ABSTRACT The sphere decoding (SD) algorithm for polar codes can achieve maximum likelihood decoding performance, but suffers from high computational complexity. Based on the polar codes with symmetrical structure, the complexity of the SD algorithm can be reduced at the expense of a slight performance loss. To further improve the performance of the SD algorithm for symmetric polar codes, an improved flip SD algorithm is proposed in this paper. When the estimated codeword fails the cyclic redundancy check (CRC), the proposed algorithm flips an unreliable combined value and re‐performs the decoding process. Specifically, using the structure of symmetric polar codes, a flipping set is first constructed to incorporate unreliable combined values. The combined values in the flipping set are sorted according to their reliability. Then, a new flipping operation is developed for the flipped values to further improve performance. Simulation results show that, compared to the symmetric SD algorithms, the proposed algorithm can achieve a performance gain of up to 1.6 dB for the polar code of length 64 with different rates at the frame error rate (FER) of , with a slightly higher computational complexity; when compared to the original SD algorithm, the proposed algorithm can achieve a maximum of 1.58 dB performance gain at FER = but with a lower computational complexity.

Edge computing plays a critical role in supporting low-latency IoT applications, yet the susceptibility of edge nodes to faults can disrupt services and degrade Quality of Service (QoS). Fault prediction offers a proactive solution by identifying potential failures through spatio-temporal feature learning from historical observations. However, existing spatio-temporal prediction models are typically designed for fixed network topologies with predefined input-output structures, which limits their effectiveness in dynamic edge networks where nodes are frequently added or removed. Adapting these models to topology variations often requires full retraining or architectural redesign, resulting in substantial computational overhead and limited real-time applicability. To overcome these limitations, this paper proposes ASTFNet, an adaptive spatio-temporal fault prediction framework for dynamic edge networks. The framework integrates a spatio-temporal fault prediction model that incorporates node identity embeddings to enable flexible representation learning under evolving topologies and an adaptive fine-tuning mechanism that detects topology changes and performs targeted model updates without full retraining. Experiments on real-world datasets demonstrate that ASTFNet significantly reduces retraining time while maintaining high prediction accuracy and achieves robust performance under dynamic node additions and removals.

In the problem of conditional disclosure of secrets (CDS), two parties, Alice and Bob, each has an input and shares a common secret. Their goal is to reveal the secret to a third party, Carol, as efficiently as possible, only if the inputs of Alice and Bob satisfy a certain functional relation$f$. To prevent leakage of the secret to Carol when the input combination is unqualified, both Alice and Bob introduce noise. This work aims to determine the noise capacity, defined as the maximum number of secret bits that can be securely revealed to Carol, normalized by the total number of independent noise bits held jointly by Alice and Bob. Our contributions are twofold. First, we establish the necessary and sufficient conditions under which the CDS noise capacity attains its maximum value of 1. Second, in addition to the above best-case scenarios, we derive an upper bound on the linear noise capacity for any CDS instance. In particular, this upper bound is equal to$(\rho-1)(d-1) /(\rho d-1)$, where$\rho$is the covering parameter of the graph representation of$f$, and$d$is the number of unqualified edges in residing unqualified path.

Motivated by federated learning (FL), secure aggregation (SA) aims to securely compute, as efficiently as possible, the sum of a set of inputs distributed across many users. To understand the impact of network topology, hierarchical secure aggregation (HSA) investigated the communication and secret key generation efficiency in a 3-layer relay network, where clusters of users are connected to the aggregation server through an intermediate layer of relays. Due to the pre-aggregation of the messages at the relays, HSA reduces the communication burden on the relay-to-server links and is able to support a large number of users. However, as the number of users increases, a practical challenge arises from heterogeneous security requirements–for example, users in different clusters may require varying levels of input protection. Motivated by this, we study weakly-secure HSA (WS-HSA) with collusion resilience, where instead of protecting all the inputs from any set of colluding users, only the inputs belonging to a predefined collection of user groups (referred to as security input sets) need to be protected against another predefined collection of user groups (referred to as collusion sets). Since the security input sets and collusion sets can be arbitrarily defined, our formulation offers a flexible framework for addressing heterogeneous security requirements in HSA. We characterize the optimal total key rate, i.e., the total number of independent key symbols required to ensure both server and relay security, for a broad range of parameter configurations. For the remaining cases, we establish lower and upper bounds on the optimal key rate, providing constant-factor gap optimality guarantees.

Accurate prediction of sea surface temperature (SST) plays a critical role in climate research and marine ecosystem management. Traditional models predict trends by analyzing and fitting data, but they struggle with capturing long-range dependencies and complex spatiotemporal patterns. The transformer’s attention mechanism effectively addresses long-range dependencies, but its high computational complexity poses challenges. To overcome these limitations, this study proposes a novel spatiotemporal sequence prediction model: the spatiotemporal vision mamba recurrent neural network (SVRNN). The model innovatively integrates a bidirectional state-space processing mechanism and decoupled memory modules. The bidirectional mechanism maintains a global receptive field with linear computational complexity, while the decoupled memory modules explicitly separate spatiotemporal dependencies, enhancing the model’s ability to capture complex spatiotemporal patterns. During the experiment on hourly SST prediction in the Taiwan Strait, where the SST of the next 12 h was predicted using data from the previous 12 h, the SVRNN model demonstrated superior performance, achieving a root mean square error (RMSE) of$0.159~^{\circ }$C, a mean absolute error (MAE) of$0.105~^{\circ }$C, and a mean absolute percentage error (MAPE) of 0.496%. Furthermore, our seasonal error analysis reveals that the model exhibits robust performance in different seasons, providing more reliable technical support for SST prediction in Taiwan Strait.

Channel model substitution (CMS) is a technique that aims to replace a computationally challenging channel model with a simpler substitute. This technique is powerful for rapid adaptive signal processing and closed-form performance analytics. The parametric mapping between an original channel model and its substitute determines the utility of CMS. In the past decades, the moment matching criterion has dominated for conducting parametric mapping, which, however, is heuristic and has been proven non-optimal. In this paper, we propose to utilize particle swarm optimization (PSO) to obtain optimal parametric mapping relations for a general CMS problem, regardless of the distributional forms of the original channel model and the substitute. Taking the CMS techniques for the lognormal shadowed channel model as examples, simulation results show that the PSO enabled parametric mapping approach is capable of converging to the global optima under diverse system configurations, making CMS computationally feasible.

This paper considers the scenario of noisy inputs and compressive diffusion (for reducing communication load) with noisy links over sensor networks. We first study the implementation of diffusion bias-compensated Bayesian adaptation (DBCBA) for noisy inputs, which outperforms the existing solution. Next, an average-estimate step is applied to lessen the impact of link noise in the full diffusion case, yielding a diffusion average-estimate bias-compensated Bayesian adaptation (DABCBA) algorithm. A Bayes-based adaptation construction step is then presented to reconstruct the compressed diffusion information in the presence of link noise, resulting in a compressive DBCBA (CDBCBA) algorithm whose mean and mean-square behaviors are analyzed and the closed-form expression of the steady-state mean-square deviation is derived. In addition, estimators are devised for the input and output noise variances. The excellent performance of our algorithms is demonstrated via numerical examples while the theoretical calculation aligns closely with the simulation results.

Abstract In recent years, large‐scale satellite networks have been studied emphatically due to its advantages in high throughput and low latency. Many companies and institutions are keen to build large‐scale low earth orbit satellite constellations to provide space‐based Internet services, which poses a great demand for the design of efficient and reliable routing schemes. Three attribute indexes to evaluate the performance of the satellites are proposed. With the Dempster–Shafer theory, the attributes can be fused for the routing decision‐making process. Based on the Message Identification divergence (M‐I divergence) measure method, a Belief Message Importance divergence based Routing algorithm is proposed. By adjusting the characteristic parameter, it can amplify the divergence between distance‐based evidence and others properly. In this way, Belief Message Importance divergence‐based Routing attempts to find the optimal shortest path according to the status of intermediate nodes. Compared with existing satellite routing schemes, the algorithm that is proposed can approach the optimal routing performance, increasing the throughput by 47.2% on average, and decreasing the total delay and packet drop rate by 59.5% and 42.1% on average, respectively.

Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentiment analysis.This paper aims at extracting new sentiment words from large-scale user-generated content.We propose a fully unsupervised, purely data-driven framework for this purpose.We design statistical measures respectively to quantify the utility of a lexical pattern and to measure the possibility of a word being a new word.The method is almost free of linguistic resources (except POS tags), and requires no elaborated linguistic rules.We also demonstrate how new sentiment word will benefit sentiment analysis.Experiment results demonstrate the effectiveness of the proposed method.

Clustering aspect-related phrases in terms of product’s property is a precursor pro-cess to aspect-level sentiment analysis which is a central task in sentiment analy-sis. Most of existing methods for address-ing this problem are context-based models which assume that domain synonymous phrases share similar co-occurrence con-texts. In this paper, we explore a novel idea, sentiment distribution consistency, which states that different phrases (e.g. “price”, “money”, “worth”, and “cost”) of the same aspect tend to have consistent sentiment distribution. Through formal-izing sentiment distribution consistency as soft constraint, we propose a novel unsu-pervised model in the framework of Poste-rior Regularization (PR) to cluster aspect-related phrases. Experiments demonstrate that our approach outperforms baselines remarkably. 1

This paper is concerned with the Gaussian relay channel (GRC) under the one-code constraint, where the source and the relay utilize the same code to send message. An advantage of such one-code constraint is that the error propagation resulting from re-encoding can be mitigated as the relay can forward directly the decoded “codeword” to the destination. The maximal achievable rate of the considered GRC is derived using the technique of superposition block Markov encoding based on the single code. Moreover, the forward-backward (FB) decoding strategies over the sliding window are developed both at the destination and at the relay. When LDPC codes are applied to the GRC system, a practical FB message passing decoding algorithm is presented. Simulation results show that the decoding performance can be improved as the window length increases and a small length (no greater than 4) is good enough for the FB decoding, and that re-encoding at relay may degrade the decoding performance at the destination.

The extended min-sum (EMS) algorithm is redescribed as a reduced-search trellis algorithm (called M-EMS algorithm). Two variants of the M-EMS algorithm, called T-EMS algorithm and D-EMS algorithm, are presented. Simulation results show that, these three algorithms (referred to as X-EMS algorithms for convenience), combined with factor correction techniques, perform almost as well as the Q-ary sum-product algorithm (QSPA) but with a much lower complexity.

A modified reliability-based iterative majority-logic decoding (MRBI-MLGD) algorithm for two classes of structured LDPC codes is presented based on a recent work by Huang et al. Compared with the original one, the modified algorithm has better performance with slightly increased complexity. Then a reliability-based iterative min-sum decoding (RBI-MSD) algorithm is presented. For the presented RBI-MSD algorithm, reliability-based integer messages are processed and exchanged between variable nodes and check nodes. The main computations include only binary logical operations and integer additions. Different from the conventional min-sum algorithm, the variable nodes pass full messages rather than extrinsic messages to check nodes. This can reduce the memory loads and the computational complexity but with a little (or negligible) performance degradation. Simulation results show that, compared with the (M)RBI-MLGD algorithms, the presented RBI-MSD algorithm achieves better error performance, faster decoding convergence rate and fewer quantization bits with moderate increased computational complexity. Furthermore, the RBI-MSD algorithm is also applicable to decoding random LDPC codes, a distinct difference from the (M)RBI-MLGD algorithms. Finally, we point out that the scaling factors employed in the MRBI-MLGD algorithm and the RBI-MSD algorithm can be optimized using discretized density evolution.

The emergence and application of cloud computing can help users access to various computing resources and services more conveniently. However, it also brings forth many security challenges. This paper proposes the application oriented remote verification trust model, which is capable of adjusting the user's trust authorization verification contents according to the specific security requirements of different applications. The model also dynamically adjusts the user's trust value with the trust feedback mechanism to determine whether or not the requested resource or service should be provided, so as to guarantee the security of information resources. This paper provides a formal description of the basic components and trust properties of the model with a belief formula, and describes the framework for the implementation of the model.

Finding the core members of a virtual community is an important problem in community analysis. Here we presented an simulated annealing algorithm to solve this problem by optimizing the user interests concentration ratio in user groups. As an example, we test this algorithm on a virtual community site and evaluate its results using human "gold standard" method.