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Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks
Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, L-1855, Luxembourg. (Signal Processing)ORCID iD: 0000-0003-2298-6774
2017 (English)In: IEEE Transactions on Wireless Communications, ISSN 1536-1276, E-ISSN 1558-2248, Vol. 16, no 7, p. 4654-4668, article id 7924387Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 16, no 7, p. 4654-4668, article id 7924387
Keywords [en]
Bayes methods;Cognitive radio;Interference channels;Interference constraints;Sensors;Signal to noise ratio;Bayesian active learning;Cognitive radio;cutting plane methods;probabilistic bisection algorithm
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-258988DOI: 10.1109/TWC.2017.2701361ISI: 000405458300038Scopus ID: 2-s2.0-85029052489OAI: oai:DiVA.org:kth-258988DiVA, id: diva2:1350476
Note

QC 20190913

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-13Bibliographically approved

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