Channel prediction-based channel allocation scheme for multichannel cognitive radio networks

Lee, Juhyeon; Park, Hyung-Kun

doi:10.1109/jcn.2014.000032

Cited by 28 publications

(16 citation statements)

References 10 publications

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“…In particular, the channel capacity-based (CCB) spectrum selection algorithm presented in [8] and the selective opportunistic spectrum access (SOSA) algorithm presented in [9] have been considered as representative techniques that address the same problem considered in this paper and enable the establishment of a comparison under the same set of assumptions. The details of the implementation of both algorithms in the simulation framework are given in Appendix 3.…”

Section: Benchmarkingmentioning

confidence: 99%

“…Based on [8], when the spectrum selection is triggered, this algorithm determines the SB to be allocated based on the expected time that each SB will remain in the idle state (i.e., state S (i) =0 in our framework) and on the bit rate achievable in each SB. For that purpose, the algorithm must observe all SBs in every time step to detect the time when each SB enters the state =0.…”

Section: A Channel Capacity-based (Ccb) Allocation Algorithmmentioning

confidence: 99%

“…For that purpose, the algorithm must observe all SBs in every time step to detect the time when each SB enters the state =0. Similarly, to have a fair comparison, the algorithm in [8] has been particularized to the case where only one channel can be allocated to one link. …”

Section: A Channel Capacity-based (Ccb) Allocation Algorithmmentioning

confidence: 99%

“…A multichannel spectrum selection algorithm that predicts the duration of the spectrum holes based on the prior statistics of the channels to limit the interference generated to a primary network was recently proposed in [8]. In [9], an opportunistic spectrum access scheme is presented based on estimating the probability of a channel appearing idle.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Belief-Based Decision-Making Framework for Spectrum Selection in Cognitive Radio Networks

Perez-Romero

Raschellà

Sallent

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-This paper presents a comprehensive cognitive management framework for spectrum selection in cognitive radio networks. The framework uses a belief vector concept as a means to predict the interference affecting the different spectrum blocks and relies on a smart analysis of the scenario dynamicity to properly determine an adequate observation strategy to balance the trade-off between achievable performance and measurement requirements. In this respect, the paper shows that the interference dynamics in a given spectrum block can be properly characterized through the second highest eigenvalue of the interference state transition matrix. Therefore, this indicator is retained in the proposed framework as a relevant parameter to drive the selection of both the observation strategy and spectrum selection decision-making criterion. The paper evaluates the proposed framework to illustrate the capability to properly choose among a set of possible observation strategies under different scenario conditions. Furthermore, a comparison against other state-of-the-art solutions is presented.

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Section: Benchmarkingmentioning

confidence: 99%

Section: A Channel Capacity-based (Ccb) Allocation Algorithmmentioning

confidence: 99%

Section: A Channel Capacity-based (Ccb) Allocation Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Belief-Based Decision-Making Framework for Spectrum Selection in Cognitive Radio Networks

Perez-Romero

Raschellà

Sallent

et al. 2016

IEEE Trans. Veh. Technol.

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“…In this review, we address both implicit and explicit formulations as statistical SOP models. Statistical SOP models proposed for spectrum occupancy analysis include Poisson processes [12,13], Bayesian prediction [9,14], and linear regression [15,16]. Machine learning-based techniques have also been proposed for model learning including neural networks, time regression, and space vector machines [5,17,18].…”

Section: Introductionmentioning

confidence: 99%

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

Eltom

Kandeepan

Evans

et al. 2018

J Wireless Com Network

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Spectrum scarcity due to inefficient utilisation has ignited a plethora of dynamic spectrum access solutions to accommodate the expanding demand for future wireless networks. Dynamic spectrum access systems allow secondary users to utilise spectrum bands owned by primary users if the resulting interference is kept below a pre-designated threshold. Primary and secondary user spectrum occupancy patterns determine if minimum interference and seamless communications can be guaranteed. Thus, spectrum occupancy prediction is a key component of an optimised dynamic spectrum access system. Spectrum occupancy prediction recently received significant attention in the wireless communications literature. Nevertheless, a single consolidated literature source on statistical spectrum occupancy prediction is not yet available in the open literature. Our main contribution in this paper is to provide a statistical prediction classification framework to categorise and assess current spectrum occupancy models. An overview of statistical sequential prediction is presented first. This statistical background is used to analyse current techniques for spectrum occupancy prediction. This review also extends spectrum occupancy prediction to include cooperative prediction. Finally, theoretical and implementation challenges are discussed.

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CCRA: channel criticality based resource allocation in cognitive radio networks

Kumar

Minz

2015

Int J Communication

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In cognitive radio networks, signal to interference plus noise ratio (SINR) is a quantity that is used to analyze the bounds of the capacity of a channel. This is the reason for SINR being one of the important parameters toward evaluating the performance of spectrum sharing in every network. To maximize the channel utilization in any network, the SINR of a channel should be considered to be within a threshold. This also leads to lesser power consumption, and the quality of service for the licensed users is maintained. Further, reduced SINR leads to an improvement in the quality of communication in the network. In this paper, a graph theoretic measure for the efficient utilization of channels in cognitive radio networks has been proposed and is named as channel criticality based resource allocation (CCRA). Using the SINR as the weight of the graph, a novel concept of channel criticality has also been introduced in this work. The proposed CCRA technique has also been compared with the existing interference aware channel assignment (IACA) technique in terms of the channel utilization. Through simulations, the CCRA has been observed to outperform the IACA scheme. The average channel utilization of the proposed CCRA was observed to have increased by 8%, when the secondary users were introduced in the network as compared with the IACA technique. Copyright Int. J. Commun. Syst. 2017; 30: e3012 V. KUMAR AND S. MINZThe betweenness of the link k for the source and destination pair .a; b/ is given by of 12 6

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Channel prediction-based channel allocation scheme for multichannel cognitive radio networks

Cited by 28 publications

References 10 publications

A Belief-Based Decision-Making Framework for Spectrum Selection in Cognitive Radio Networks

A Belief-Based Decision-Making Framework for Spectrum Selection in Cognitive Radio Networks

Statistical spectrum occupancy prediction for dynamic spectrum access: a classification

CCRA: channel criticality based resource allocation in cognitive radio networks

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