Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hinder real-world deployment: (1) noisy or degraded sensory inputs from weather, motion blur, or hardware limitations, and (2) the need to issue timely yet reliable predictions that balance early alerts with false-alarm suppression. We propose a unified framework that integrates diffusion-based denoising with a time-aware actor-critic model to address these challenges. The diffusion module reconstructs noise-resilient image and object features through iterative refinement, preserving critical motion and interaction cues under sensor degradation. In parallel, the actor-critic architecture leverages long-horizon temporal reasoning and time-weighted rewards to determine the optimal moment to raise an alert, aligning early detection with reliability. Experiments on three benchmark datasets (DAD, CCD, A3D) demonstrate state-of-the-art accuracy and significant gains in mean time-to-accident, while maintaining robust performance under Gaussian and impulse noise. Qualitative analyses further show that our model produces earlier, more stable, and human-aligned predictions in both routine and highly complex traffic scenarios, highlighting its potential for real-world, safety-critical deployment.

Long-tail motion forecasting is a core challenge for autonomous driving, where rare yet safety-critical events-such as abrupt maneuvers and dense multi-agent interactions-dominate real-world risk. Existing approaches struggle in these scenarios because they rely on either non-interpretable clustering or model-dependent error heuristics, providing neither a differentiable notion of “tailness” nor a mechanism for rapid adaptation. We propose SAML, a Semantic-Aware Meta-Learning framework that introduces the first differentiable definition of tailness for motion forecasting. SAML quantifies motion rarity via semantically meaningful intrinsic (kinematic, geometric, temporal) and interactive (local and global risk) properties, which are fused by a Bayesian Tail Perceiver into a continuous, uncertainty-aware Tail Index. This Tail Index drives a meta-memory adaptation module that couples a dynamic prototype memory with an MAML-based cognitive set mechanism, enabling fast adaptation to rare or evolving patterns. Experiments on nuScenes, NGSIM, and HighD show that SAML achieves state-of-the-art overall accuracy and substantial gains on top 1-5% worst-case events, while maintaining high efficiency. Our findings highlight semantic meta-learning as a pathway toward robust and safety-critical motion forecasting.

This paper presents a comprehensive power and thermal integrity analysis of a commercial IP based 7nm 3D CPU with a much larger SRAM area compared to its logic section. We systematically investigate the impact of different 3D stacking architectures—Memory-on-Logic (MoL) and Logic-on-Memory (LoM)—combined with both front-side and back-side power delivery networks (FSPDN/BSPDN). A key contribution is a novel lightweight IR drop modeling tool developed in-house, which enables supper fast and highly accurate power integrity estimation at early physical design stages—far before signoff—significantly reducing design iteration time caused by IR violations. This tool also fills a critical gap in commercial EDA support for advanced 3D integration and BSPDN evaluation. Using this tool alongside multi-physics thermal simulations, we compare four 3D design scenarios. Results show that the MoL architecture with BSPDN achieves an optimal balance between power and thermal integrity: it reduces worst-case IR drop in the logic die to just one-fourth of the 2D reference, and lowers peak temperature by over 15°C compared to a LoM counterpart. Further improvements, 50% in IR drop decrease and 14°C temperature reduction, are attainable through TSV optimization and high-thermal-conductivity material integration. This study provides essential 3D architeture, design and technology cooptimization methodologies for future high-perfermance 3D CPUs of advanced technology nodes.

The swift growth of e-commerce has led to an increase in fraudulent activities, which results in significant financial losses for both suppliers and consumers. Current research on detecting fraudulent activities within e-commerce platforms primarily focuses on analyzing individual user behavioral patterns over time or examining the spatial relationships among users. However, considering temporal or spatial contexts alone is not sufficient for fraud detection since they may not exist in real scenarios. Additionally, the issue caused by the imbalance of the data to be classified has not been solved in the field of fraud identification. To address these challenges, a novel scheme is proposed for fraudulent user detection in this work. The main contribution lies in the spatiotemporal fusion of user behavior and the layer-by-layer selective aggregation of graph models. Specifically, we utilize a long short-term memory model and a multi-layer perceptron model to extract the discriminant features from time-dependent and time-independent user behavior, respectively. This approach enhances the model’s ability to detect fraudulent users with different behavioral characteristics, including time-correlated and/or time-independent fraud behavior. Furthermore, a shared classifier is added to general graph neural network, it reclassifies the output of each layer of the graph model and reconstructs the spatial neighbor relationship. This little trick makes minority class samples select similar samples with a greater probability to build their spatial neighbor relationships, which can alleviate the issue of data imbalance. In the numerical experiments, three real datasets are used to validate the proposed scheme. Experiment results, including performance evaluation, comparison with existing benchmark approaches and ablation analysis, are presented and discussed.

Mobile Edge Computing (MEC) is a promising technology for future 6G communication systems. However, the dynamic network environment and the selfish nature of devices pose challenges to task offloading. Therefore, it is very critical to design an effective cooperative offloading scheme in dynamic environments. In this paper, a social-assisted two-stage cooperative task offloading and resource allocation algorithm based on Stackelberg game and DRL (SAC-SDRL) is proposed to maximize the system utility. The problem is formulated as a mixed integer non-linear programming (MINLP) problem that jointly determined the edge server selection, and offloading rate, resource price, and resource allocation. To address this problem, two stage-solutions are introduced. In the first stage, given a fixed resource price and offloading rate, the edge server selection and resource allocation scheme based on hybrid actor-critic algorithm is designed to solve the problem of hybrid action space. In order to avoid invalid decision space, a clustering method based on social relationship and spectral clustering is developed. In the second stage, based on the obtained edge server selection and resource allocation decision, a dynamic pricing and offloading incentive scheme based on the Stackelberg game is proposed, in which the optimal resource price and optimal offloading rate can be determined with the proposed gradient-based iterative search method. Moreover, it is proved that the game can achieve the Stackelberg equilibrium. Finally, simulation results show that the proposed SAC-SDRL algorithm can achieve higher system utility compared with other concerned algorithms.

Handover (HO) is a critical component of mobility management in the 5th generation (5G) of communication networks, which ensures seamless connectivity and optimal communication performance for user equipment (UE) in motion across different cells. In previous studies, the deep reinforcement learning (DRL) techniques were employed to solve the HO problem. However, for most of these methods, the growing complexity in action space was not considered as the number of UEs increases, leading to inefficient model convergence and HO failures. To address this issue, this paper proposes a novel PPO-MH (Proximal Policy Optimization with Masking for Handover) model for multi-cell selection handover problem. This model calculates the action mask for each UE before each handover using the UE's measurement report, providing prior information for the decision-making process. By dynamically masking base stations (BSs) that do not meet the handover conditions, the model avoids invalid hand overs and improves sampling efficiency. Experimental results demonstrate that the PPO- MH outperforms the traditional PPO across various scenarios, ensuring Quality of Service (QoS) for UEs and reducing the handover frequency. Additionally, PPO- MH converges significantly faster than PPO, which validates the effectiveness of the action mask strategy. Benefiting from these advantages, our methods have broad potential applications, especially in scenarios requiring efficient resource management and low-latency handovers.

In order to meet increasing demands of higher energy efficiency and better reliability performance for the multiuser Internet of things (IoT) secure communications, in this paper, we design a reliable and energy efficient superposition modulation (SM) and singular value decomposition (SVD) detection based multi-user orthogonal frequency division modulation-based differential chaos shift keying (SM-MU-OFDM-DCSK) transceiver for IoT devices. At the transmitter, we propose to superimpose and overlap the information-bearing chaotic signals for higher energy efficiency. Since the chaotic modulated signals of each user share identical reference chaotic signals, the transmitted superimposed signal matrices have the low-rank property. Then at the receiver, we apply the SVD-aided detection to recover these low-rank signals, which can suppress the noise to attain better reliability performance. Moreover, we prove that the proposed design can achieve the maximum likelihood detection performances. Furthermore, we derive the theoretical bit rate, energy efficiency and bit error rate expressions over additive white Gaussian noise (AWGN) channel. Simulation results are then provided to validate the theoretical derivations. Subsequently, the simulated performances over AWGN and fading channels are investigated to show that higher energy efficiency and better reliability performances than benchmark schemes can be achieved in industrial IoT scenarios.

Mobile Edge Computing (MEC) is one of the key enabling technologies for future 6G wireless networks that can provide lower latency service and more efficient resource utilization for future intelligent applications and the Internet of Things (IoT), while also reducing the energy consumption of end devices. In the intricate dynamic edge environment, the task offloading problem is entangled with several factors, such as the uncertainty of online tasks, the heterogeneity of edge servers, and the mobility of devices. In this paper, considering the randomness of online task arrivals, time-varying channels, and mobility of devices, a deep reinforcement learning-based online task offloading (DRL-OTO) algorithm is designed to minimize the energy consumption of all mobile devices. Specifically, by portraying the system model consisting of the communication model, energy consumption model, and node mobility model, the task offloading optimization problem is modeled as a mixed integer nonlinear programming (MINLP) problem. By decomposing this problem, each mobile device first determines the edge server to be offloaded, and then the DRL-OTO algorithm is designed by utilizing the DDPG method, in which each mobile device is able to determine the offloading rate. Simulation results show that the proposed DRL-OTO algorithm can achieve fast convergence and is able to reduce energy consumption, thus increasing the utility of all devices in the dynamic edge environment.

Abstract The residual belief propagation (RBP) algorithm, which is the most classic informed dynamic scheduling strategy, achieves outstanding performance in error correction and can drastically accelerate convergence speed. However, the greedy algorithmic property of this iterative decoding will inevitably cause loss of decoding performance. To address this, a novel algorithm called the partial average bundle residual belief propagation (PABRBP) is proposed in this paper. According to the construction characteristics of a base matrix of protograph‐LDPC codes, informed dynamic scheduling (IDS) strategies are applied to an edge bundle of base matrices for the first time. This edge bundle of the base matrix can be applied to a corresponding cyclic permutation matrix. Furthermore, the update level of each bundle is determined by the value of the Partially Average Bundle Residual (PABR). Therefore, the edge message with the maximum residual in each bundle is updated in order, and the process of iterative decoding is less likely to become trapped in a local optimum. Additionally, the generation of silent nodes is reduced as much as possible. To further improve the PABRBP decoding performance for medium and long codes over the fading channel, the adjusted compensation term is periodically introduced. Analysis and simulation results show that PABRBP demonstrates a notable convergence quality and decoding performance improvement over the fading channels compared to existing state‐of‐art IDS algorithms.

Abstract Polar codes have received widespread attention because of their excellent performance. However, although successive cancellation (SC)‐based decoding algorithms have achieved excellent error correction performance, they still encounter large time delays in decoding because a serial decoding structure is employed. At present, a cyclic redundancy check (CRC) is used to judge whether the decoding result of a frame is erroneous. To obtain such a result, the decoder has to wait until the end of decoding. In this work, a judgement strategy for error frames using frozen bits is proposed. With the proposed strategy, the recognition of erroneously decoded frames can be achieved based on the error state of the frozen bits in this frame. Accordingly, early termination of decoding can be realized. By using this mechanism, the strategy can also be applied in the SC List algorithm to prune the path. In addition, the SC Flip decoding algorithm can also use the proposed strategy to help find the first error message bit. The experimental results show that at least 85% of the frames decoded erroneously can be recognized by the proposed strategy so that decoding can be terminated in advance and computational resources can be saved.

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors, such as the existence of holes, nonuniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine (ELM) and ring-shaped salp swarm algorithm (SSA) is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of ELM, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the SSA based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.

Identifying the nature of data flows can help improve network service and security. Most existing solutions usually simplify the traffic classification to protocol and application identification based on some uniqueness assumptions. However, in the real world these assumptions aren’t always reasonable due to the abuse of multiplexing techniques. In this work, a new scheme is proposed from a different perspective that aims to directly identify the content inside a data flow without considering the external protocols and applications. We use wavelet to obtain the time-scale signals of each data flow and develop a new hidden Markov tree (HMT) with an embedding deep neural network (DNN) to model these signals. Each hidden state of the HMT represents a specific signal generation pattern. Transition of hidden states describes the time-scale context of the signal patterns. DNN is used to describe the probabilistic relationship between the implicit patterns and the observed time-scale signals. We derive new algorithms for the model and create an instance for each type of traffic, which projects the data flows into a multi-dimensional decision space and achieves their content identification through a classifier. Numerical experiments using real datasets are presented to validate the proposed scheme. Performance-related issues and comparisons with related works are discussed.

Hop Count Matrix (HCM) contains rich connectivity information, which is very important for various Internet of Things (IoT) applications, especially for obtaining the locations of sensor nodes. However, some items of HCMs may be missing due to attacks by malicious nodes or unexpected termination of flooding operations. To solve this problem, two methods, called HCMR-AM and HCMR-DT, are proposed to recover the missing items. In HCMR-AM, the collected partial hop counts are employed to construct Adjacency Matrix (AM), and then the constructed AM is used to obtain the complete HCM. In HCMR-DT, the recovery of HCM is transformed into a classification problem, where multi-dimensional features are used for joint prediction to achieve more accurate recovery performance. Extensive experimental results demonstrate that compared to the original SVT and HCMR-NBC, our proposed algorithms have significant improvement in recovery performance and execution efficiency. In addition, the complete HCM is used for node localization, and experimental results show that the HCM recovered by the proposed methods can achieve the same localization performance as the HCM without missing value when the observation ratio of HCM is greater than 30%, which cannot be achieved by other recovery algorithms.

Abstract The informed dynamic scheduling (IDS) strategies, in which the edge message with the maximum message‐residual is updated preferentially, achieve remarkable error‐correction performance when applied to low‐density parity‐check (LDPC) codes. However, the IDS strategies incur inferior convergence in iterative decoding owing to the greedy problem, which is called the update‐relayed trend here. In order to solve the greediness, two locally informed dynamic scheduling algorithms based on the law of large numbers are proposed. The proposed decoding algorithms use random select of check nodes over a predefined update range (RSPUR) which effectively suppresses the propagation of the update‐relayed trend and accordingly restrains the forming of multi‐update cycles. Moreover, the decoding algorithm is further improved based on random select of check nodes over an adjustable update range (RSCAR). The update ranges are selected based on the law of large numbers. Therefore, the computational resources can be allocated more equitably by increasing iterations. Simulation results show that both the proposed algorithms achieve excellent performance in terms of throughput and convergence with low decoding complexity over the Additive White Gaussian Noise (AWGN) and the fading channels compared to the previous IDS strategies. Hence, the proposed algorithms behave excellently over the wireless channels.

Abstract The successive cancellation flip (SC Flip) decoding algorithm was recently suggested for decoding polar codes, which could improve the performance of the frame error rate (FER). The performance of the SC Flip (SCF) decoding algorithm is strong, and its computational complexity tends to be the same as that of the SC decoder at medium to high signal‐to‐noise ratios (SNRs). However, the decoding latency of the SCF decoding algorithm is large. In this paper, a new method for detecting whether or not the flipped bit is correct is proposed. The proposed method makes a decision according to the changing log likelihood ratio (LLR) value caused by the flipped bit to ensure that the decoding process can be terminated in advance and the decoding latency is reduced. The simulation results show that the proposed Latency‐Reduced SCF decoding algorithm can decrease computational complexity and decoding latency, while also achieving similar decoding performance compared to its counterpart.

Location information plays an important role in many applications of the Internet of Things (IoT). The low cost and ease of scalability of range-free localization algorithms have attracted the attention of many researchers, but the performance of many localization algorithms available in the literature varies greatly in different networks. Specifically, algorithms designed for anisotropic networks may not perform well in isotropic networks, and vice versa. To improve localization accuracy in both isotropic and anisotropic networks, a novel range-free localization algorithm named LSAE is proposed in this article, oriented to the network positioning without ranging over the IoT. The proposed algorithm utilizes the known information in the network, namely, the hop counts and distances between anchor nodes, to train the stacked autoencoders (SAE) model. In this way, it achieves accurate prediction of the distances between unknown nodes and anchor nodes. To further improve the localization accuracy, the disadvantage of the least square method is analyzed, and a novel coordinate estimation method based on the statistical results of distance estimation errors is proposed. We conducted a huge number of numerical simulations with and without the impact of multiple anisotropic factors in three different types of networks. The results indicate that the proposed algorithm outperforms other state-of-the-art algorithms treating the impact of multiple anisotropic factors, and demonstrates the high accuracy and robustness.

Distributed threat-events are one of the main challenges faced in computer networks. Although a lot of research has been conducted for these issues, the situation has not been significantly improved. Different from existing victim-centric approaches, in this article we propose a new network-centric approach for the detection of distributed threat-events. The distributed network is treated as a holistic system that consists of spatially interconnected network elements. Network events are detected by the dynamic behavior analysis of the distributed networks. We develop a model consisting of two-layer random fields to describe the time-varying traffic forwarding behavior of the distributed networks. The bottom layer describes the interaction and influence of the network elements under the action of network events. Markovianity is adopted to characterize the spatiotemporal context of each network element’s behavior patterns. The top layer describes each network element’s traffic features driven by the underlying behavior patterns. A Gaussian mixture model is used to capture the statistical features of the network traffic for each behavior pattern. We derive algorithms for parameter estimation and event detection. Numerical experiments using real datasets and different network scenarios are presented to validate the proposed approach. Performance-related issues and comparison with related works are discussed.

Abstract The performance of the cyclic redundancy check aided successive cancellation list (CA‐SCL) decoder for polar codes exceeds that of the Turbo codes and the Low Density Parity Check (LDPC) codes adopted in the World Interoperability for Microwave Access (WiMAX) proposal. However, CA‐SCL decoder has a high computational complexity, resulting in a long time delay and high memory complexity. In order to alleviate this problem, an improved path splitting strategy on successive cancellation list (IPSS‐SCL) decoder was proposed, which can significantly reduce the average list size. The influence of splitting on CA‐SCL decoder was analysed at first and a new selective splitting criterion using the Gaussian approximation method was proposed according to the analysis. In addition, a path contraction mechanism based on the location estimation of the correct path was proposed to further reduce the average number of unnecessary candidate paths. Compared with existing path splitting strategies, simulation results show that the proposed IPSS‐SCL decoding algorithm can reduce the average computational complexity significantly over the additive white Gaussian noise (AWGN) channel with almost no performance loss.

In this study, a serially concatenated scheme of polar codes with convolutional codes is proposed to improve the error correction performance. The novel belief propagation (BP) decoding algorithm addresses two issues that are present in the currently available BP decoding algorithms. The first issue is the poor performance of the BP decoding algorithms, in particular the introduction of an error floor. The second is the component codes can only use systematic codes in the traditional concatenated scheme of polar codes with convolutional codes, which inhibits the effective update of the prior information of the redundant check bits. The proposed BP decoding algorithm is based on right‐directed message processing, which effectively improves the decoding performance. In addition, the proposed concatenated scheme extends the selection of component codes from the systematic polar codes to the non‐systematic polar codes. Hence, the areas of applications and the prior information of information bits for polar codes are expanded and more effectively updated, respectively. The simulation results show that the proposed scheme is much better than the traditional concatenated scheme, and the error floor is no longer introduced in terms of the block error rate, while the storage and computational complexities have not increased obviously.

The hop count matrices (HCMs) are very helpful in obtaining the location information of sensing nodes in Internet of Things (IoT). However, in some scenarios, the HCMs cannot be completely observed due to abnormal termination of the flooding process, or some of the entries are contaminated by false information in external malicious attacks. Therefore, it is very important to recover the missing HCMs. However, to the best of our knowledge, there is no specific algorithm used in the current research to recover the HCMs, which would cause the positioning accuracy to be seriously deteriorated. In this article, for the scenarios of the entries partially observed in the HCMs, the HCMs recovery schemes, namely, HCMR-NBC and HCMR-MC, are proposed. The former, HCMR-NBC, is to learn the internal relations of different sensing node pairs in the HCMs. It is a simple and fast approach which utilizes the feature with a single dimension to predict the missing hop count values between the sensing nodes. The latter, HCMR-MC, is to transform the problem of the matrices recovery to the one of matrices completion. Compared with the previous SVT and BLMC algorithms, the proposed algorithms have great advantages in terms of the reconstruction performance and the computation complexity.

It is essential to acquire the location of sensor nodes in the wireless sensor networks (WSNs), since the data collected with nodes would become meaningless without their location. In the existing studies, range-free localization schemes have been proved suitable to large-scaled WSNs due to the low cost in hardware implementation. However, the defect of those schemes is their poor accuracy in anisotropic networks with coverage holes. To tackle this problem, we propose a range-free localization scheme that combines the advantages of geometric constraint and hop progress-based methods. The geometric information provided by the combination of anchor pairs and unknown nodes is used to design the discrimination conditions, and each node divides the anchor pairs into one of three proposed categories. For different categories of anchor pairs, corresponding methods are proposed to estimate the distancse between sensor nodes. In this way, the trade-off between distance estimation accuracy and anchor utilization can be achieved. Simulation results indicate that the proposed scheme outperforms other schemes in terms of localization accuracy and proportion of outliers with acceptable computational and time complexity, where the localization accuracy and proportion of outliers in the proposed scheme are improved by up to 47% and 61% compared to DV-maxHop.

Many studies were carried out for the construction of low density parity-check (LDPC) codes. They usually focused on introducing the construction methods for good LDPC codes instead of a general method for code optimization. This paper proposes a method with high versatility, called the parallel vector message passing-based edge exchange (PMPE), for optimizing a type of graph-based LDPC codes, without changing the code parameters of mother codes, such as the code length, code rate, and degree distribution. With the approximately nearest codewords searching approach, we find the optimization method can increase the Hamming distance of the LDPC codes. For the quasi-cyclic (QC) LDPC codes, an optimization method, called the parallel vector message passing oriented-to the QC-LDPC codes (QC-PMP), is further suggested, with which the quasi-cyclic characteristics of QC-LDPC codes can remain unchanged in the optimization. To evaluate the performance of the parity-check matrix corresponding to a Tanner graph, a very simple metric, the cycles metric, is introduced to work with the proposed PMPE and QC-PMP algorithms. The experimental results show that the performance of the LDPC codes optimized with the proposed PMPE can be improved significantly at low BER range compared with the mother codes of the random codes, including the regular MacKay code of rate 0.5 and the regular PEG code of rate 0.9. For the case of the regular and irregular QC-LDPC codes with different code lengths and code rates, the optimized LDPC codes with the proposed QC-PMP algorithm significantly outperform the mother codes.

The informed dynamic scheduling (IDS) strategy decoding algorithms performed exceptionally well for low-density parity-check codes in terms of the error-rate performance. However, the IDS decoding algorithm is greedy because of the unfair computation resources allocation among different variables nodes, which leads to poor convergence performance. In order to reduce the greediness of the IDS algorithm, the tabu search (TS) algorithm is introduced to the dynamic scheduling-based decoding in this paper. In the TS-based dynamic scheduling (TSDS) algorithm, the variable nodes in the Tanner graph are temporarily stored in a tabu list. In the decoding process with the TSDS algorithm, variable nodes stored in the tabu list will not be selected and updated until they are shifted out of the tabu list. Besides, an improved updating order is provided for the TSDS algorithm, by which the computational complexity can be decreased without the loss of error correction performance. Simulation results show that the proposed algorithm outperforms other decoding algorithms of interest in terms of bit error rate and convergence performance over the additive white Gaussian noise channel.

Among most of the message scheduling strategies for low-density parity-check (LDPC) codes, the dynamic scheduling strategy behaves best in error correction performance. Dynamic selection is an integral part of dynamic scheduling decoding, which plays a decisive role throughout the decoding process. Usually, the dynamic selection strategy based on the message residuals only is employed in dynamic decoding algorithms, while other potentials of the dynamic selection strategy are rarely cared about. In this paper, we propose the triple judgment dynamic selection strategy combined with a Stability Criterion. Interestingly, the new strategy can be well applied to two different dynamic algorithms, namely, the V-VCRBP and the V-CVRBP algorithms. The proposed strategy has a great advantage: locating the message to be preferentially updated is extremely quick and accurate. Simulation results demonstrate that the V-VCRBP algorithm outperforms existing decoding algorithms in terms of BER performance and convergence speed, while the V-CVRBP algorithm has good error correction performance with a lower computational complexity.

Shuffled belief propagation (SBP), as a sequential belief propagation (BP) algorithm, speeds up the convergence of BP decoding, and maintains the least complexity of flooding BP. However, its performance is remarkably inferior to informed dynamic scheduling (IDS) BP algorithms. The authors design an informed dynamic location method, based on the residuals of variable node log‐likelihood ratio values, to reorder variable nodes of SBP to be updated. The location method significantly accelerates the convergence of SBP algorithm from two aspects: the unstable variable node with the largest residual to be updated first, and selecting the largest residual locally. Simulation results show that the proposed algorithm performs nearly the same as the best performance of IDS BP algorithms, and behaves prominently at high signal‐to‐noise ratios.

In this study, an efficient decoding algorithm is proposed to decode non‐binary low‐density parity‐check (LDPC) codes. The algorithm is derived from the belief‐propagation (BP) decoding with genetic algorithm of binary LDPC codes. For the proposed algorithm, two decoding constraints are introduced to determine variable nodes which are considered highly reliable. The messages from these highly reliable variable nodes are then magnified with an appropriate parameter γ . This process can make the messages propagate in the Tanner graph more efficiently. Simulation results show that, compared with the fast Fourier transform‐based BP algorithm, the proposed algorithm can have an equal or better performance in low bit‐error‐rate region over both the additive white Gaussian noise channels and the mobile fading channels.

The simultaneous flooding scheduling is popular for Low-Density Parity-Check (LDPC) Belief Propagation (BP) decoding. Non-simultaneous sequential scheduling is superior to the flooding scheduling, and asynchronous dynamic scheduling has better FER performance than the sequential scheduling. However, all strategies encounter the trouble of locating the error variable node. This paper proposes an informed dynamic scheduling strategy, which utilizes the instability of the variable node and the residual of the variable-to-check message to locate the message to be updated first. The informed dynamic scheduling overcomes the trapping sets effectively. This paper also designs an informed dynamic scheduling strategy with adaptivity to pass more messages in parallel, which effectively postpones the influence of cycles in the Tanner graph. In some sense, the strategy lengthens cycles. Simulation results show that the two informed dynamic scheduling strategies outperform other algorithms.

The energy constraint in wireless sensor networks is a crucial issue affecting the network lifetime and connectivity. To realize true energy saving in a wireless environment and ensure reliable communications, the noise condition of the wireless channel should be taken into account. In this paper, w