A Stochastic Geometry Analysis of Multichannel Cognitive Radio Networks

Rattaro, Claudina; Bermolen, Paola; Larroca, Federico; Belzarena, Pablo

doi:10.1145/2998373.2998450

Cited by 4 publications

(10 citation statements)

References 12 publications

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“…Moreover, an analytical model is formulated to measure the delay performance of the proposed scheme. In another study [5], authors considered geometric characteristics (i.e., path loss and random node positions) to analyze the performance of the MAC protocol for MCRNs. Similarly, a decentralized MAC protocol for MCRNs is also proposed in [6].…”

Section: Related Workmentioning

confidence: 99%

Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks

et al. 2018

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The increasing popularity of wireless services and devices necessitates high bandwidth requirements; however, spectrum resources are not only limited but also heavily underutilized. Multiple license channels that support the same levels of quality of service (QoS) are desirable to resolve the problems posed by the scarcity and inefficient use of spectrum resources in multi-channel cognitive radio networks (MCRNs). One reason is that multimedia services and applications have distinct, stringent QoS requirements. However, due to a lack of coordination between primary and secondary users, identifying the QoS levels supported over available licensed channels has proven to be problematic and has yet to be attempted. This paper presents a novel, Bayesian, non-parametric channel clustering scheme, which identifies the QoS levels supported over available license channels. The proposed scheme employs the infinite Gaussian mixture model and collapsed Gibbs sampler to identify the QoS levels from the feature space of the bitrate, packet delivery ratio, and packet delay variation of licensed channels. Moreover, the real measurements of wireless data traces and comparisons with baseline clustering schemes are used to evaluate the performance of the proposed scheme.Index Terms-Multi-channel cognitive radio networks, quality of service levels, multimedia transmissions, channel clustering.

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Section: Related Workmentioning

confidence: 99%

Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks

et al. 2018

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“…Because of the need to capture more characteristics of practical systems, the single-channel assumption is normally relaxed to consider the multichannel scenario in order to demonstrate or investigate the reliability of any proposed interference model under the multichannel scenario. For instance, the interference models derived for a single-channel scenario in [52] were extended to capture a multichannel case in [66]. When a multichannel scenario is considered, interchannel relationships can be properly captured in the interference analysis [7], [67].…”

Section: Spectrum Opportunity Detection Analysismentioning

confidence: 99%

“…Network throughput [7], [11], [17], [25], [52], [53], [56], [67], [69], [77]. Medium access probability [25], [52], [53], [66], [69]. Spectral efficiency [16] where λ p is the intensity of the active PUs.…”

Section: Point Processmentioning

confidence: 99%

Stochastic geometry approach towards interference management and control in cognitive radio network: A survey

Okegbile

Maharaj

Alfa

2021

Computer Communications

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“…In [3], [4], threshold-based opportunistic spectrum access was adopted to characterize spatial throughput and opportunity, while interference was characterized in two-tier cognitive networks by using the exclusion region in [5]. Similarly, interference and throughout modeling was carried out in multichannel CRN in [6], [7]. With the tool of SG, these works derived tractable analysis for various performance metrics of interest under full buffer assumption.…”

Section: A Related Workmentioning

confidence: 99%

“…A successful packet is removed from the ST's internal buffer and STs with empty buffers are removed from the secondary queue. The SINR received at any typical SR y s k located at the origin at any time slot t is also affected by interference from all active PTs and the neighbouring STs and can be given as (7), as shown at the bottom of this page.…”

Section: B Signal-to-noise Plus Interference Ratio Analysis In the Smentioning

confidence: 99%

Spatiotemporal Characterization of Users’ Experience in Massive Cognitive Radio Networks

2020

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The need to capture the actual network traffic condition and fundamental queueing dynamics in a massive cognitive radio network (CRN) is important for proper analysis of the intrinsic effects of spatial distribution while capturing the essential temporal distribution properties of the network. In massive CRN, many users, including primary and secondary users, transmit on scarce spectrum resources. While primary users (PUs) are delay-sensitive users that require prioritized access over secondary users (SUs), carrying out analysis that captures this property becomes imperative if users' service experience is to be satisfactory. This paper presents priority conscious spatiotemporal analysis capable of characterizing users' experience in massive CRN. Users in the primary priority queue were considered to have pre-emptive priory over users in the virtual and secondary priority queues. A Geo/G/1 discrete-time Markov chain queueing system was adopted to characterize both primary and secondary priority queues, while the virtual priority queue was analyzed as part of the secondary priority queue. Using the tools of stochastic geometry and queueing theory, the user's coverage probability was determined while the delay experienced by each class of users in the network was obtained using existing results. Through the obtained delay for each class of users in the network, the corresponding quality of service was also obtained. The results obtained show that the proposed framework is capable of accurately characterizing users' service experience in massive CRN.

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A Stochastic Geometry Analysis of Multichannel Cognitive Radio Networks

Cited by 4 publications

References 12 publications

Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks

Channel Clustering and QoS Level Identification Scheme for Multi-Channel Cognitive Radio Networks

Stochastic geometry approach towards interference management and control in cognitive radio network: A survey

Spatiotemporal Characterization of Users’ Experience in Massive Cognitive Radio Networks

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