Minimum-Energy Multicast Tree in Cognitive Radio Networks

Ren, Wei; Xiao, Xiangyang; Zhao, Qing

doi:10.21236/ada554825

Cited by 10 publications

(16 citation statements)

References 7 publications

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“…COCAST [115] Source Based Tree Improve the scalability of ODMRP Reactive routing protocol incorporating channel assignment that seeks to maximize delivery ratio and minimize delay is proposed MEMT [116] Steiner Tree Construct minimum energy multicast tree Proposed a low-complexity approximate solution QoS multicast [117] Spanning Tree Tree construction with minimum bandwidth consumption A QoS-satisfying multicast tree, with minimal bandwidth consumption, is constructed through a novel slot assignment algorithm.…”

Section: Tree Construction Techniquesmentioning

confidence: 99%

Multicasting in cognitive radio networks: Algorithms, techniques and protocols

Qadir

Baig

Ali

et al. 2014

Journal of Network and Computer Applications

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Multicasting is a fundamental networking primitive utilized by numerous applications. This also holds true for cognitive radio networks (CRNs) which have been proposed as a solution to the problems that emanate from the static non-adaptive features of classical wireless networks. A prime application of CRNs is dynamic spectrum access (DSA), which improves the efficiency of spectrum allocation by allowing a secondary network, comprising of secondary users (SUs), to share spectrum licensed to a primary licensed networks comprising of primary users (PUs). Multicasting in CRNs is a challenging problem due to the dynamic nature of spectrum opportunities available to the SUs. Various approaches, including those based in optimization theory, network coding, algorithms, have been proposed for performing efficient multicast in CRNs. In this paper, we provide a self-contained tutorial on algorithms and techniques useful for solving the multicast problem, and then provide a comprehensive survey of protocols that have been proposed for multicasting in CRNs. We conclude this paper by identifying open research questions and future research directions.

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Section: Tree Construction Techniquesmentioning

confidence: 99%

Multicasting in cognitive radio networks: Algorithms, techniques and protocols

Qadir

Baig

Ali

et al. 2014

Journal of Network and Computer Applications

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show abstract

“…Proof: The proof is based on Independent Thinning Theorem [7,Chapter 5]. Due to the space limit, we omit the details, which can be found in [10].…”

Section: B Calculation Of the Edge Weightsmentioning

confidence: 99%

“…Apply the approximation algorithm for the directed Steiner problem specified in [10] to the auxiliary graph G and obtain an approximate Steiner tree T G in G. This approximation algorithm for the directed Steiner problem is a special case of the one proposed in [9]. 3.…”

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confidence: 99%

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Minimum-energy multicast tree in cognitive radio networks

Ren

Xiao

Zhao

2009

2009 Conference Record of the Forty-Third Asilomar Conference on Signals, Systems and Computers

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We address the multicast problem in cognitive radio networks, where secondary users exploit channels temporarily unused by primary users (i.e., spectrum opportunities). The existence of a communication link between two secondary users depends not only on the transmission power of the secondary transmitter and the distance between these two users, but also on the occurrence of spectrum opportunities. This dependency on the occurrence of spectrum opportunities complicates the construction of an efficient multicast tree in cognitive radio networks. By taking into account this dependency, we propose a lowcomplexity approximation algorithm with bounded performance guarantee for constructing the minimum-energy multicast tree, which transforms the multicast problem into a directed Steiner tree problem. We also demonstrate this dependency by studying the impact of the traffic load of the primary network on the minimum-energy multicast tree.

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QoS multicast routing in cognitive radio ad hoc networks

Xie

Jia

Zhou

2011

Int J Communication

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SUMMARYIn this paper, we discussed the issues of QoS multicast routing in cognitive radio ad hoc networks. The problem of our concern was: given a cognitive radio ad hoc network and a QoS multicast request, how to find a multicast tree so that the total bandwidth consumption of the multicast is minimized while the QoS requirements are met. We proposed two methods to solve it. One is a two-phase method. In this method, we first employed a minimal spanning tree-based algorithm to construct a multicast tree and then proposed a slot assignment algorithm to assign timeslots to the tree links such that the bandwidth consumption of the tree is minimized. The other is an integrated method that considers the multicast routing together with the slot assignment. Extensive simulations were conducted to show the performance of our proposed methods.

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Minimum-Energy Multicast Tree in Cognitive Radio Networks

Cited by 10 publications

References 7 publications

Multicasting in cognitive radio networks: Algorithms, techniques and protocols

Multicasting in cognitive radio networks: Algorithms, techniques and protocols

Minimum-energy multicast tree in cognitive radio networks

QoS multicast routing in cognitive radio ad hoc networks

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