Data-Centric Prioritization in a Cognitive Radio Network: A Quality-of-Service Based Design and Integration

Wiggins, Urban Terrell; Kannan, Rajgopal; Chakravarthy, V J; Vasilakos, Athanasios V.

doi:10.1109/dyspan.2008.62

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…Inspired by the work presented in [19] which introduces the concept of association of application specific requirements employed with the network dynamics of the frequency spectrum is driven to follow a QoS methodology. This was made possible by a new paradigm, Data Centric Prioritization (DCP), the enhancement of the unique union between the type of application data being transmitted and a "true best fit" cognitive radio frequency decision in a cognitive radio community cluster.…”

Section: Application Data Types Used During Transmissionmentioning

confidence: 99%

An AHP-TOPSIS Integrated Model for QoS-Aware Energy Efficiency in Green Cognitive Radio Networks

Bendella¹,

Benmammar²,

Krief³

2022

RIA

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The selection of the best spectrum band available to meet the quality of service requirements of secondary users without interfering with the transmission of the primary users is considered as a challenge in cognitive radio networks. On the other side, green network is a recent concept that refers to the processes used to optimize a network in order to make it more energy efficient. In this paper, we propose a new algorithm ensuring the selection of the best available spectrum satisfying the demands of the secondary users based on TOPSIS and AHP in OFDM-based cognitive radio networks. We will assess the need for the secondary users in terms of quality of service and energy efficiency, by analyzing the characteristics of the available channels and taking into account the interference generated with the presence of the primary user. It efficiently manages energy because it allows a significant reduction of the transmission power used by the secondary user and therefore presents an effective solution in green networking.

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Section: Application Data Types Used During Transmissionmentioning

confidence: 99%

An AHP-TOPSIS Integrated Model for QoS-Aware Energy Efficiency in Green Cognitive Radio Networks

Bendella¹,

Benmammar²,

Krief³

2022

RIA

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“…Although the effect of collision probability is included, the results are only valid when the SU groups have the same connection length. The prioritisation among SU traffic from a physical layer and network integration perspective have been studied in Wiggins et al (2008); Gosh et al (2009). Moreover, as identified in Tumuluru et al ( 2011), these works do not include the effect of spectrum handoff.…”

Section: Reactive Spectrum Handoffmentioning

confidence: 99%

Foundation of Cognitive Radio Systems

Cheng¹

2012

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Chapter 6, the second chapter of this part, the authors look into the possibility of improving spectrum sensing performance by exploiting multiuser diversity. They also describe a practical MAC protocol to coordinate transmissions between SU and a fusion center that handles fusion of sensing information.The remaining two parts deal with the aspects of spectrum management. Part 3 includes chapters that layout the foundation of interference management. Chapter 7, the first chapter of Part 3, introduces partial response signaling as a tool for spectrum shaping in cognitive radio. It addresses the potential weakness of having large out-ofband spectrum components in conventional OFDM based systems. In Chapter 8, the second chapter of Part 3, the authors discuss the structure, reconfiguration, and the parameter selection when adopting the dynamic frequency-band allocation (DFBA) and and dynamic frequency-band reallocation (DFBR) for cognitive radios. They also demonstrate how the reconfigurability can be achieved either by a channel switch or by variable multipliers/commutators. In Chapter 9, the third chapter of Part 3, the authors present detail theoretical analysis of opportunistic spectrum access using continuous-time Markov chains. In particular, they explain how SU may be forced to terminate under different traffic scenarios.The last part of this book includes chapters that focus more on the interaction among multiple SU, which naturally leads to the problem of resource allocation. Chapter 10, the first chapter of Part 4, considers different system models in which SUs compete for a chance to transmit simultaneously or orthogonally with the PU. On the basis of these models, the chapter also offers insights into how to design such scenario in a cognitive radio network environments. Chapter 11, the second chapter of Part 4, focuses on the issue of how to implement interference mitigation by power control techniques amongst multiple cognitive radios in the light of game theory. In Chapter 12, the third chapter of Part 4, the authors design a joint PHY layer cooperative spectrum sensing and MAC layer resource scheduling scheme. They also formulate the joint design as a non-convex optimization problem and derive the asymptotic optimum solution based on recent advances in duality optimization theory. The last chapter of this book is rather different from the rest but is equally important. The author presents a summary of standards activities related to cognitive radio systems and dynamic spectrum access systems.

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“…Although the effect of collision probability is included, the results are only valid when the SU groups have the same connection length. The prioritisation among SU traffic from a physical layer and network integration perspective have been studied in Wiggins et al (2008); Gosh et al (2009). Moreover, as identified in Tumuluru et al (2011), these works do not include the effect of spectrum handoff.…”

Section: Reactive Spectrum Handoffmentioning

confidence: 99%