Probability Of Successful Decoding Research Articles

Throughput and delay analysis of cognitive M2M communications

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

Exploiting data compression to improve reliability of phase-modulated holographic data storage.

Due to the interference of complex noise in holographic channels and the limitation of phase retrieve algorithms, the reliability of phase-modulated holographic data storage (PHDS) is seriously threatened, especially for multi-level phase modulation. A method for improving data reliability of PHDS is proposed by applying lossless data compression and low-density parity-check (LDPC) codes, which can eliminate data redundancy and correct errors effectively. We allocate the space saved by compression to store more LDPC parity bits and develop a method to determine the LDPC code rate and a method to manage the free space. Our method does not require the characteristics of the reconstructed phase distribution, which simplifies the statistical analysis and calculation. Simulation and experimental results demonstrate that our method greatly decreases the bit error rate (BER) and decoding iterations, and boosts the decoding success probability. For instance, when the phase error rate is 0.029 and the compression rate is 0.6, our method reduces the BER by 87.8%, the decoding iterations by 84.3%, and improves the decoding success probability by 93%. Our method enhances both data reliability and storage efficiency in PHDS.

Fast phase error correction with reference beam-assisted LDPC coding for collinear holographic data storage.

Low-density parity-check (LDPC) coding is a significant technique for ensuring data reliability in phase-modulated holographic data storage. To accelerate LDPC decoding, we design reference beam-assisted LDPC coding for 4-level phase-modulated holography. The reliability of a reference bit is higher than that of an information bit during decoding because reference data are known during recording and reading processes. By considering the reference data as prior information, the weight of the initial decoding information (i.e., log-likelihood ratio (LLR) information) of the reference bit is increased during LDPC decoding. The performance of the proposed method is evaluated through simulations and experiments. In the simulation, compared with the conventional LDPC code with a phase error rate (PER) of 0.019, the proposed method can reduce bit error rate (BER) by 38.8%, uncorrectable bit error rate (UBER) by 24.9%, decoding iteration time by 29.9%, the number of decoding iterations by 14.8%, and improve decoding success probability by 38.4% approximately. Experimental results demonstrate the superiority of the proposed reference beam-assisted LDPC coding. The developed method can significantly decrease the PER, BER, the number of decoding iterations, and decoding time by using the real captured images.

A Homomorphism Transform Based Analysis of Sparse Random Linear Network Coding Over Erasure Channels

In this letter, we provide a method for deriving the first exact expression for the decoding success probability of sparse random linear network coding over erasure channels. The key idea of the proposed method is to transform the characterization of the full rank probability of a sparse random matrix into the characterization of the homomorphism transform of the sparse distribution and to obtain a closed-form exact expression for the homomorphism transform of the sparse distribution. The former is solved by employing Möbius inversion theorem and the property of the homomorphism transform, and the latter is solved by employing the feature of the sparse distribution and the property of the nontrivial character of a finite field.

Multicell Grant-Free Uplink IoT Networks With Hard Deadline Services in URLLC

We consider a multi-cell grant-free uplink Internet of Things (IoT) network where multiple types of ultra-reliable and low-latency communications (URLLC) services with hard deadlines coexist. A mathematical framework is proposed for characterizing the packet loss rate (PLR) of each service given the network topology. Particularly, we derive the collision probability of resource block (RB) selection and a lower bound of the successful decoding probability. In addition, we formulate and solve the RB consumption minimization problem subject to the PLR constraints and an efficient algorithm is proposed. Numerical results show that the gap between the lower bound and the simulations is quite small in the ultra reliable cases, and the proposed algorithm outperforms the existing benchmark scheme.

Energy-Saving Deployment Optimization and Resource Management for UAV-Assisted Wireless Sensor Networks With NOMA

Energy-saving techniques are vital for the battery-powered sensor devices (SDs), which affect their lifetime. In this paper, we propose an air-and-ground cooperative wireless sensor network (AGWSN), wherein several UAVs are deployed as aerial access points (AAPs) to assist the terrestrial access point (TAP) for data collecting. The positions of the AAPs can be modified to approach the cell-edge SDs, therefore reducing the energy of the SDs expended in uploading data. To fully exploit the potential of the AGWSN, we formulate a joint AAP position optimization, channel allocation, and power control problem to minimize the total power consumption of all SDs subject to their decoding threshold. To solve the formulated problem, we first analyze the optimal user pairing rule in each cell and based on the rule propose a maximum-weighted-independent-set inspired algorithm for the AAP position optimization. Then, we remodel the channel allocation problem as an interference minimization problem and devise a K-CUT based algorithm to solve it. We further propose a low-complex iterative algorithm to obtain the optimal transmission power for each SD. The performance of the proposed algorithms is evaluated via theoretical analysis and numerical simulation. Simulation results indicate that if the intracell and intercell interference are not well coordinated, the superiorities of the AGWSN cannot be developed, and its performance is even worse than the traditional terrestrial network (TTN). Cooperated with our algorithms, the AGWSN significantly outperforms the TTN in terms of total power consumption and probability of successful decoding.

Memory-Based LT Codes for Efficient 5G Networks and Beyond

The next-generation networks (5G and beyond) require robust channel codes to support their high specifications, such as low latency, low complexity, significant coding gain, and flexibility. In this paper, we propose using a fountain code as a promising solution to 5G and 6G networks, and then we propose using a modified version of the fountain codes (Luby transform codes) over a network topology (Y-network) that is relevant in the context of the 5G networks. In such a network, the user can be connected to two different cells at the same time. In addition, the paper presents the necessary techniques for analyzing the system and shows that the proposed scheme enhances the system performance in terms of decoding success probability, error probability, and code rate (or overhead). Furthermore, the analyses in this paper allow us to quantify the trade-off between overhead, on the one hand, and the decoding success probability and error probability, on the other hand. Finally, based on the analytical approach and numerical results, our simulation results demonstrate that the proposed scheme achieves better performance than the regular LT codes and the other schemes in the literature.

On the Self-Decoding of Polar-Coded HARQ

In this letter, we concern the self-decoding capability of polar-coded hybrid automatic repeat request (Polar-HARQ) schemes. The proposed self-decoding method enables the incremental redundancy (IR) based Polar-HARQ to recover all source bits solely using the retransmission codewords. It provides an extra decoding chance when the joint decoding attempt fails. With this, the final decoding success probability increases such that the number of retransmissions can be reduced and the system latency goes down. We derive one necessary condition for the successful self-decoding, which can also guide the optimization of Polar-HARQ. Three mainstream Polar-HARQ schemes are compared from the perspective of self-decoding. Simulation results validate the analysis.

Secure dimensionality reduction fusion estimation against eavesdroppers in cyber–physical systems

This paper studies the distributed dimensionality reduction fusion estimation problem for cyber-physical systems with limited bandwidth in presence of eavesdroppers. Since wireless communication is implemented by broadcasting, the eavesdroppers can collude to collect the data through anther communication networks. To protect data privacy, based on the physical processes and local estimation error covariance (EEC) matrix, an insertion method of artificial noise (AN) is developed such that only eavesdroppers’ fusion EEC becomes worse. Meanwhile, the fusion center needs to decode the received signal due to the noise interference, while the successful decoding probability varies with signal to noise ratio. Subsequently, some criteria for the selection probabilities and the successful decoding probabilities are given to guarantee the effectiveness of the AN insertion strategy. Moreover, a sufficient condition of the designed AN power is derived to guarantee the confidentiality. Simulation examples are given to show the effectiveness of the proposed methods.

Inter-Frame Polar Coding With Dynamic Frozen Bits

A new inter-frame correlated polar coding scheme is proposed, where two consecutive frames are correlated-encoded and assist each other during decoding. The correlation is achieved by dynamic configuration of the frozen bits. The frozen bits of the second frame partially depend on the unfrozen bits of the first frame in encoding and the number of bits that are viewed as frozen by decoder is alterable in different decoding modes. Using this new encoding/decoding scheme, a failed decoded frame can be decoded again with extra information which corrects the errors in its highly unreliable unfrozen bits. Thus, the probability of successful decoding is improved. Simulation results show that the performance of the proposed polar codes outperforms that of the classical counterpart significantly with negligible memory and complexity increment.

Cache-Aided Non-Orthogonal Multiple Access for 5G-Enabled Vehicular Networks

The increasing demand for rich multimedia services and the emergence of the Internet of Things (IoT) pose challenging requirements for the next-generation vehicular networks. Such challenges are largely related to high spectral efficiency and low latency requirements in the context of massive content delivery and increased connectivity. In this respect, caching and non-orthogonal multiple access (NOMA) paradigms have been recently proposed as potential solutions to effectively address some of these key challenges. In this paper, we introduce cache-aided NOMA as an enabling technology for vehicular networks. In this context, we first consider the full file caching case, where each vehicle caches and requests entire files using the NOMA principle. Without loss of generality, we consider a two-user vehicular network communication scenario under double Nakagami $\text{-}m$ fading conditions and propose an optimum power allocation policy. To this end, an optimization problem that maximizes the overall probability of successful decoding of files at each vehicle is formulated and solved. Furthermore, we consider the case of split file caching, where each file is divided into two parts. A joint power allocation optimization problem is formulated, where power allocation across vehicles and cached split files is investigated. The offered analytic results are corroborated by extensive results from computer simulations and interesting insights are developed. Indicatively, it is shown that the proposed caching-aided NOMA outperforms the conventional NOMA technique.

A Probabilistic Peeling Decoder to Efficiently Analyze Generalized LDPC Codes Over the BEC

In this paper, we analyze the tradeoff between coding rate and asymptotic performance of a class of generalized low-density parity-check (GLDPC) codes constructed by including a certain fraction of generalized constraint (GC) nodes in the graph. The rate of the GLDPC ensemble is bounded using classical results on linear block codes, namely Hamming bound and Varshamov bound. We also study the impact of the decoding method used at GC nodes. To incorporate both bounded-distance (BD) and Maximum Likelihood (ML) decoding at GC nodes into our analysis without resorting on multi-edge type of degree distributions (DDs), we propose the probabilistic peeling decoding (P-PD) algorithm, which models the decoding step at every GC node as an instance of a Bernoulli random variable with a successful decoding probability that depends on both the GC block code as well as its decoding algorithm. The P-PD asymptotic performance over the BEC can be efficiently predicted using standard techniques for LDPC codes such as density evolution (DE) or the differential equation method. Furthermore, for a class of GLDPC ensembles, we demonstrate that the simulated P-PD performance accurately predicts the actual performance of the GLPDC code under ML decoding at GC nodes. We illustrate our analysis for GLDPC code ensembles with regular and irregular DDs. In all cases, we show that a large fraction of GC nodes is required to reduce the original gap to capacity, but the optimal fraction is strictly smaller than one. We then consider techniques to further reduce the gap to capacity by means of random puncturing, and the inclusion of a certain fraction of generalized variable nodes in the graph.

Network Coverage in Interference Limited Wireless Sensor Networks

The performance of a wireless sensor network is limited by the available bandwidth of the shared wireless channel. For the need to share the available bandwidth sensors contend for concurrent transmissions. The simultaneous transmissions by the neighbouring nodes are the main source of interference at the intended receiver sensor. In this paper, we derive the successful decoding probability of receiver sensor in presence of interference and Rayleigh fading channel. We introduce the concept of circular influence zone, to impose a restriction on the aggregated interference from the distant neighbours at an intended receiver sensor. We provide an insight about the critical sensor density which yields an optimal trade-off between the achievable coverage and receiver decoding probability in presence of co-channel interference.

Reliability-Intercept Gap Analysis of Underlay Cognitive Networks Under Artificial Noise and Primary Interference

Underlay mode of cognitive radio networks (CRNs) permits secondary users (SUs) to simultaneously operate with primary users (PUs), inducing mutual interference between them. Nevertheless, primary interference (interference from PUs to SUs) is either neglected or treated as Gaussian noise in existing works. Moreover, artificial noise, which is deliberately introduced to harm signal reception of wire-tappers, can enhance information securing capability of CRNs. This paper analyzes a gap between reliability probability (successful decoding probability of legitimate receiver) and intercept probability (successful decoding probability of wire-tappers), which accounts for artificial noise, peak transmit power constraint, interference power constraint, and fading channels, and considers primary interference as non-Gaussian noise. Towards this end, exact closed-form expressions of reliability probability and intercept probability are first derived and then validated by computer simulations. Numerous results illustrate that both probabilities are saturated at either large peak interference power or large peak transmit power, the primary interference dramatically deteriorates them while the artificial noise creates a large gap between them, demonstrating its effectiveness in securing legitimate information.

Efficient Cellular Network with Network Coding in Disaster Area

Intermittent connectivity due to the difficult topography in disaster area prevents a direct transmission from the base station to the demand users. An analytical framework to evaluate the reliability performance of the cellular network has been proposed. This project aims to evaluate the communication efficiency over the cellular network with the implementation of random linear network coding (RLNC) at the source node before transmission. The RLNC scheme employed in a new bounds algorithm provides a better estimation of the reliability performance of the communication over the cellular network. Thus, the decoding success probability in the communication, with and without the implementation of RLNC at the source node is developed. The implementation of the RLNC at the source node before transmission can increase the performance of the communication reliability over the cellular network from 2 to 82% under a high noise interference environment, d0 = 0.7 for M = 4, without requiring a large value of relay nodes at cluster 2. Thus, as the number of relay nodes at cluster 2 increases (M > N), the reliability of data exchange between the source and the destination node increases.

Delay-Optimized File Retrieval under LT-Based Cloud Storage

Fountain-code based cloud storage system provides reliable online storage solution through placing unlabeled content blocks into multiple storage nodes. Luby Transform (LT) code is one of the popular fountain codes for storage systems due to its efficient recovery. However, to ensure high success decoding of fountain codes based storage, retrieval of additional fragments is required, and this requirement could introduce additional delay. In this paper, we show that multiple stage retrieval of fragments is effective to reduce the file-retrieval delay. We first develop a delay model for various multiple stage retrieval schemes applicable to our considered system. With the developed model, we study optimal retrieval schemes given requirements on success decodability. Our numerical results suggest a fundamental tradeoff between the file-retrieval delay and the target probability of successful file decoding, and that the file-retrieval delay can be significantly reduced by optimally scheduling packet requests in a multi-stage fashion.

Error Performance Analysis of Random Network Coded Cooperation Systems

This paper presents a framework for computing successful decoding probability of random network coding (RNC) in wireless networks. As cooperation emerges due to the naturally occurring broadcasting in wireless links, the application of RNC in wireless networks enables random network coded cooperation (RNCC). The theoretical successful decoding probability of RNCC systems is derived by obtaining the ratio of the full rank and the rank deficient matrices. The full rank condition of the global encoding matrix indicates the successful decoding of source symbols. The results of a single relay along with a relay selection scheme are also investigated. The validity of the presented theoretical expressions is demonstrated through identical simulation results. An implementation scenario is also presented to demonstrate the practical usage effectiveness of RNC in real-time applications, by using software-defined radio nodes.

Generation of Luby Transform Codes with Low Redundancy

Given the same number of encoded symbols, a Luby Transform (LT) decoder is more likely to decode successfully when there is little redundancy among the symbols. With the use of a Tanner graph, we describe two kinds of redundancy appearing in LT codes in this paper. We further propose an encoding algorithm called Low Redundancy (LR) algorithm used in the formation of the encoded symbols. The algorithm aims to reduce the redundancy of LT codes and to improve the decoder performance under the same complexity. Simulation results show various LT codes improve in terms of average overhead factor and probability of successful decoding when the codes are encoded using the proposed LR algorithm. Moreover, the encoding complexity remains unchanged.

SCALE-FREE LUBY TRANSFORM CODES

This paper reports the characteristics and performance of a new type of Luby Transform codes, namely scale-free Luby Transform (SF-LT) codes. In the SF-LT codes, the degree of the encoded symbol follows a modified power-law distribution. Moreover, the complexity and decoding performance of SF-LT codes are compared with LT codes based on robust soliton degree distribution and LT codes based on suboptimal degree distribution. The results show that SF-LT codes outperform other LT codes in terms of the probability of successful decoding over an ideal channel and a binary erasure channel. Moreover, the encoding/decoding complexity for the SF-LT codes is superior.

Information Fusion-Based Storage and Retrieve Algorithms for WSNs in Disaster Scenarios

Sensor networks are especially useful in catastrophic or disaster scenarios such as abysmal sea, floods, fires, or earthquakes where human participation may be too dangerous. Storage technologies take a critical position for WSNs in such scenarios since the sensor nodes may themselves fail unpredictably, resulting in the loss of valuable data. This paper focuses on fountain code-based data storage and recovery solutions for WSNs in disaster scenarios. A review on current technologies is given on challenges posed by disaster environments. Two information fusion-based distributed storage (IFDS) algorithms are proposed in the “few global knowledge” and “zero-configuration” paradigm, respectively. Correspondingly, a high-efficient retrieve algorithm is designed for general storage algorithms using Robust Soliton distribution. We observe that the successful decoding probability can be provisioned by properly selecting parameters—the ratio of number of source node and total nodes, and the storage capacity M in each node.