ABSLearn: a GNN-based framework for aliasing and buffer-size information retrieval

Liang, Ke; Tan, Jim; Zeng, Dongrui; Huang, Yongzhe; Huang, Xiaolei; Tan, Gang

doi:10.1007/s10044-023-01142-2

Cited by 6 publications

(3 citation statements)

References 72 publications

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“…Traditional modulation identification methods are highly dependent on prior knowledge, have the disadvantages of high computational complexity and low identification accuracy, and are difficult to apply in practical problems (Xu et al , 2010). In recent years, deep learning has shined in the fields of natural language processing (Otter et al , 2020), computer vision (Ramezani et al , 2023), speech recognition (Weng et al , 2023) and information retrieval (Liang et al , 2023), and has gradually attracted attention from all walks of life. To overcome the inherent shortcomings of traditional modulation recognition algorithms, researchers in the field of wireless communications have also begun to apply deep learning ideas to the field of signal modulation recognition, which can often achieve better recognition performance.…”

Section: Introductionmentioning

confidence: 99%

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Qian,

Tu,

Hou

et al. 2024

IJWIS

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Purpose Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise. Design/methodology/approach This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise. Findings Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36. Originality/value At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

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Section: Introductionmentioning

confidence: 99%

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Qian,

Tu,

Hou

et al. 2024

IJWIS

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show abstract

“…W ITH the rapid development of information technology, data are often generated from different sources in realworld scenarios [1]- [4]. For instance, the same news can be described from different views, i.e., textual reports and visual pictures [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, how to effectively utilize information from different views is the key to enhancing the final clustering performance. Multi-view clustering (MVC), one of the most classical unsupervised tasks [9]- [13], has drawn increasing attention by effectively leveraging the complementary and consensus information from multiple views.…”

Section: Introductionmentioning

confidence: 99%

Continual Multi-view Clustering

Wan

Liu

Liang

et al. 2022

Proceedings of the 30th ACM International Conference on Multimedia

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Multi-view clustering (MVC) has gained broad attention owing to its capacity to exploit consistent and complementary information across views. This paper focuses on a challenging issue in MVC called the incomplete continual data problem (ICDP). In specific, most existing algorithms assume that views are available in advance and overlook the scenarios where data observations of views are accumulated over time. Due to privacy considerations or memory limitations, previous views cannot be stored in these situations. Some works are proposed to handle it, but all fail to address incomplete views. Such an incomplete continual data problem (ICDP) in MVC is tough to solve since incomplete information with continual data increases the difficulty of extracting consistent and complementary knowledge among views. We propose Fast Continual Multi-View Clustering with Incomplete Views (FCMVC-IV) to address it. Specifically, it maintains a consensus coefficient matrix and updates knowledge with the incoming incomplete view rather than storing and recomputing all the data matrices. Considering that the views are incomplete, the newly collected view might contain samples that have yet to appear; two indicator matrices and a rotation matrix are developed to match matrices with different dimensions. Besides, we design a three-step iterative algorithm to solve the resultant problem in linear complexity with proven convergence. Comprehensive experiments on various datasets show the superiority of FCMVC-IV.

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Semantic Constraint-Based Knowledge Representation Learning for Knowledge Graph Completion

Tang,

Hou

2024

2024 7th International Conference on Computer Information Science and Application Technology (CISAT)

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ABSLearn: a GNN-based framework for aliasing and buffer-size information retrieval

Cited by 6 publications

References 72 publications

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences

Continual Multi-view Clustering

Semantic Constraint-Based Knowledge Representation Learning for Knowledge Graph Completion

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