Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks

In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. The main idea is that the CRN probes the PU and subs...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 16; H. 7; S. 4654 - 4668
Hauptverfasser: Tsakmalis, Anestis, Chatzinotas, Symeon, Ottersten, Bjorn
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Active learning
Bayes methods
Bayesian active learning
Bayesian analysis
Cognitive radio
Computer simulation
cutting plane methods
Feedback
Interference
Interference channels
Interference constraints
Learning
probabilistic bisection algorithm
Probabilistic methods
Probability theory
Radio networks
Sensors
Signal to noise ratio
cutting plane methods
Bayesian active learning
Bayes methods
Interference channels
Sensors
probabilistic bisection algorithm
Cognitive radio
Interference constraints
Signal to noise ratio
ISSN:1536-1276, 1558-2248, 1558-2248
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Zusammenfassung:In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback is implicit channel state information of the PU link, indicating whether the probing-induced interference is harmful or not. The intelligence of this sequential probing process lies in the selection of the power levels of the secondary users, which aims to minimize the number of probing attempts, a clearly active learning (AL) procedure, and expectantly the overall PU QoS degradation. The enhancement introduced in this paper is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a multivariate Bayesian AL method. This technique is inspired by the probabilistic bisection algorithm and the deterministic cutting plane methods (CPMs). The optimality of this multivariate Bayesian AL method is proven and its effectiveness is demonstrated through numerical simulations. Computationally cheap CPM adaptations are also presented, which outperform existing AL methods.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1536-1276
1558-2248
1558-2248
DOI:10.1109/TWC.2017.2701361