Binary Inference for Primary User Separation in Cognitive Radio Networks

Nguyen, Huy; Zheng, Guanbo; Zheng, Rong; Han, Zhu

doi:10.1109/twc.2013.022213.112260

Cited by 19 publications

(12 citation statements)

References 28 publications

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“…More explicitly, a substantial security enhancement was achieved by a robust version of the PCA-based method, which exploited the sparse, low-rank nature of the auto-covariance matrices of the smart metering signal and of the wideband interferer, respectively, in order to confidently separate them prior to ICA processing. Another pertinent example is found in cognitive radio scenarios, where the so called Boolean ICA relied on the Boolean mixing of OR, XOR, and other functions of binary signals [12]. It was also incorporated into the PU separation problem often encountered in cognitive radio networks for the sake of distinguishing and characterizing the activities of PUs in the context of collaborative spectrum sensing.…”

Section: K-means Clustering: Heterogeneous Networkmentioning

confidence: 99%

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Machine Learning Paradigms for Next-Generation Wireless Networks

Jiang¹,

Zhang

Ren

et al. 2017

IEEE Wireless Commun.

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992

496

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Next-generation wireless networks are expected to support extremely high data rates and radically new applications, which require a new wireless radio technology paradigm. The challenge is that of assisting the radio in intelligent adaptive learning and decision making, so that the diverse requirements of next-generation wireless networks can be satisfied. Machine learning is one of the most promising artificial intelligence tools, conceived to support smart radio terminals. Future smart 5G mobile terminals are expected to autonomously access the most meritorious spectral bands with the aid of sophisticated spectral efficiency learning and inference, in order to control the transmission power, while relying on energy efficiency learning/inference and simultaneously adjusting the transmission protocols with the aid of quality of service learning/inference. Hence we briefly review the rudimentary concepts of machine learning and propose their employment in the compelling applications of 5G networks, including cognitive radios, massive MIMOs, femto/small cells, heterogeneous networks, smart grid, energy harvesting, device-todevice communications, and so on. Our goal is to assist the readers in refining the motivation, problem formulation, and methodology of powerful machine learning algorithms in the context of future networks in order to tap into hitherto unexplored applications and services.

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Section: K-means Clustering: Heterogeneous Networkmentioning

confidence: 99%

“…In [14] the authors presented a heterogeneous fully distributed multi-objective strategy based on a reinforcement learning model con- Spectrum learning in cognitive radio [12] figure 3. Illustration of reinforcement learning: a) Markov decision process; b) partially observed Markov decision process; c) Q-learning.…”

Section: Q-learning: Femto/small Cellsmentioning

confidence: 99%

Machine Learning Paradigms for Next-Generation Wireless Networks

Jiang¹,

Zhang

Ren

et al. 2017

IEEE Wireless Commun.

Self Cite

992

496

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“…Hence its cancellation requires at least 120 dB interference rejection. Furthermore, in [265], the Boolean ICA concept was proposed based on the integration of Boolean functions of binary signals for inferring the activities of the underlying latent signal sources. Specifically, it was shown that given m SUs, the activities of up to (2m − 1) PUs can be determined.…”

Section: a N M ] T Furthermore Letmentioning

confidence: 99%

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Wang

Jiang

Zhang

et al. 2020

IEEE Commun. Surv. Tutorials

456

200

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Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (Het-Nets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.

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“…The errors in the noisy cases can be attributed to the fact that the average PU active probability is around 2%, which is comparable to the noise level. More information on this application can be found in [22].…”

Section: Number Of Pus Prediction Error On Y (%)mentioning

confidence: 99%

Binary Independent Component Analysis With or Mixtures

Nguyen

Zheng

2011

IEEE Trans. Signal Process.

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Abstract-Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals. The classical Independent Components Analysis (ICA) framework usually assumes linear combinations of independent sources over the field of realvalued numbers R. In this paper, we investigate binary ICA for OR mixtures (bICA), which can find applications in many domains including medical diagnosis, multi-cluster assignment, Internet tomography and network resource management. We prove that bICA is uniquely identifiable under the disjunctive generation model, and propose a deterministic iterative algorithm to determine the distribution of the latent random variables and the mixing matrix. The inverse problem concerning inferring the values of latent variables are also considered along with noisy measurements. We conduct an extensive simulation study to verify the effectiveness of the propose algorithm and present examples of real-world applications where bICA can be applied.

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Binary Inference for Primary User Separation in Cognitive Radio Networks

Cited by 19 publications

References 28 publications

Machine Learning Paradigms for Next-Generation Wireless Networks

Machine Learning Paradigms for Next-Generation Wireless Networks

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Binary Independent Component Analysis With or Mixtures

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