Journal of Information and Intelligence最新文献

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Interference management for active RIS-aided symbiotic radio networks 有源ris辅助共生无线网络的干扰管理

Journal of Information and Intelligence

Pub Date : 2025-07-01 Epub Date: 2025-04-07 DOI: 10.1016/j.jiixd.2025.03.002

Weidong Wan, Yi Liu, Hailin Zhang

Symbiotic radio (SR) is a technology that facilitates mutually beneficial sharing of spectrum and energy between primary and secondary systems. In SR networks, utilizing active reconfigurable intelligent surface (RIS) as the secondary transmitter (STx) enhances this mutual benefit compared to passive RIS. This paper addresses the interference management challenges that inevitably arise from employing active RIS. We consider a common SR network consisting of three types of users: SR users, non-SR users, and eavesdroppers. Additionally, each SR user has their own unique cellular services. We propose minimizing the total power consumption while satisfying a sufficiently large signal-to-interference-plus-noise ratio (SINR) for SR users, a small enough SINR for eavesdroppers, and a small enough interference temperature for non-SR users. The alternative optimization (AO) method is used for decoupling multi-variables. The non-convex constraints are relaxed as convex ones through first-order Taylor approximation, and the bounded channel state information (CSI) error model is handled using the S-procedure. Simulations validate the superiority of the proposed algorithm and demonstrate that the total power consumption is minimized while meeting performance thresholds. Additionally, the results offer valuable insights for SR network deployment.

共生无线电（SR）是一种促进主次系统之间频谱和能量互利共享的技术。在SR网络中，与被动RIS相比，利用主动可重构智能表面（RIS）作为二次发射机（STx）增强了这种互利。本文讨论了采用主动RIS不可避免地产生的干扰管理挑战。我们考虑一个普通的SR网络，由三种类型的用户组成：SR用户、非SR用户和窃听者。此外，每个SR用户都有自己独特的蜂窝服务。我们建议最小化总功耗，同时满足SR用户足够大的信噪比（SINR），窃听者足够小的SINR，以及非SR用户足够小的干扰温度。采用备选优化（AO）方法求解多变量解耦。通过一阶泰勒近似将非凸约束放宽为凸约束，并使用s过程处理有界信道状态信息（CSI）误差模型。仿真结果验证了该算法的优越性，并证明在满足性能阈值的情况下，总功耗最小。此外，研究结果为SR网络部署提供了有价值的见解。

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引用次数: 0

The interference range of the spatial outage capacity in bipolar wireless networks 双极无线网络空间中断容量的干扰范围

Journal of Information and Intelligence

Pub Date : 2025-07-01 Epub Date: 2025-04-01 DOI: 10.1016/j.jiixd.2025.03.004

Min Ouyang , Tong Wang , Pei Xiao , Jiyi Wu , Shan Gao , Liwei Chen

Interference range plays a critical role in wireless network performance, significantly impacting both link reliability and resource utilization. This paper studies the interference range associated with the spatial outage capacity (SOC), which is the maximum density of reliable links of bipolar networks. We establish a recursive equation based on the transmitter's active probability, establishing a link between the interference range and the SOC. The analytical results are then verified through numerical and network simulations. The experimental results indicate that the interference range may improve the SOC of Poisson bipolar networks while deteriorating the SOC of Poisson cellular networks and random distance bipolar networks.

干扰范围在无线网络性能中起着至关重要的作用，对链路可靠性和资源利用率都有重要影响。本文研究了与空间中断容量（SOC）相关的干扰范围，SOC是双极网络中可靠链路的最大密度。我们建立了基于发射机主动概率的递归方程，建立了干扰范围与SOC之间的联系。通过数值模拟和网络仿真验证了分析结果。实验结果表明，干扰范围可以提高泊松双极网络的SOC，而使泊松蜂窝网络和随机距离双极网络的SOC恶化。

引用次数: 0

Positionally restricted masked knowledge graph completion via multi-head mutual attention 基于多头相互关注的位置受限掩码知识图谱补全

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-03-22 DOI: 10.1016/j.jiixd.2025.02.006

Qiang Yu , Liang Bao , Peng Nie , Lei Zuo

Knowledge graph completion aims to enhance the completeness of knowledge graphs by predicting missing links. Link prediction is a common approach for this task, but existing methods, particularly those based on similarity computation, are often computationally expensive, especially for large models. To address this, we propose a novel method, positionally restricted masked knowledge graph completion (PR-MKGC), which reduces inference time by leveraging masked prediction and relying solely on structural information from the knowledge graph, without using textual data. We introduce a multi-head mutual attention mechanism that aggregates neighbor information more effectively, improving the model's ability to predict missing links. Experimental results demonstrate that PR-MKGC outperforms existing models in terms of both predictive performance and inference time on the FB15K-237 dataset.

知识图谱补全的目的是通过预测缺失环节来提高知识图谱的完备性。链接预测是该任务的常用方法，但是现有的方法，特别是基于相似性计算的方法，通常计算成本很高，特别是对于大型模型。为了解决这个问题，我们提出了一种新的方法，位置限制掩膜知识图补全（PR-MKGC），该方法通过利用掩膜预测和仅依赖知识图中的结构信息来减少推理时间，而不使用文本数据。我们引入了多头相互注意机制，更有效地聚合邻居信息，提高了模型预测缺失链接的能力。实验结果表明，PR-MKGC在FB15K-237数据集上的预测性能和推理时间都优于现有模型。

引用次数: 0

Channel computation based on multi-scale attention residual network 基于多尺度注意残差网络的信道计算

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-03-26 DOI: 10.1016/j.jiixd.2025.03.001

Wengang Li, Deli Zhou, Qiong Ye

Orthogonal time-frequency space (OTFS) modulation can effectively counter ICI in high-speed mobile scenarios, fully enhance the spectral efficiency of communication systems in high Doppler expansion scenarios, and improve the quality of communication systems. Channel estimation performance serves as a critical evaluation parameter within the OTFS modulation system. In this paper, we propose a multi-scale attention residual neural structure for improved channel estimation of OTFS waveforms in different satellite-ground scenario. Firstly, a multi-scale channel feature extraction module is designed, which applies multi-dimensional feature extraction to the channel matrix, thereby bolstering the capability to capture features at diverse scales. Subsequently, a self-attention mechanism is incorporated to concentrate on subtle yet significant features. The extracted features are then integrated and exploited through a residual convolutional architecture to derive an estimation of the channel matrix. Simulations are conducted using the satellite-ground mobile channel model outlined in 3GPP TR 38.811, with the NTN-TDL-C and NTN-TDL-B channel models representing line of sight (LoS) and non-line of sight (NLoS) conditions, respectively. Results demonstrate that the attention-based approach presented surpasses alternative neural network methodologies in terms of mean squared error (MSE), bit error rate (BER), and complexity, and meets the demands of OTFS channel estimation in satellite-ground scenario.

正交时频空间（OTFS）调制可以有效对抗高速移动场景下的ICI，充分增强通信系统在高多普勒扩展场景下的频谱效率，提高通信系统的质量。信道估计性能是OTFS调制系统的一个重要评价参数。本文提出了一种多尺度注意力残差神经网络结构，用于改进不同星地场景下OTFS波形的信道估计。首先，设计了多尺度通道特征提取模块，对通道矩阵进行了多维特征提取，增强了对不同尺度特征的捕获能力；随后，一个自我注意机制被纳入集中在细微但重要的特征。然后通过残差卷积架构对提取的特征进行集成和利用，以得出信道矩阵的估计。利用3GPP TR 38.811中概述的卫星-地面移动信道模型进行了仿真，其中NTN-TDL-C和NTN-TDL-B信道模型分别代表瞄准线（LoS）和非瞄准线（NLoS）条件。结果表明，该方法在均方误差（MSE）、误码率（BER）和复杂度方面均优于其他神经网络方法，能够满足星-地场景下OTFS信道估计的要求。

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引用次数: 0

METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation 度量：多重偏好学习与改进项目属性的多模式推荐

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-04-18 DOI: 10.1016/j.jiixd.2025.04.001

Yunfei Zhao , Jie Guo , Longyu Wen , Letian Wang

In recent years, there has been a burgeoning interest in multimodal recommender systems, which integrate various data types to achieve more personalized recommendations. Despite this, the effective incorporation of user preferences for multimodal data and the exploration of inherent semantic relationships between modalities still need to be explored. Prior research typically utilizes multimodal data to construct item graphs, often overlooking the nuanced details within the data. As a result, these studies fail to thoroughly examine the semantic relationships between items and user behavioral patterns. Our proposed approach, METRIC, addresses this gap by delving deeper into multimodal information. METRIC consists of two primary modules: the multiple preference modelling (MPM) module and the item semantic enhancement (ISE) module. The ISE module performs relational mining across multiple attributes, leveraging the semantic structural relationships inherent in items. In contrast, the MPM module enables users to articulate their preferences across different modalities and facilitates adaptive fusion through an attention mechanism. This approach not only improves precision in capturing user preferences and interests but also minimizes interference from varying modalities. Our extensive experiments on three benchmark datasets substantiate METRIC's superiority and the efficacy of its core components.

近年来，人们对多模式推荐系统产生了浓厚的兴趣，多模式推荐系统集成了各种数据类型，以实现更个性化的推荐。尽管如此，有效地整合用户对多模态数据的偏好和探索模态之间固有的语义关系仍然需要探索。先前的研究通常利用多模态数据来构建项目图，往往忽略了数据中细微的细节。因此，这些研究未能彻底检查项目与用户行为模式之间的语义关系。我们提出的方法METRIC通过深入研究多模态信息来解决这一差距。METRIC由两个主要模块组成：多偏好建模（MPM）模块和项目语义增强（ISE）模块。ISE模块跨多个属性执行关系挖掘，利用项目中固有的语义结构关系。相比之下，MPM模块使用户能够在不同的模式中表达自己的偏好，并通过注意机制促进自适应融合。这种方法不仅提高了捕获用户偏好和兴趣的精度，而且最大限度地减少了来自不同模式的干扰。我们在三个基准数据集上的广泛实验证实了METRIC的优越性及其核心组件的有效性。

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引用次数: 0

Rethink delay Doppler channels and time-frequency coding 重新考虑延迟多普勒信道和时频编码

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-03-25 DOI: 10.1016/j.jiixd.2025.02.002

Xiang-Gen Xia

In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes (including the current active VOFDM/OTFS) can compensate a non-trivial Doppler spread well. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. TF coding is equivalent to space-time coding in the math part. We also summarize state of the art on space-time coding that was an active research topic over a decade ago.

本文重新考虑了延迟多普勒信道（也称为双选择信道）。我们证明了任何调制方案（包括当前有源VOFDM/OTFS）都不能很好地补偿非平凡的多普勒扩频。然后讨论了一些处理时变信道的现有方法，特别是OFDM系统中的时频（TF）编码。TF编码在数学部分相当于空时编码。我们还总结了十多年前一个活跃的研究课题——时空编码的最新进展。

引用次数: 0

An efficient machine learning-enhanced DTCO framework for low-power and high-performance circuit design 一种高效的机器学习增强DTCO框架，用于低功耗和高性能电路设计

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-03-10 DOI: 10.1016/j.jiixd.2025.02.001

Mingyang Liu , Zhengguang Tang , Hailong You , Cong Li , Guangxin Guo , Zeyuan Wang , Linying Zhang , Xingming Liu , Yu Wang , Yong Dai , Geng Bai , Xiaoling Lin

The standard design technology co-optimization (DTCO) involves frequent interactions between circuit design and process manufacturing, which requires several months. To assist designers in establishing a bridge between device parameters and circuit metrics efficiently, and provide guidance for parameter optimization in the early stages of circuit design. In this paper, we propose an efficient machine learning (ML)-enhanced DTCO framework. This framework achieves the co-optimization of device parameters and circuit metrics. We select the gate metal work function (WF) as the parameter to validate the effectiveness of our framework. And the ridge regression approach is used to bypass TCAD simulation, compact model extraction and cell library characterization. We reduces time consumption by at least 92% compared to traditional DTCO framework, while ensuring that errors of delay, internal power consumption and leakage power below 4 ps, 0.035 mJ, and 0.4 μW, respectively. By adjusting the WF, we achieved a better balance between circuit delay and power consumption. This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.

标准设计技术协同优化（DTCO）涉及电路设计和工艺制造之间的频繁交互，需要数月的时间。协助设计人员有效地在器件参数和电路指标之间建立桥梁，为电路设计初期的参数优化提供指导。在本文中，我们提出了一个高效的机器学习（ML）增强的DTCO框架。该框架实现了器件参数和电路指标的协同优化。我们选择闸门金属功函数（WF）作为参数来验证框架的有效性。脊回归方法可以绕过TCAD仿真、紧凑模型提取和细胞库表征。与传统的DTCO框架相比，我们将时间消耗降低了至少92%，同时确保延迟、内部功耗和泄漏功率的误差分别低于4 ps、0.035 mJ和0.4 μW。通过调整WF，我们在电路延迟和功耗之间取得了更好的平衡。这项工作有助于设计师探索更广阔的设计空间，实现高效的DTCO流程。

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引用次数: 0

Resource allocation for coexistence of eMBB and bursty URLLC based on queueing with preemption 基于抢占排队的eMBB和突发URLLC共存资源分配

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-04-10 DOI: 10.1016/j.jiixd.2025.03.003

Wei Guo , Kai Liang , Yuewen Song , Xiaoli Chu , Gan Zheng , Kai-Kit Wong

Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) are two critical services in 5G mobile networks. While there has been extensive research on their coexistence, few studies have considered the impact of bursty URLLC on their coexistence performance. In this paper, we propose a method to allocate computing and radio resources for coexisting eMBB and bursty URLLC services by preempting both computing queues in the base station (BS) and time-frequency resources at the air interface. Specifically, we first divide the computing resources at the BS into a shared part for both URLLC and eMBB users and an exclusive part only for eMBB users, and propose a queuing mechanism with preemptive-resume priority for accessing the shared computing resources. Furthermore, we propose a preemptive puncturing method and a threshold-based queuing mechanism in the air interface to enable the multiplexing of eMBB and URLLC on shared time-frequency resources. We analytically derive the average queuing delay, average computation delay, and average transmission delay of eMBB and URLLC packets. Based on this analysis, we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation, the URLLC subcarrier scheduling and the computing resource allocation. We decompose this problem into three sub-problems and solve them alternately using a block coordinate descent algorithm. Numerical results show that our proposed method reduces the outage probability and average delay of URLLC compared to the existing works.

增强型移动宽带（eMBB）和超可靠低延迟通信（URLLC）是5G移动网络中的两项关键业务。虽然对它们的共存进行了大量的研究，但很少有研究考虑突发URLLC对它们共存性能的影响。本文提出了一种通过抢占基站（BS）的计算队列和空中接口的时频资源，为同时存在的eMBB和突发URLLC业务分配计算和无线电资源的方法。具体而言，我们首先将BS的计算资源划分为URLLC和eMBB用户共享的部分和eMBB用户独占的部分，并提出了一种具有抢占-恢复优先级的访问共享计算资源的排队机制。此外，我们提出了一种先发制人的穿刺方法和基于阈值的空中接口排队机制，以实现eMBB和URLLC在共享时频资源上的复用。分析了eMBB和URLLC包的平均排队延迟、平均计算延迟和平均传输延迟。在此基础上，通过对eMBB子载波分配、URLLC子载波调度和计算资源分配进行联合优化，提出了在满足eMBB平均时延和吞吐量要求的同时最小化URLLC数据包平均时延的混合整数非线性规划问题。我们将该问题分解为三个子问题，并使用块坐标下降算法交替求解。数值计算结果表明，与现有工程相比，该方法降低了URLLC的中断概率和平均延迟。

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引用次数: 0

Multimodal emotion recognition method in complex dynamic scenes 复杂动态场景中的多模态情感识别方法

Journal of Information and Intelligence

Pub Date : 2025-05-01 Epub Date: 2025-03-24 DOI: 10.1016/j.jiixd.2025.02.004

Long Liu , Qingquan Luo , Wenbo Zhang , Mengxuan Zhang , Bowen Zhai

Multimodal emotion recognition technology leverages the power of deep learning to address advanced visual and emotional tasks. While generic deep networks can handle simple emotion recognition tasks, their generalization capability in complex and noisy environments, such as multi-scene outdoor settings, remains limited. To overcome these challenges, this paper proposes a novel multimodal emotion recognition framework. First, we develop a robust network architecture based on the T5-small model, designed for dynamic-static fusion in complex scenarios, effectively mitigating the impact of noise. Second, we introduce a dynamic-static cross fusion network (D-SCFN) to enhance the integration and extraction of dynamic and static information, embedding it seamlessly within the T5 framework. Finally, we design and evaluate three distinct multi-task analysis frameworks to explore dependencies among tasks. The experimental results demonstrate that our model significantly outperforms other existing models, showcasing exceptional stability and remarkable adaptability to complex and dynamic scenarios.

多模态情感识别技术利用深度学习的力量来解决高级视觉和情感任务。虽然通用深度网络可以处理简单的情绪识别任务，但它们在复杂和嘈杂环境（如多场景户外环境）中的泛化能力仍然有限。为了克服这些挑战，本文提出了一种新的多模态情感识别框架。首先，我们开发了基于T5-small模型的鲁棒网络架构，设计用于复杂场景下的动态-静态融合，有效减轻噪声的影响。其次，我们引入了一种动态-静态交叉融合网络（D-SCFN）来增强动态和静态信息的集成和提取，并将其无缝嵌入到T5框架中。最后，我们设计并评估了三个不同的多任务分析框架，以探索任务之间的依赖关系。实验结果表明，我们的模型明显优于其他现有模型，表现出优异的稳定性和对复杂和动态场景的卓越适应性。

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引用次数: 0

Automated data processing and feature engineering for deep learning and big data applications: A survey 用于深度学习和大数据应用的自动数据处理和特征工程：一项调查

Journal of Information and Intelligence

Pub Date : 2025-03-01 Epub Date: 2024-01-08 DOI: 10.1016/j.jiixd.2024.01.002

Alhassan Mumuni , Fuseini Mumuni

Modern approach to artificial intelligence (AI) aims to design algorithms that learn directly from data. This approach has achieved impressive results and has contributed significantly to the progress of AI, particularly in the sphere of supervised deep learning. It has also simplified the design of machine learning systems as the learning process is highly automated. However, not all data processing tasks in conventional deep learning pipelines have been automated. In most cases data has to be manually collected, preprocessed and further extended through data augmentation before they can be effective for training. Recently, special techniques for automating these tasks have emerged. The automation of data processing tasks is driven by the need to utilize large volumes of complex, heterogeneous data for machine learning and big data applications. Today, end-to-end automated data processing systems based on automated machine learning (AutoML) techniques are capable of taking raw data and transforming them into useful features for big data tasks by automating all intermediate processing stages. In this work, we present a thorough review of approaches for automating data processing tasks in deep learning pipelines, including automated data preprocessing – e.g., data cleaning, labeling, missing data imputation, and categorical data encoding – as well as data augmentation (including synthetic data generation using generative AI methods) and feature engineering – specifically, automated feature extraction, feature construction and feature selection. In addition to automating specific data processing tasks, we discuss the use of AutoML methods and tools to simultaneously optimize all stages of the machine learning pipeline.

人工智能（AI）的现代方法旨在设计直接从数据中学习的算法。这种方法取得了令人印象深刻的结果，并对人工智能的进步做出了重大贡献，特别是在监督深度学习领域。它还简化了机器学习系统的设计，因为学习过程高度自动化。然而，并非传统深度学习管道中的所有数据处理任务都已实现自动化。在大多数情况下，数据必须手动收集、预处理，并通过数据增强进一步扩展，才能有效地用于训练。最近，出现了自动化这些任务的特殊技术。数据处理任务的自动化是由机器学习和大数据应用需要利用大量复杂的异构数据驱动的。如今，基于自动机器学习（AutoML）技术的端到端自动化数据处理系统能够通过自动化所有中间处理阶段，将原始数据转化为大数据任务的有用特征。在这项工作中，我们全面回顾了深度学习管道中自动化数据处理任务的方法，包括自动数据预处理-例如，数据清洗，标记，缺失数据输入和分类数据编码-以及数据增强（包括使用生成式人工智能方法生成合成数据）和特征工程-特别是自动特征提取，特征构建和特征选择。除了自动化特定的数据处理任务外，我们还讨论了使用AutoML方法和工具来同时优化机器学习管道的所有阶段。

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