Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

Tsakmalis, Anestis; Chatzinotas, Symeon; Ottersten, Björn

doi:10.1109/jstsp.2017.2785826

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…2) provides details of the robust solution using a bisection search method where the solution alternates iteratively in solving the problem. The complexity of bisection search in term of system parameters is

scriptO false(K^{3} N^{6} + M K N^{2} false)

[20].…”

Section: Robust Solutionmentioning

confidence: 99%

“…Integrating the MIMO technique in CRN can significantly improve the performance of a wireless communication system and open a new gate of applications and research in wireless communication. The full benefit of MIMO in CRN can be exploited when the channel state information (CSI) is perfectly gathered, thus much research has been done in this area such as [17–20]. In practice and due to the nature of wireless communication channels, the estimated CSI is subject to error, and much research attention has been given to its mitigation in wireless communication [21, 22].…”

Section: Introductionmentioning

confidence: 99%

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Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

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Achieving high quality of service (QOS) at the end‐users while maintaining the low interference power at the primary users is the main goal in underlay cognitive radio networks. This goal becomes a more difficult task in the designing of beamforming vectors where all channels state information (CSIs) in the secondary network, as well as the interference CSIs, are uncertain. This task is addressed in this study using an iterative optimization technique. In this technique, the original CSI problem, which is difficult to solve as a single optimization problem, is instead separated into two sub‐problems. The first subproblem is the interference power problem, which can be solved either sub‐optimally or optimally using Lagrange duality. The second sub‐problem is the QOS problem, which can be solved either sub‐optimally or robustly using non‐monotone spectral projected gradient method. The two sub‐problem solutions are then recombined into a single problem to extract beamforming vectors. Two methods are invoked to extract the beamforming vectors: either the successive convex approximation (SCA) method or the bisection search method. Theoretical analysis and simulation results indicate that the two methods can offer a tradeoff between better QOS (using bisection search method) or less computational complexity (using SCA method)

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scriptO false(K^{3} N^{6} + M K N^{2} false)

[20].…”

Section: Robust Solutionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

et al. 2020

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“…To address this problem, Active Learning (AL) have been recently proposed, allowing the learning algorithms to dynamically query instances for labeling. AL has been applied in [10,11] to learn the interference channel between the PU and SU in an underlay CRN. In addition, PUs behavior is not static, i.e.…”

Section: Introductionmentioning

confidence: 99%

Margin-Based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks

Kumar

Lagunas

Sharma

et al. 2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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In this paper, we consider a problem of acquiring accurate spectrum availability information in the Cooperative Spectrum Sensing (CSS) based Cognitive Radio Networks (CRNs), where a fusion center collects the sensing information from all the sensing nodes within the network, analyzes the information and determines the spectrum availability. Although Machine Learning (ML) techniques have been recently applied to enhance the cooperative sensing performance in CRNs, they are mostly supervised learning based techniques and need a significant amount of labeled data, which is difficult to acquire in practice. Towards relaxing this requirement of large labeled data of supervised learning, we focus on Active Learning (AL), where the fusion center can query the label of the most uncertain cooperative sensing measurements. This is particularly relevant in CRN environments where primary user behavior changes in a quick manner. In this regard, we briefly review the existing AL techniques and adapt them to the considered CSS based CRNs. More importantly, we propose a novel margin based active on-line learning algorithm that selects the instance to be queried and updates the classifier by using the Stochastic Gradient Descent (SGD) technique. In this approach, whenever an unlabeled instance is presented, the proposed AL algorithm compares the margin of instance with a threshold to decide whether it should query a label or not. Supporting results based on numerical simulations show that the proposed method has significant advantages on classification and detection performances, and timecomplexity as compared to state-of-the-art techniques.

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Enabling federated learning of explainable AI models within beyond-5G/6G networks

Corcuera Bárcena,

Ducange,

Marcelloni

et al. 2023

Computer Communications

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Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

Cited by 7 publications

References 23 publications

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Robust underlay cognitive network download beamforming in multiple users, multiple groups multicell scenario

Margin-Based Active Online Learning Techniques for Cooperative Spectrum Sharing in CR Networks

Enabling federated learning of explainable AI models within beyond-5G/6G networks

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