References of "Tsakmalis, Anestis 50003228"
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See detailConstrained bayesian active learning of linear classifier
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2018)

In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially ... [more ▼]

In this paper, an on-line interactive method is proposed for learning a linear classifier. This problem is studied within the Active Learning (AL) framework where the learning algorithm sequentially chooses unlabelled training samples and requests their class labels from an oracle in order to learn the classifier with the least queries to the oracle possible. Additionally' a constraint is introduced into this interactive learning process which limits the percentage of the samples from one “unwanted” class under a certain threshold. An optimal AL solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP) and its performance is demonstrated through numerical simulations. [less ▲]

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See detailConstrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Journal of Selected Topics in Signal Processing (2017)

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an ... [more ▼]

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. 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 indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work. [less ▲]

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See detailActive Learning in Cognitive Radio Networks
Tsakmalis, Anestis UL

Doctoral thesis (2017)

In this thesis, numerous Machine Learning (ML) applications for Cognitive Radios Networks (CRNs) are developed and presented which facilitate the e cient spectral coexistence of a legacy system, the ... [more ▼]

In this thesis, numerous Machine Learning (ML) applications for Cognitive Radios Networks (CRNs) are developed and presented which facilitate the e cient spectral coexistence of a legacy system, the Primary Users (PUs), and a CRN, the Secondary Users (SUs). One way to better exploit the capacity of the legacy system frequency band is to consider a coexistence scenario using underlay Cognitive Radio (CR) techniques, where SUs may transmit in the frequency band of the PU system as long as the induced to the PU interference is under a certain limit and thus does not harmfully a ect the legacy system operability. [less ▲]

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See detailInterference Constraint Active Learning with Uncertain Feedback for Cognitive Radio Networks
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Wireless Communications (2017), 16(7), 4654-4668

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 ... [more ▼]

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 probinginduced 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. [less ▲]

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See detailCentralized Power Control in Cognitive Radio Networks Using Modulation and Coding Classification Feedback
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Cognitive Communications and Networking (2016), 2(3),

In this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary ... [more ▼]

In this paper, a centralized Power Control (PC) scheme and an interference channel learning method are jointly tackled to allow a Cognitive Radio Network (CRN) access to the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The learning process enabler is a cooperative Modulation and Coding Classification (MCC) technique which estimates the Modulation and Coding scheme (MCS) of the PU. Due to the lack of cooperation between the PU and the CRN, the CRN exploits this multilevel MCC sensing feedback as implicit channel state information (CSI) of the PU link in order to constantly monitor the impact of the aggregated interference it causes. In this paper, an algorithm is developed for maximizing the CRN throughput (the PC optimization objective) and simultaneously learning how to mitigate PU interference (the optimization problem constraint) by using only the MCC information. Ideal approaches for this problem setting with high convergence rate are the cutting plane methods (CPM). Here, we focus on the analytic center cutting plane method (ACCPM) and the center of gravity cutting plane method (CGCPM) whose effectiveness in the proposed simultaneous PC and interference channel learning algorithm is demonstrated through numerical simulations. [less ▲]

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See detailActive Interference Constraint Learning with Uncertain Feedback for Cognitive Radio Networks
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Proceedings of IEEE International Conference on Communications (ICC) 2016 (2016)

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 ... [more ▼]

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) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is that the CRN probes the PU and subsequently applies a Modulation and Coding Classification (MCC) technique to acquire the Modulation and Coding scheme (MCS) of the PU. This feedback is an implicit channel state information (CSI) of the PU link, indicating how harmful the probing induced interference is. The intelligence of this sequential probing process lies on the selection of the power levels of the Secondary Users (SUs) which aims to minimize the number of probing attempts, a clearly Active Learning (AL) procedure, and consequently the overall PU QoS degradation. The enhancement introduced in this work is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a univariate Bayesian Nonparametric AL method, the Probabilistic Bisection Algorithm (PBA). An adaptation of the PBA is implemented for higher dimensions and its effectiveness as an uncertainty driven AL method is demonstrated through numerical simulations. [less ▲]

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See detailPower control in cognitive radio networks using cooperative modulation and coding classification
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (2015), 156

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU ... [more ▼]

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is the CR network to constantly probe the band of the PU with intelligently designed aggregated interference and sense whether the Modulation and Coding scheme (MCS) of the PU changes in order to learn the interference channels. The coordinated probing is engineered by the Cognitive Base Station (CBS), which assigns appropriate CR power levels in a binary search way. Subsequently, each CR applies a Modulation and Coding Classification (MCC) technique and sends the sensing information through a control channel to the CBS, where all the MCC information is combined using a fusion rule to acquire an MCS estimate of higher accuracy and monitor the probing impact to the PU MCS. After learning the normalized interference channel gains towards the PU, the CBS selects the CR power levels to maximize total CR network throughput while preserving the PU MCS and thus its QoS. The effectiveness of the proposed technique is demonstrated through numerical simulations. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2015. [less ▲]

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See detailPower Control in Cognitive Radio Networks Using Cooperative Modulation and Coding Classification
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Proceedings of 10th International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM) 2015 (2015)

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU ... [more ▼]

In this paper, a centralized Power Control (PC) scheme aided by interference channel gain learning is proposed to allow a Cognitive Radio (CR) network to access the frequency band of a Primary User (PU) operating based on an Adaptive Coding and Modulation (ACM) protocol. The main idea is the CR network to constantly probe the band of the PU with intelligently designed aggregated interference and sense whether the Modulation and Coding scheme (MCS) of the PU changes in order to learn the interference channel gains. The coordinated probing is engineered by the Cognitive Base Station (CBS), which assigns appropriate CR power levels in a binary search way. Subsequently, each CR applies a Modulation and Coding Classification (MCC) technique and sends the sensing information through a control channel to the CBS, where all the MCC information is combined using a fusion rule to acquire an MCS estimate of higher accuracy and monitor the probing impact to the PU MCS. After learning the normalized interference channel gains towards the PU, the CBS selects the CR power levels to maximize total CR network throughput while preserving the PU MCS and thus its QoS. The effectiveness of the proposed technique is demonstrated through numerical simulations. [less ▲]

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See detailModulation and Coding Classification for Adaptive Power Control in 5G Cognitive Communications
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Proceedings of IEEE International Workshop on Signal Processing Advances in Wireless Communications 2014 (2014)

Detailed reference viewed: 177 (15 UL)
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See detailAutomatic Modulation Classification for Adaptive Power Control in Cognitive Satellite Communications
Tsakmalis, Anestis UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Proceedings of 7th Advanced Satellite Multimedia Systems Conference (ASMS) and 13th Signal Processing for Space Communications Workshop (SPSC) 2014 (2014)

Detailed reference viewed: 225 (22 UL)