Unsupervised Clustering-Based Non-Coherent Detection for Molecular Communications

Liu, Shenghan; Wei, Zhuangkun; Li, Bin; Zhao, Chenglin

doi:10.1109/lcomm.2020.2985073

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Both detectors exploit the prior estimated symbol, and one can observe a good agreement between them [7]. Another research uses the unsupervised method to classify different transmitted symbols according to the characteristics of the received signals [13]. Note that it surpasses conventional model-based schemes in terms of error performance, except for the MLSD with perfect CIR knowledge.…”

Section: B Comparison With Model-based Schemesmentioning

confidence: 99%

Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods

Huang

Wei

et al. 2021

IEEE Commun. Mag.

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Multi-scale molecular communication (MC) employs the characteristics of information molecules for information exchange. The received signal in MC inevitably encounters severe inter-symbol interference and signal-dependent noise due to the stochastic diffusion mechanism. Focusing on the critical signal detection in MC, first this article reviews the commonly used model-based detectors, and exposes their limitations in practical implementation. Then, the emerging data-driven detectors that can make up for some deficiencies of the model-based detectors are presented. Despite the black-box nature of the data-driven detectors, the explainable artificial intelligence can be further investigated for the performance improvement of transparency and trust. I. INTRODUCTION Molecular communication (MC) is an emerging multi-scale communication paradigm that conveys messages via the patterns of molecules [1]. MC has been regarded as an alternative for conventional communication techniques in virtue of its unique transmission mechanism.For the nano-scale and micro-scale channels in vivo, the implementation of the conventional communication techniques is hindered for the following reasons. The electromagnetic (EM) wave based wireless communication has its constraint in wave generation. As the wavelength in operation is proportional to the antenna size, nanomachine requires the Tera-hertz band for communications, which is, however, still in its research infancy, let alone the micro-scale implementation. Besides, optical communication is not an appropriate choice due to complex cell/tissue obstacles in the channel. Finally, acoustic communication has an energy issue on such an extremely tiny scale. In addition to the implementation restrictions, the bio-compatibility issue of the above schemes has not been verified in the open literature. As for the macro-scale channels, the EM wave based communication has the propagation problem in metal and aqueous environments. Inspired by the biological systems, MC can remedy the flaws in the existing communication systems. For micro-scale channels, MC via diffusion (MCvD) is an energy-efficient signal transmission mechanism that uses information molecules from the communication environments, and no extra energy is required for the

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Section: B Comparison With Model-based Schemesmentioning

confidence: 99%

Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods

Huang

Wei

et al. 2021

IEEE Commun. Mag.

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“…Concentration-based Shift Keying (CSK) is one of the most prevalent modulation techniques used in MCvD systems with the lowest complexity [29], [30], as depicted in the left half of Fig. 3.…”

Section: Preliminaries and Modulation Techniques A Preliminariesmentioning

confidence: 99%

Channel Capacity Analysis of a Comprehensive Absorbing Receiver for Molecular Communication via Diffusion

Liu

Wei

Wang³

et al. 2020

IEEE Access

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“…Even many people still believe that if the township government has financial problems, they can only wait for the state to issue relevant policies to solve them, resulting in the financial risks at the lowest level not being given sufficient attention [11]. This thesis is based on the evaluation and prediction of township government finance in the current period from the perspective of the debt risk of township government and tries to provide useful ideas and ways to prevent and solve the financial risk of township government [12]. There are many different clustering algorithms, which differ in the criteria used to measure object similarity: distance, density, or statistical distribution; the most common and simplest clustering algorithm is K-means.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

Sun

2021

Journal of Sensors

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The data generated through telecommunication networks has grown exponentially in the last few years, and the resulting traffic data is unlikely to be processed and analyzed by manual style, especially detecting unintended traffic consumption from normal patterns remains an important issue. This area is critical because anomalies may lead to a reduction in network efficiency. The origin of these anomalies may be a technical problem in a cell or a fraudulent intrusion in the network. Usually, they need to be identified and fixed as soon as possible. Therefore, in order to identify these anomalies, data-driven systems using machine learning algorithms are developed with the aim from the raw data to identify and alert the occurrence of anomalies. Unsupervised learning methods can spontaneously describe the data structure and derive network patterns, which is effective for identifying unintended anomalous behavior and detecting new types of anomalies in a timely manner. In this paper, we use different unsupervised models to analyze traffic data in wireless networks, focusing on models that analyze traffic data combined with timeline information. The factor analysis method is used to derive the results of factor analysis, obtain the three major public factors and comprehensive factor scores, and combine the results with the BP neural network model to conduct a nonlinear simulation study on local governmental debt risk. A potential semantic analysis model based on Gaussian probability is presented and compared with other methods, and experimental results show that this model can provide a robust, over-the-top anomaly detection in a fully automated, data-driven solution.

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Unsupervised Clustering-Based Non-Coherent Detection for Molecular Communications

Cited by 11 publications

References 13 publications

Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods

Signal Detection for Molecular Communication: Model-Based vs. Data-Driven Methods

Channel Capacity Analysis of a Comprehensive Absorbing Receiver for Molecular Communication via Diffusion

Unsupervised Wireless Network Model‐Assisted Abnormal Warning Information in Government Management

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