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IEEE Transactions on Cognitive Communications and Networking is a peer-reviewed journal published by IEEE, covering Advanced MIMO Systems Optimization, Cognitive Radio Networks and Spectrum Sensing, Advanced Wireless Communication Technologies. Over its lifetime it has published 2,128 papers, which have received 39,584 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Cognitive Communications and Networking, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Cognitive Communications and Networking style. No sign-up. |
| Abbreviation (ISO-4) | TCCN |
| Publishes research in | Advanced MIMO Systems Optimization Cognitive Radio Networks and Spectrum Sensing Advanced Wireless Communication Technologies Wireless Signal Modulation Classification UAV Applications and Optimization |
| ISSN | 2332-7731 |
| Citation impact (2-yr) | 4.66 |
| h-index | 85 |
| i10-index | 756 |
| Top institutions publishing here | Beijing University of Posts and Telecommunications |
| Journal website | ieeexplore.ieee.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We present and discuss several novel applications of deep learning for the physical layer. By interpreting a communications system as an autoencoder, we develop a fundamental new way to think about communications system design as an end-to-end reconstruction task that seeks to jointly optimize transmitter and receiver components in a single process. We show how…
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which…
This paper looks into the modulation classification problem for a distributed wireless spectrum sensing network. First, a new data-driven model for automatic modulation classification based on long short term memory (LSTM) is proposed. The model learns from the time domain amplitude and phase information of the modulation schemes present in the training data without requiring…
Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modeling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It…
In this paper, we introduce a radar information metric, the estimation rate, that allows the radar user to be considered in a multiple-access channel enabling performance bounds for joint radar-communications coexistence to be derived. Traditionally, the two systems were isolated in one or multiple dimensions. We categorize new attempts at spectrum-space-time convergence as either coexistence,…
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