Reactive routing for mobile cognitive radio ad hoc networks

Cacciapuoti, Angela Sara; Caleffi, Marcello; Paura, Luigi

doi:10.1016/j.adhoc.2011.04.004

Cited by 109 publications

(78 citation statements)

References 15 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

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“…The arrival time is the beginning of an ON (or non-white space) period; while the departure time is the beginning of an OFF (or white space) period. The ON-OFF transitions of PU activity for PU j ∈ J k using channel k follows a Poisson model in which ON and OFF periods, namely T k ON,j and T k OF F,j , are exponentially distributed with rates λ k ON,j and λ k OF F,j , respectively [21,22]. The mean values of exponential distribution for ON and OFF periods are given by…”

Section: Pus' Activitiesmentioning

confidence: 99%

SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks

et al. 2015

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Cognitive radio (CR) is the next-generation wireless communication system that allows unlicensed users (or secondary users, SUs) to exploit the underutilized spectrum (or white spaces) in licensed spectrum whilst minimizing interference to licensed users (or primary users, PUs). This article proposes a SpectruM-Aware clusteR-based rouTing (SMART) scheme that enables SUs to form clusters in a cognitive radio network (CRN) and enables each SU source node to search for a route to its destination node on the clustered network. An intrinsic characteristic of CRNs is the dynamicity of operating environment in which network conditions (i.e., PUs' activities) change as time goes by. Based on the network conditions, SMART enables SUs to adjust their cluster size, which represents the number of nodes in a cluster, and searches for a route on the clustered network using an artificial intelligence approach called reinforcement learning. Simulation results show that SMART selects stable routes and significantly reduces interference to PUs, as well as, routing overhead in terms of route discovery frequency without significant degradation of throughput and end-to-end delay.

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“…In [7], Cognitive ad hoc Ondemand Distance Vector (CAODV) routing protocol with two di erent versions has been proposed to exploit path and spectrum diversity separately. The version exploiting path diversity is considered to be a multipath routing.…”

Section: Related Workmentioning

confidence: 99%

CMORC: Class-based Multipath On-demand Routing Protocol for Cognitive Radio Networks

Rezaei

Hemmatyar

2017

Scientia Iranica

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Abstract. Cognitive Radio (CR) technology enables Dynamic Spectrum Access (DSA) toimprove the e ciency of under-utilized licensed bands and overcrowded unlicensed bands. However, providing an acceptable service for cognitive users requires more sophisticated approaches due to the existence of Primary Users (PU) with high priority over licensed bands. Additionally, it is of paramount importance that interference with PUs be reduced so that they can communicate without interruption. In order to meet requirements of users as much as possible and reduce interference with PUs, a new Class-based Multi-path Ondemand Routing protocol for Cognitive radio networks (CMORC) is proposed. CMORC is a multipath routing protocol, which de nes two classes of routing to make delay or interference optimum. To the best of our knowledge, it is the rst multipath protocol that takes into consideration both route discovery and rate allocation. CMORC nds the optimum sending rates for each path by solving an optimization problem for each routing class. Since solving these optimization problems is shown to be easy, CMORC is an e cient and practical solution for radio equipment with limited hardware. The simulation results reveal that CMORC outperforms the other recent multipath routing protocol, D 2 CARP, in terms of interference, delay and packet delivery ratio depending on class type.

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Reactive routing for mobile cognitive radio ad hoc networks

Cited by 109 publications

References 15 publications

Performance Comparison of Routing Protocols for Cognitive Radio Networks

Performance Comparison of Routing Protocols for Cognitive Radio Networks

SMART: A SpectruM-Aware ClusteR-based rouTing scheme for distributed cognitive radio networks

CMORC: Class-based Multipath On-demand Routing Protocol for Cognitive Radio Networks

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