Collaborative Strategy for Route and Spectrum Selection in Cognitive Radio Networks

Deng, Shoufeng; Chen, Jie; He, Hao; Tang, Wanbin

doi:10.1109/fgcn.2007.88

Cited by 12 publications

(6 citation statements)

References 5 publications

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“…RELATED WORK In recent years, numerous routing protocols for CRNs have been proposed with different design goals, e.g., maximizing throughput [14], [15], [10], [16], [17], [18], minimizing delay [11], [19], [6], [13], maximizing route stability [20], [12], minimizing route recovery/maintenance cost [21], [22], etc. Many of those protocols [14], [15], [20], [11], [16], [19], [17] assume static channel availability and do not include PU dynamics in their routing metrics. Consequently, such protocols are similar to those proposed for multi-channel WMNs and cannot deal with temporal variations of spectrum availability in CRNs.…”

Section: Testbed Resultsmentioning

confidence: 99%

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Zheng

Rawat

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

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Abstract-Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals.In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

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Section: Testbed Resultsmentioning

confidence: 99%

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Zheng

Rawat

et al. 2013

2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems

View full text Add to dashboard Cite

show abstract

“…Many of those protocols [14], [15], [20], [11], [16], [19], [17] assume static channel availability and do not include PU dynamics in their routing metrics. Consequently, such protocols are similar to those proposed for multi-channel WMNs and cannot deal with temporal variations of spectrum availability in CRNs.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, numerous routing protocols for CRNs have been proposed with different design goals, e.g., maximizing throughput [14], [15], [10], [16], [17], [18], minimizing delay [11], [19], [6], [13], maximizing route stability [20], [12], minimizing route recovery/maintenance cost [21], [22], etc. Many of those protocols [14], [15], [20], [11], [16], [19], [17] assume static channel availability and do not include PU dynamics in their routing metrics.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, numerous routing protocols for CRNs have been proposed (e.g., [6], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22]). Besides the main goal of protecting PU transmissions, each protocol is proposed based on different design goals, e.g., maximizing spectrum opportunities, maximizing available bandwidth, minimizing hopcount, minimizing end-to-end delay, etc.…”

Section: Introductionmentioning

confidence: 99%

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Performance Comparison of Routing Protocols for Cognitive Radio Networks

Zheng

Rawat

et al. 2015

IEEE Trans. on Mobile Comput.

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Cognitive radio networks (CRNs) have emerged as a promising solution to the ever-growing demand for additional spectrum resources and more efficient spectrum utilization. A large number of routing protocols for CRNs have been proposed recently, each based on different design goals, and evaluated in different scenarios, under different assumptions. However, little is known about the relative performance of all these protocols, let alone the tradeoffs among their different design goals. In this paper, we conduct the first detailed, empirical performance comparison of three representative routing protocols for CRNs, under the same realistic set of assumptions. Our extensive simulation study shows that the performance of routing protocols in CRNs is affected by a number of factors, in addition to PU activity, some of which have been largely ignored by the majority of previous works. We find that different protocols perform well under different scenarios, and investigate the causes of the observed performance. Furthermore, we present a generic software architecture for the experimental evaluation of CRN routing protocols on a testbed based on the USRP2 platform, and compare the performance of two protocols on a 6 node testbed. The testbed results confirm the findings of our simulation study.

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“…As mentioned in Chapter 2, many existing proposals for CRNs [79][80][81][110][111][112] have assumed a static CRN that considers an available frequency band as a permanent resource indefinitely available during its activity. However, such assumptions are no longer valid in the context of the CRN.…”

Section: Crnsmentioning

confidence: 99%

Design and development of MAC and routing protocols for cognitive radio based wireless mesh networks

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Wireless Mesh Networks (WMNs) are communications networks made up of radio nodes organized in a mesh topology. It is envisaged that WMNs can provide broadband wireless access and replace traditional wired based communications infrastructure in the near future. Correspondingly, WMNs are also expected to support delay sensitive and/or high bandwidth real-time streaming multimedia applications such as VoIP and video conferencing. However, Quality-of-Service (QoS) provisioning in WMNs is very challenging due to various factors. These include physical layer factors such as fading or attenuation of a transmitted signal over the wireless medium which results in a loss of signal power at the receiver. Other factors include the dynamical changing of topology, capacity limitations, link variability and multi-hop communications. On the network layer, many existing dynamic routing protocols select paths based on only one criteria (shortest hops, delay etc), which do not take into account topology, interference or traffic pattern. This causes the traffic to flow through nodes that experience high interference and thus increases the occurrences of dropped packets and retransmissions. Despite ongoing research efforts, one key limitation to the performance of the WMNs is the interfering nature of wireless transmissions which degrades the network capacity. This is made worse, especially for the case of devices operating in the unlicensed spectrum where it is becoming increasingly crowded due to the proliferation of diverse wireless communication systems. The overcrowding of the unlicensed spectrum aggravates the interference issues which further reduces the performances of wireless devices. The interference is further exacerbated in WMNs where traffic takes multiple hops to reach the destination.

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Collaborative Strategy for Route and Spectrum Selection in Cognitive Radio Networks

Cited by 12 publications

References 5 publications

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Design and development of MAC and routing protocols for cognitive radio based wireless mesh networks

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