The implementation of spectrum sensing and spectrum allocation on cognitive radio

Rungsawang, Pitcha; Khawne, Amnach

doi:10.23919/icact.2017.7890206

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…In the provision of quality of service a dynamic scheduling is outlined in [6]. In [7] a spectrum sensing and allocation in cognitive radio is presented. The developed approach allows to use unlicensed user radio spectrum in dynamic approach of user mobility scenario.…”

Section: International Journal Of Intelligent Engineering and Systemsmentioning

confidence: 99%

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Arikatla¹,

Gavini²,

Nanjappa³

2018

IJIES

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Provision quality of service (QoS) is the need of coming generation networks. Wherein mechanism for the provision of quality of service has emerged in different directions, the effort on minimizing the broadcasting overhead in a setup process is observed to be high. The past development in resource allocation has focused on the optimization of quality of service in the communication process. However, not much emphasis is made on the signaling overhead for multicast signal propagation under dynamic network resource condition. The limitation is observed to be more predominant in dynamic network such as mobile Adhoc network. The link overhead under a dynamic resource variation impact on the channel allocation performance and hence introduces delay. This paper, present an approach of resource allocation under the dynamic network condition for 1-hop and Multihop broadcasting. The proposed approach optimizes the channel allocation by the self-detection of available channel due to additional secondary users and allocates a communication channel based on a sensing mechanism. Wherein, the conventional method uses a fixed level of available channel, the proposed approach gives the advantage of channel allocation with channel sensing under dynamic user condition. The proposed approach illustrates a higher success rate and minimization of average delay under a dynamic network condition.

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Section: International Journal Of Intelligent Engineering and Systemsmentioning

confidence: 99%

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Arikatla¹,

Gavini²,

Nanjappa³

2018

IJIES

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“…A widely studied form of CR is opportunistic spectrum access [25][26][27]. It is believed to be the most fundamental CR approach.…”

Section: Cognitive Radio Overviewmentioning

confidence: 99%

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

Gharib¹

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The emerge of Internet of Things (IoT) brings up revolutionary changes to the field of wireless communications. Providing connection to billions of IoT devices together with challenging IoT environments lead to the need of new technologies in order to fulfill IoT spectrum demands. Cognitive radio (CR) technology can be seen as one of the prominent solutions to the spectrum scarcity issue in IoT, where multiband cooperative spectrum sensing (CSS) is the key. In this thesis, we consider heterogeneous distributed learning-based CR networks (CRNs). Focus is given to learning approaches named as incremental, consensus, and diffusion. We perform a comparative study to investigate which one of them best fits cognitive IoT demands. Simulation results are performed illustrating potentials of diffusion and consensus algorithms and disadvantages of incremental for cognitive IoT systems. Lack of centralized control, increase in number of devices, and dynamic environments place a room for lots of challenges in the CSS process. Conventional CSS techniques have to be improved in order to fulfill sophisticated IoT requirements. One of the main challenges is cooperative secondary users' (SUs') scheduling to sense a subset of channels. To overcome the aforementioned challenge, in this thesis, we propose a novel multi-band CSS scheme, named as heterogeneous multi-band multiuser CSS (HM2CSS). The proposed scheme allows heterogeneous SUs to sense multiple channels and works by selecting cooperative SUs in two stages. Only SUs owning different information about channels are chosen to be cooperative. This is done by selecting leaders for each channel in the first stage and corresponding cooperative SUs in the second stage. Careful choice of leaders reflects the selection of cooperative SUs and hence, improves system performance. Then, diffusion learning algorithm is used to exchange locally sensed information among cooperative SUs for all channels. Further, the decision on the availability of channels is made. Extensive simulation results illustrate that the proposed HM2CSS scheme satisfies the IEEE 802.22 detection performance standard. Detection performance and CRN To my beloved family v Table of Contents vii List of Tables x List of Figures xi Nomenclature xiv List of Tables 4.1 Comparison of incremental, consensus, and diffusion-based learning approaches' characteristics. .

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Enhancing Cooperative Spectrum Sensing in Flying Cell Towers for Disaster Management Using Convolutional Neural Networks

Suriya

Sumithra

2019

EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing

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The implementation of spectrum sensing and spectrum allocation on cognitive radio

Cited by 4 publications

References 11 publications

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Optimal Channel Allocation for QoS Provisioning in Cognitive Multihop Radio Adhoc Network

Distributed Learning-Based Cooperative Spectrum Sensing for Cognitive Internet of Things Systems

Enhancing Cooperative Spectrum Sensing in Flying Cell Towers for Disaster Management Using Convolutional Neural Networks

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