Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times

Sengottuvelan, Senthilmurugan; Ansari, Junaid; Mähönen, Petri; Venkatesh, T. G.; Petrova, Marina

doi:10.1109/tmc.2016.2592917

Cited by 38 publications

(20 citation statements)

References 29 publications

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“…The inherent capability of the proposed IDI in isolating bursts from multiple concurrent IRNs opens up new opportunities for coexistence modeling and enhancement [8], [28]. Although devising a coexisting strategy is not in the scope of this work, we demonstrate IDI's effectiveness in extracting the traffic statistics of an interference traffic that is interweaved with other concurrent heterogeneous interference.…”

Section: A Real-time Estimation Of Interference Traffic Distributionmentioning

confidence: 84%

“…2) We bring the identification time (the time for detecting and processing an interference burst) to minimum, with respect to minimum interference-to-noise ratio (INR) and on-air-time (OAT) achievable with the employed COTS platform. 3) Apart from IDI, our solution provides a first such framework based on COTS hardware that allows onboard inference of the traffic distributions of concurrent heterogeneous IRNs, desired by coexistence solutions exploiting channel idle times [8]. 4) The proposed method, instead of flimsy and heuristic power threshold-based features, utilizes signal features with unrestrictive requirement in reference to actual noise floor.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

2019

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Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity tradeoff for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b/g/n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90 %-97 % burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.

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Section: A Real-time Estimation Of Interference Traffic Distributionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

2019

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“…For efficient spectrum utilization in CR networks, many channel selection mechanisms have been extensively studied in the existing literature [28][29][30][31][32][33]. Most of these mechanisms consider only the remaining idle duration of the channel.…”

Section: Introductionmentioning

confidence: 99%

“…A learning strategy for distributed channel selection in cognitive radio networks was proposed in [30], by which the QoS of competing SUs converges to their rank-optimal channels to avoid the collision on their own orthogonal channels. The authors of [31,32] performed channel ranking based on the channel state prediction, which is related to the duration of the channel availability. Aslam et al [33] proposed the dynamic channel selection and parameter adaptation scheme based on the genetic algorithm to provide better QoS for the CR such that the best channel can be selected in terms of the quality, the power, and the PU activity.…”

Section: Introductionmentioning

confidence: 99%

A Repeated Games-Based Secure Multiple-Channels Communications Scheme for Secondary Users with Randomly Attacking Eavesdroppers

Thien

Koo

2019

Applied Sciences

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The cognitive radio network (CRN) is vulnerable to various newly-arising attacks targeting the weaknesses of cognitive radio (CR) communication and networking. In this paper, we focus on improving the secrecy performance of CR communications in a decentralized, multiple-channel manner while various eavesdroppers (EVs) try to listen to their private information. By choosing the best channel, the secondary user (SU) aims at mitigating the effects of eavesdropping and other SUs that compete for the same channel. Accordingly, the problem of finding the best channel that maximizes the secrecy rate for the SU is formulated as the framework of multiple repeated games where both the SU and the EVs try to maximize their own performance. In this case, the secrecy rate of an SU is defined based on the expected rewards of the SUs and the EVs. In the paper, we propose a repeated games-based scheme that can provide the best channel for the SU to avoid eavesdropping attacks and also minimize interference from other SUs that compete for the same channel. The simulation results demonstrate that the proposed scheme can combat a physical layer attack from EVs quite well and can provide much better performance, in comparison with other conventional channel selection schemes.

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“…These key techniques can be designed properly based on PU SPU, which can be characterized by appropriate SPU statistics [8]. For example, the knowledge of the duty cycle (DC) can enhance spectrum sensing performance [9]- [11], spectrum management [12]- [15] and channel selection [16].…”

Section: Introductionmentioning

confidence: 99%

Design of Spectrum Usage Detection in Wideband Spectrum Measurements

Umebayashi

Yoshitaka²,

López-Benítez

et al. 2019

IEEE Access

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We investigate the design of signal processing in wideband spectrum usage (SPU) measurements for efficient and smart dynamic spectrum access (DSA). In particular, we focus on spectrum usage detection (SPUD) in the experimental measurements. The detection results can be exploited to estimate statistics of the SPU. An appropriate design of the SPUD depends on the actual SPU in the target frequency band. There is a broad range of wireless systems in a considered broad measurement frequency band, such as from 60MHz to 6GHz, therefore a general design framework in the measurement frequency band is desired. In the proposed design framework, we at first define two models in terms of the SPU and the SPUD process. In addition, the proposed design procedure determines the adequate choice of parameters for the SPUD model based on given parameters of the SPU model in the target frequency band. Numerical evaluation based on computer simulations shows the validity of the design framework and design procedure. Moreover, a modified duty cycle (DC) estimation method is proposed, which can remove bias errors caused by low time resolution in the SPUD. Numerical evaluation based on experimental measurements demonstrates the practicality of the detection framework and procedure proposed in this work.

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Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times

Cited by 38 publications

References 29 publications

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

Real-Time Interference Identification via Supervised Learning: Embedding Coexistence Awareness in IoT Devices

A Repeated Games-Based Secure Multiple-Channels Communications Scheme for Secondary Users with Randomly Attacking Eavesdroppers

Design of Spectrum Usage Detection in Wideband Spectrum Measurements

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