Energy-Efficient Distributed Spectrum Sensing for Wireless Cognitive Radio Networks

Wei, Jin; Zhang, Xi

doi:10.1109/infcomw.2010.5466680

Cited by 39 publications

(21 citation statements)

References 12 publications

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“…As an example, a clusterhead collects channel sensing outcomes from its member nodes and subsequently makes final decisions on channel availability. This has been shown to improve the accuracy of channel sensing outcomes in the presence of channel fading and shadowing which may cause the detection of PUs' activities to failure [6]. Figure 1 shows an example of a cluster structure in which nodes in a CRN are grouped.…”

Section: Clustering In Cognitive Radio Networkmentioning

confidence: 99%

“…The cluster-first approach forms clusters, and subsequently a clusterhead is selected in each cluster while the rest of the nodes in the cluster become member nodes. For instance, in [6,13], nodes with common channels or that are geographically close form a cluster. Subsequently, a node with favorable characteristics (or dominating node) in the cluster, such as greater channel sensing capability, is selected to serve as clusterhead [6]; alternatively, nodes take equal opportunity to serve as clusterheads [13].…”

Section: Clustering Algorithmsmentioning

confidence: 99%

“…For instance, in [6,13], nodes with common channels or that are geographically close form a cluster. Subsequently, a node with favorable characteristics (or dominating node) in the cluster, such as greater channel sensing capability, is selected to serve as clusterhead [6]; alternatively, nodes take equal opportunity to serve as clusterheads [13]. Hence, in cluster merging, it is necessary to combine nodes from two clusters into a single cluster, and subsequently to relinquish a clusterhead and to select a common channel for the newly merged cluster.…”

Section: Clustering Algorithmsmentioning

confidence: 99%

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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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Section: Clustering In Cognitive Radio Networkmentioning

confidence: 99%

Section: Clustering Algorithmsmentioning

confidence: 99%

Section: Clustering Algorithmsmentioning

confidence: 99%

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

et al. 2015

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“…researches devote to develop distributed spectrum sensing [7], spectrum access [8], power control [9][10], relay selection [11], and cross-layer design [12][13][14] approaches.…”

mentioning

confidence: 99%

“…Through the existing energy-efficient distributed spectrum sensing approach (e.g., [7]), each unlicensed user has its own available frequency bands/channels (i.e., the bands/channels are not occupied by the primary users). We jointly consider power control, channel assignment, and routing in the cognitive radio ad hoc network.…”

mentioning

confidence: 99%

Distributed energy-efficient cross-layer design for cognitive radio networks

Tseng

Chung

Chen

et al. 2012

2012 IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC)

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Abstract-In this paper, we jointly consider power control, channel assignment, and routing to minimize network energy consumption while maintaining data rate requirements on each radio link in the multi-hop cognitive radio network. This problem is formulated as a Mixed Integer Non-linear Programming (MINP). Due to the NP-hard property of the MINP, the MINP is decomposed into |N| (the number of nodes in the network) problems so that each problem can be distributedly and locally solved at each node. Furthermore, a Lagrangean Relaxation based Heuristic (LRH) was proposed to approximate the solution of each problem. Through the technique of Lagrangean relaxation, each problem is transformed into its relaxed form and solved iteratively. Numerical results demonstrate that the proposed scheme can efficiently save network energy consumption up to 90%.

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Green Mobile Networks

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Energy-Efficient Distributed Spectrum Sensing for Wireless Cognitive Radio Networks

Cited by 39 publications

References 12 publications

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

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

Distributed energy-efficient cross-layer design for cognitive radio networks

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