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- 2ND-ORDER CYCLOSTATIONARITY (1)
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- Blind modulation classification (1)
- Blind modulation classification; orthogonal frequency division multiplexing; higher-order cumulant and cyclic cumulant; maximum-likelihood; maximum a posteriori; deep learning; convolutional neural networks; probability of correct classification; testbed implementation; DEEP NEURAL-NETWORK; AUTOMATIC MODULATION; 2ND-ORDER CYCLOSTATIONARITY; IDENTIFICATION; ALGORITHM; IMPLEMENTATION; SYSTEMS; DESIGN; STREAM (1)
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- DESIGN (1)
- Deep learning (1)
- Higher-order cumulant and cyclic cumulant (1)
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- IMPLEMENTATION (1)
- Maximum a posteriori (1)
- Maximum-likelihood (1)
- Orthogonal frequency division multiplexing (1)
- Probability of correct classification (1)
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Articles 1 - 2 of 2
Full-Text Articles in Engineering
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Faculty Publications
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind …
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
A Survey Of Blind Modulation Classification Techniques For Ofdm Signals, Anand Kumar, Sudhan Majhi, Guan Gui, Hsiao-Chun Wu, Chau Yuen
Faculty Publications
Blind modulation classification (MC) is an integral part of designing an adaptive or intelligent transceiver for future wireless communications. Blind MC has several applications in the adaptive and automated systems of sixth generation (6G) communications to improve spectral efficiency and power efficiency, and reduce latency. It will become a integral part of intelligent software-defined radios (SDR) for future communication. In this paper, we provide various MC techniques for orthogonal frequency division multiplexing (OFDM) signals in a systematic way. We focus on the most widely used statistical and machine learning (ML) models and emphasize their advantages and limitations. The statistical-based blind …