Iterative Multidimensional Impulse Detectors for Communications Based on the Classical Diagnostic Methods

Channel bonding (CB) is a technique that enables a wireless link to combine channels and achieve higher data rates. In this paper, competition for efficient spectrum access among autonomous users with heterogeneous CB capabilities is considered. Specifically, we propose distributed and coordinated channel/bonding selection methods under signal-to-interference-plus-noise ratio (SINR) and collisionprotocol models. In our methods, users utilize only limited feedback to distributively arrive at CB selections that minimize their probability of conflict. The proposed method utilizes a novel channel quality metric, which is based on the ratio of noise power to the sum of interference and noise power. It is shown that CB can lead to higher data rates, and it is most beneficial when users have a high SINR. However, it is also shown that as the ratio of users to available channels increases, CB performance degrades. Our results show that under certain scenarios, the proposed coordinated and distributed channel/bonding selection schemes help users converge fast to conflict-free channel selections as compared to the other channel/bonding selection schemes. Moreover, the proposed schemes result in considerably superior performance to existing CB schemes in terms of network data rate.

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“…One way is to use receivers that (FCME) algorithm [28], which has been used in many measurement studies [29].…”

Section: A π Aut Methodsmentioning

confidence: 99%

Distributed and Coordinated Spectrum Access Methods for Heterogeneous Channel Bonding

Khan

Lehtomäki

Scott

et al. 2017

IEEE Trans. Cogn. Commun. Netw.

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“…Thus, |r i | 2 = ζ i follows a chi-squared distribution. Sample ζ i is decided to be an outlier if [11] …”

Section: Consecutive Mean Excision Algorithmsmentioning

confidence: 99%

“…is an estimator corresponding to the shape parameter (i.e., the average signal sample energy), and Q is the size of the so called clean set (in the first iteration called as an initial set) [11]. The value of the threshold parameter T is calculated based on the desired false alarm probability P FA,DES in the outlier-free case [11].…”

Section: Consecutive Mean Excision Algorithmsmentioning

confidence: 99%

“…The iteratively operating backward and forward consecutive mean excision (CME/FCME) algorithms [10], [11] are computationally simple but effective energy based diagnostic [12] outlier detection methods, so they can be used both in NB interference suppression and NB signal detection. In contrast to energy detector, the CME methods are blind in the sense that a priori knowledge about the noise level is not required.…”

Section: Introductionmentioning

confidence: 99%

“…Therein, the resulting SNR limits were not analyzed and the rules for choosing the optimal CME algorithm were not considered. It is known that the FCME algorithm outperforms the CME algorithm but it is also more complex [11]. However, in the case of very NB signals, the performance of the simpler CME algorithm is adequate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Limits of detection for the consecutive mean excision algorithms

Vartiainen¹,

Lehtomäki²,

Saarnisaari³

et al. 2010

Proceedings of the 5th International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

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Abstract-Iterative backward and forward consecutive mean excision (CME/FCME) algorithms are efficient in suppressing and detecting narrowband signals. Being computationally relatively simple, they can be applied for spectrum sensing in cognitive radios. In this paper, theoretical limits of detection for the CME algorithms are derived. These limits predict whether a signal is detectable or not. More specifically, resulting signalto-noise ratio (SNR) values at which the signals can be detected are presented. In addition, rules for choosing the optimal CME algorithm are conveyed. The validity of the presented limits of detection is confirmed with computer simulations.

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DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands

Reddy

Battula

Gopalani

et al. 2020

Int J Communication

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Summary Spectrum sensing is one of the critical tasks in a cognitive radio system that allows a secondary user to use the spectrum while the primary user does not use it. The energy detection (ED) sensing is one of the most common techniques to identify the unused portions in the spectrum bands. In ED, threshold plays a vital role in signal detection, and noise is one of the significant factors in threshold calculation. However, ED efficiency is degraded by the noise uncertainty phenomenon caused by the random changes in noise level. The adverse effects of noise uncertainty are reduced by changing its detection threshold dynamically to the noise circumstances encountered during each sensing period. In the proposed method, received random samples are arranged in M blocks, applied strong Pearson correlation to separate and estimate the variance from the noise samples. The enhanced dynamic noise variance‐based energy sensing is implemented in GNU radio processing blocks and tested on industrial, scientific, medical (ISM) 2.4 GHz frequency bands by using national instrument universal software radio peripheral (NI USRP‐2932) device. The experimental results of proposed energy detction mechanism are compared with existing sensing techniques.

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Iterative Multidimensional Impulse Detectors for Communications Based on the Classical Diagnostic Methods

Cited by 58 publications

References 10 publications

Distributed and Coordinated Spectrum Access Methods for Heterogeneous Channel Bonding

Distributed and Coordinated Spectrum Access Methods for Heterogeneous Channel Bonding

Limits of detection for the consecutive mean excision algorithms

DISCERN: Enhanced Dynamic noISe varianCe based EneRgy sensing for cognitive radio using USRP at Wi‐Fi bands

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