Automatic Classification of Digitally Modulated Signals Based on K-Nearest Neighbor

Ahn, Woo-Hyun; Nah, Sun-Phil; Seo, Bo-Seok

doi:10.1007/978-94-017-9558-6_8

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…A KNN classifier calculates the distances of the test sample with respect to the class in the training sample [90]. In [102], eight digitally modulated signals, namely, 2FSK, 4FSK, MSK, BPSK, QPSK, 8PSK, 16QAM, and 64QAM, were classified using KNN. KNN has various disadvantages, such as nonparametric, lazy learner, cannot determine parameter k, and computationally greedy algorithm [12].…”

Section: Knnmentioning

confidence: 99%

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

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Section: Knnmentioning

confidence: 99%

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

et al. 2019

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“…-The most popular context-based data classification is k nearest neighbor classification (KNN). Given a test data point and a training set, we first search the training set to find the k nearest neighbors of the test data point to present its context, and then we determine its class label by a majority vote of the the labels of the context [2,3]. All the data points of the context contribute equally to the final classification result, and no representation procedure is needed.…”

Section: Related Workmentioning

confidence: 99%

Supervised learning of sparse context reconstruction coefficients for data representation and classification

Liu

Wang

Yin

et al. 2015

Neural Comput & Applic

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Context of data points, which is usually defined as the other data points in a data set, has been found to paly important roles in data representation and classification. In this paper, we study the problem of using context of a data point for its classification problem. Our work is inspired by the observation that actually only very few data points are critical in the context of a data point for its representation and classification. We propose to represent a data point as the sparse linear combination of its context, and learn the sparse context in a supervised way to increase its discriminative ability. To this end, we proposed a novel formulation for context learning, by modeling the learning of context parameter and classifier in a unified objective, and optimizing it with an alternative strategy in an iterative algorithm. Experiments on three benchmark data set show its advantage over state-of-the-art context-based data representation and classification methods.

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One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

2017

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Automatic Classification of Digitally Modulated Signals Based on K-Nearest Neighbor

Cited by 3 publications

References 4 publications

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Performance of Feature-Based Techniques for Automatic Digital Modulation Recognition and Classification—A Review

Supervised learning of sparse context reconstruction coefficients for data representation and classification

One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

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