A Survey on Machine-Learning Techniques in Cognitive Radios

By Mario Bkassiny, Yang Li, Sudharman K. Jayaweera. IEEE Communications Surveys and Tutorials, 2013

544 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/73b51b02a061e2eae2eebe2ceae45872ea7d509d

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In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

An Introduction to Deep Learning for the Physical Layer (2017, background): “These comprise channel modeling and prediction, localization, equalization, decoding, quantization, compression, demodulation, modulation recognition, and spectrum sensing, to name a few [19], [20] (and references therein).”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “Moreover, the characterization of learning problems in cognitive radios and the importance of ML in achieving full cognitive networks has been discussed in [114].”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “Though there are various surveys on ML in networking [18, 61, 82, 142, 246, 339], this survey is purposefully different.”
An Overview on Application of Machine Learning Techniques in Optical Networks (2018, background): “Some applications of ML in different networking areas have already matched these expectations in areas such as intrusion detection [2], traffic classification [3], cognitive radios [4].”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, methodology): “The traditional feature-based AMC algorithms [3], [4] can be realized by three steps: data preprocessing, feature extraction and classificatory decision.”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “nd deep CNN were combined to avoid jamming signals for cognitive radios that increase RL performance. Cognitive radio (CR) devices have dynamic changing capabilities according to working environments [143]. Recently, ML-based a new centralized scheme was proposed in [144] for the security of IoT dev”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, background): “Contribution of the paper: To the best of our knowledge, there does not exist a survey that specifically focuses on the important applications of unsupervised ML techniques in networks, even though a number of surveys exist that focus on specific ML applications pertaining to networking—for instance”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, background): “However, differently from [22] and [23], which surveyed”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “, transmit power, coding scheme, modulation scheme, sensing algorithm, communication protocol, and sensing policy), and no simple formula may be able to determine these setup parameters simultaneously [76].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Intelligent mobile networking is becoming a popular research area and related work has been reviewed in [7], [34], [37], [54], and [56]–[59].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “. Kulkarni et al. [25] surveyed some common issues of WSNs solved by computational intelligence algorithms, such as data fusion, routing, task scheduling, localization, etc. Moreover, Bkassiny et al. [26] investigated decision-making and feature classiﬁcation problems solved by both centralized and ”
Cognitive Radio for Smart Grids: Survey of Architectures, Spectrum Sensing Mechanisms, and Networking Protocols (2016, background): “This local access point is further connected to a control center for storage, processing, and management of meter data [108], [109].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “rich spectrum of available of AI and machine learning frameworks. Second, they mostly restrict their scope to a single wireless application such as sensor networks [55]–[57], cognitive radio networks [53], machine-to-machine (M2M) communication [4], or physical layer design [32] and, hence, they do ”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, background): “[8] have studied several challenging learning problems in Cognitive Radio Networks (CRNs), and surveyed existing ML-based methods to address them.”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “strict quality-of-service requirements, and studies on learning related to IoT are also reviewed in [16]. The applications of machine learning in cognitive radio (CR) environments are investigated in [17] and [18]. Speciﬁcally, the author in [17] classiﬁes those applications into decision-making tas”
Deep Learning Based Communication Over the Air (2017, background): “Although researchers have tried to address communications-related problems with ML in the past (see [8], [9], [10] and references therein), it did not have any fundamental impact on the way we design and implement communications systems.”
Cognitive Radio Wireless Sensor Networks: Applications, Challenges and Research Trends (2013, background): “Some issues in CRNs have been addressed well [18–33].”