT) is used to develop a novel sensor fusion strategy for HAR in BSNs, which can effectively improve the accuracy of recognition. Specifically, in the training stage, the kernel density estimation (KDE)-based models are first built and then precisely selected for each specific activity according to the proposed discriminative functions. After that, a structure of basic belief assignment (BBA) can be constructed, using the relationship between the test data of unknown class and the selected KDE models of all considered types of activities. In order to deal with the conflict between the obtained BBAs, proportional conflict redistribution-6 (PCR6) is applied to fuse the acquired BBAs. Moreover, the missing data of the involved sensors are addressed as ignorance in the framework of the DSmT without manual interpolation or intervention. Experimental studies on two real-world activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) are conducted, and the results shows the superiority of our proposed method over some state-of-the-art approaches proposed in the literature.

This paper studies the impact of propagation path losses in urban/sub-urban vehicular networks. The impact has been analyzed considering both the network-layer and application-layer reliabilities. We have built a realistic vehicular simulation environment and performed an extensive simulation experiment in a semi-urban traffic environment. The results show that there are significant performance differences between LOS (Line-of-Sight)/OLOS (Obstructed-LOS)/NLOS (Non-LOS) path loss model and any other studied loss model. With a moderate communication distance between a transmitter and a receiver (300 m), while with the other existing loss models the network-level reliability is around 60\%, with the LOS/OLOS/NLOS model that falls below 30\%. Moreover, the application-level reliability of LOS/OLOS/NLOS model is not more than 55\% for the delay sensitive safety application.

This study investigates the performance of a bidirectional/two‐way relaying scheme with an energy‐harvesting relay while amplify‐and‐forward relaying is performed. With multiple antennas at the relay, the authors propose the use of the max–min antenna selection scheme in the context of an energy harvesting scenario. They show that the proposed scheme attains diversity gain, whereby its end‐to‐end symbol error probability performance improves with increments in the number of energy harvesting relay antennas. For limited battery capacity at the energy harvesting node, they derive the lower bound of the outage probabilities and observe a good match between the analytical and simulated results. For this scenario, a simple power provisioning technique is proposed to optimise the end nodes' transmission powers. It is observed that the proposed technique saves significantly more transmission energy than the conventional equal power provisioning technique.

Real-time traffic updates, safety and comfort driving, infotainment, and so on, are some envisioned applications in vehicular networks. Unlike traditional broadcast, network-coding-assisted broadcast can satisfy multiple vehicles with different data items in a coded form. However, server side encoding requires the prior knowledge about vehicles’ cache information for the successful decoding at the vehicles’ sides. The explicit cache upload from vehicles to Road Side Unit (RSU) wastes upload bandwidth. In multi-RSU vehicular networks, we propose a Cooperative Cache Transfer-based On-demand Network Coded Broadcast called CCTCB. In the proposed CCTCB approach, vehicles do not need to upload their cache information to the server, rather the RSU server learns the vehicles’ cache intrinsically. We derive a probabilistic model to analyze the coding opportunity in the proposed cooperative cache transfer mechanism incorporating vehicle mobility. The comprehensive simulation results validate the superiority of the proposed approach.

Unmanned aerial vehicles (UAVs) have attracted a lot of attention due to their enormous potentiality in civil and military applications over the past years. In order to allow accurate control action of UAV, a robust and real-time state estimation technique is required. In this paper, we propose a Kalman filter based UAV state estimation technique when the communication takes place over wireless links in an Internet of Things (IoT) network. We consider that a set of sensors observes the state of the UAV and transmits the observation to a control center (central server) over a distributed wireless IoT network. To deal with the communication impairments due to wireless communication links between the UAV's sensors and the IoT system components, e.g., IoT gateways, a Bose-Chaudhuri-Hocquenghem coded communication system is presented. Based on the received signals at the IoT gateways, a global state estimation technique is proposed. Performance of the proposed communication and estimation scheme is demonstrated through numerical results for different conditions. From the comparison with a conventional estimation scheme, it is observed that the proposed scheme significantly outperforms the conventional scheme in terms of state estimation and error performance.

Recently, much attentions have been paid to network coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this work, we have studied the network coding-assisted heterogeneous on-demand data access in vehicular networks. Firstly, we have investigated the less efficiency of conventional coding assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Secondly, based on our observations, we have proposed a new algorithm. The proposed network coding assisted approach exploits the different MCSs (Modulation and coding schemes) of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions' of vehicles along with the vehicle mobility. Thirdly, we have built a vehicular simulation environment and evaluated the performance of the proposed approach along with competitive no-coding and coding-assisted approaches under various circumstances. The results show that the proposed approach outperforms the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the data access time.

The low‐density parity‐check (LDPC) code design for three‐terminal (namely, source, relay, and destination) relay network while considering decode‐and‐forward protocol is studied. Numerous works have been done on LDPC relay code design with parity bits forwarding approach, where additional parity bits are generated at the relay and forwarded to the destination. Most of the previous works assume that the forwarded parity bits are received perfectly at the destination and hence ignore the impact of relay–destination channel. This assumption is unrealistic in practical/finite‐length codeword scenario. In this study, a protograph based LDPC code design is proposed while lifting the above unrealistic assumption. A Gaussian approximated density evolution is presented for the proposed scheme, which considers that the overall codeword experiences source–destination and relay–destination channels. For various designed codes with different rates, the authors show that the asymptotic thresholds of the designed codes are very close to the corresponding capacity bounds. Asymptotically, our designed code gives similar decoding threshold compared to the existing optimised irregular LDPC codes, while protograph codes are simpler to optimise and encode than the irregular LDPC codes. More importantly, proposed code performs better than the existing LDPC‐based relay codes for finite‐length scenarios.

Through the embedded processors and communication technologies, vehicles are increasingly being connected with the Internet of Things. Recently, much attentions have been paid to network-coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this paper, we have studied the network coding-assisted heterogeneous on-demand real-time data access in vehicular networks. First, we have investigated the less efficiency of conventional coding assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Second, based on our observations, for efficiently serving heterogeneous data items, we have proposed a dynamic threshold-based coding-assisted real-time data broadcast approach called earliest deadline firstΘ. Third, we have derived the probabilistic analysis of the system performance of the proposed approach and the state-of-the-art approaches. Fourth, based on our further investigations, we have proposed another approach, called inverse of slack time multiply distance with THRESHOLD (Θ) (ISXDΘ). The proposed network coding-assisted ISXDΘexploits the different modulation and coding scheme of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions of vehicles along with the vehicle mobility. The comprehensive simulation results demonstrate the efficacy of the proposed approaches over the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the system response time.

Anytime codes are known to be useful for tracking and controlling unstable systems over noisy channels. Although practical anytime codes have been developed for point-to-point communication, to date no practical code constructions and tractable analysis of anytime codes are known to exist for relay networks. In this paper, we study the design and theoretical analysis of anytime codes for a three-terminal relay network. We propose bilayer codes for anytime transmission over the relay network and consider two transmission schemes for the relay-destination link. For both schemes, we analytically derive the delay-exponents and show that they closely match the corresponding numerical results obtained from density evolution. Through simulation, we also show the finite-length performance of the proposed bilayer anytime code.

In this paper, we propose a design of spatially coupled repeat-accumulate (SC-RA) codes over a three-phase bidirectional relay. The channels between the nodes are assumed binary input additive white Gaussian noise (BIAWGN) channels, while decode-and-forward (DF) relaying is considered at the relay. We present low complexity density evolution analysis for the proposed bidirectional relay code, which leads to a simple code design procedure. The performance of the proposed relay coding scheme significantly outperforms the performance of the relay code based on regular RA block code. We show that the decoding threshold of the proposed relay code approaches the theoretical limit for a wide range of design rates. We also show the finite-length performance of the proposed bidirectional relay code through simulation results.

This paper presents the design and analysis of spatially coupled low‐density parity‐check (SC‐LDPC) codes for Gaussian multiple access channel (GMAC). For two‐user GMAC, we consider that each user encodes their messages by SC‐LDPC codes while joint belief propagation decoding is considered at the receiver. We present low complexity density evolution analysis of SC‐LDPC GMAC codes and show simple optimisation procedure. For the asymptotic case, we observe that SC‐LDPC code‐based GMAC code outperforms existing irregular LDPC code‐based GMAC code.

Anytime transmission has been shown to be an effective means of stabilizing an unstable system over noisy channels. A crucial issue in implementing anytime transmission strategies is the design of anytime codes that offer good finite-length performance and asymptotic anytime properties. This paper introduces an efficient anytime code derived from protograph-based low-density parity-check convolutional codes known as extended S-LDPCC (XS-LDPCC) codes. The asymptotic anytime properties of XS-LDPCC codes over both the binary erasure channel (BEC) and the binary-input additive white Gaussian noise (BIAWGN) channel are demonstrated through density evolution. By means of certain observations verified by simulations, we provide accurate estimates of the delay exponents of XS-LDPCC codes over the BEC and the BIAWGN channel. Through simulations, the finite-length performance of XS-LDPCC codes and the effects of different code design parameters are quantified.

This paper studies the bilayer codes for anytime reliable transmission over three terminal relay networks. We propose an anytime code structure and provide a theoretical analysis of bilayer anytime transmission when uncoded relaying takes place over the relay-destination channel. Through asymptotic analysis, we show the anytime characteristics of the proposed relaying scheme. We find the theoretical delay exponent through the mathematical induction technique and compare it with the delay exponent obtained from numerical results. Through simulation results, we also show the finite-length performance of the proposed bilayer anytime code.

In this paper, an efficient anytime code derived from protograph-based low-density parity-check convolutional (P-LDPCC) code is proposed. Density evolution technique is applied to demonstrate the anytime properties of the P-LDPCC code over the binary erasure channel (BEC). Through simulation, the finite-length performance is evaluated and the effects of code design parameters are observed. The delay exponent is evaluated using the density evolution functions and simulations of finite-length codes. The simulation results show that P-LDPCC code outperforms existing LDPCC anytime codes.

In this paper, the authors study the performance of finite‐length repeat–accumulate (RA) codes in the waterfall region. Utilising density evolution technique, the authors present estimation algorithms to predict the error performance of finite‐length RA codes over binary memoryless channels. Through comparison with numerical results, the authors show that the finite‐length performance of RA codes can be closely estimated by using authors’ proposed approach.

Anytime transmission was previously shown to be necessary and sufficient for tracking and controlling an unstable plant over a noisy channel. In this paper, we propose an efficient channel coding scheme for anytime transmission. We design this coding scheme based on spatially coupled codes and investigate its performance over binary erasure channels (BEC) and binary input additive white Gaussian noise (BIAWGN) channels. Through density evolution analysis, we asymptotically show the desired anytime properties of the proposed code. For the BEC, we derive the corresponding delay-exponent and numerically predict the finite-length performance of the proposed anytime code. We also propose adaptive window decoding techniques to ensure low complexity anytime receivers.

In this paper, we study the design and the delay-exponent of anytime codes over a three terminal relay network. We propose a bilayer anytime code based on anytime spatially coupled low-density parity-check (LDPC) codes and investigate the anytime characteristics through density evolution analysis. By using mathematical induction technique, we find analytical expressions of the delay-exponent for the proposed code. Through comparison, we show that the analytical delay-exponent has a close match with the delay-exponent obtained from numerical results.

In this paper, we study the performance of finite-length LDPC codes in the waterfall region. We propose an algorithm to predict the error performance of finite-length LDPC codes over various binary memoryless channels. Through numerical results, we find that our technique gives better performance prediction compared to existing techniques.