A Dynamic Channel Assignment Strategy via Power Control for Ad-Hoc Network Systems

Chin, Choong Ming; Sim, Moh Lim; Olafsson, S.

doi:10.1109/vetecs.2007.616

Cited by 8 publications

(20 citation statements)

References 16 publications

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“…After a review of the DCPA literature on multi-channel ad hoc networks, we adapted algorithms based on the works of [1,2,4,5] for our comparative analysis. We adapted some of the underlying assumptions of each work to allow for equitable comparison, while maintaining their unique algorithmic features.…”

Section: Algorithmsmentioning

confidence: 99%

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Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Deaton

Ahmad

Shukla

et al. 2010

Wireless Pers Commun

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In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and an evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links 123 278 J. D. Deaton et al.while minimizing the aggregate transmit power. We apply our evaluation method to five representative DPCA algorithms proposed in the literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate the effectiveness of the algorithms in achieving feasible link allocations in the network, and their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment algorithm does not require cross-link gain information, has the overall lowest run time, and achieves the highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.

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Section: Algorithmsmentioning

confidence: 99%

“…Then, in Sect. 3, we summarize each of the five algorithms, based on [1,2,4,5], and an additional algorithm proposed by us. In Sect.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Deaton

Ahmad

Shukla

et al. 2010

Wireless Pers Commun

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“…We adapted some of the underlying assumptions of each work to allow for equitable comparison, while maintaining their unique algorithmic features. In light of changes from their original work in [1,2,4,5], these adapted algorithms are renamed as: Least Interfering Channel and Non-Iterative Power Assignment (LICNPA), Spatial Channel Separation and Iterative Power Assignment (SCSI-PA), Least Interfering Channel and Iterative Power Assignment (LICIPA), Minimum Power Increase Assignment (MPIA), and Conflict Graph Assignment (CGA). We also propose a new distributed algorithm LICIPA, which combines mechanisms from [2,4].…”

Section: Algorithmsmentioning

confidence: 99%

“…Our derivation of the complexity of MPIA is shown in CGA, based on [1], maximizes the number of feasible links through a greedy assignment algorithm using global knowledge of the cross-link gains in a weighted conflict graph. The algorithm begins by calculating the number of possible feasible links for each unassigned channel by attempting to place all links on each unassigned channel.…”

Section: Minimum Power Increase Assignment (Mpia)mentioning

confidence: 99%

“…Research on Dynamic Channel and Power Assignment (DCPA) seeks effective ways in which a CN of autonomous radios can assign an appropriate frequency channel and transmit power to improve connectivity and spectral efficiency given available spectrum. Many DCPA techniques have been proposed in the literature [1][2][3][4][5]8], and evaluating the tradeoffs among these techniques from the existing body of work is a difficult task. Each DCPA algorithm can be evaluated under different topologies, node densities, and metrics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Deaton

Ahmad

Shukla

et al. 2010

Wireless Pers Commun

View full text Add to dashboard Cite

In this paper, we develop a unifying optimization formulation to describe the Dynamic Channel and Power Assignment (DCPA) problem and an evaluation method for comparing DCPA algorithms. DCPA refers to the allocation of transmit power and frequency channels to links in a cognitive network so as to maximize the total number of feasible links while minimizing the aggregate transmit power. We apply our evaluation method to five 123 6 J. D. Deaton et al.representative DPCA algorithms proposed in the literature. This comparison illustrates the tradeoffs between control modes (centralized versus distributed) and channel/power assignment techniques. We estimate the complexity of each algorithm. Through simulations, we evaluate the effectiveness of the algorithms in achieving feasible link allocations in the network, and their power efficiency. Our results indicate that, when few channels are available, the effectiveness of all algorithms is comparable and thus the one with smallest complexity should be selected. The Least Interfering Channel and Iterative Power Assignment algorithm does not require cross-link gain information, has the overall lowest run time, and achieves the highest feasibility ratio of all the distributed algorithms; however, this comes at a cost of higher average power per link.

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Multicast communications in cognitive radio networks using directional antennas

Guo¹,

Huang

2012

Wirel. Commun. Mob. Comput.

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This paper presents a study on multicast communications in cognitive radio networks (CRNs)using directional antennas. The objective is to maximize the throughput of the CRN. The spectrum is divided into multiple channels and licensed to the primary network. While the CRN is accessing the spectrum, the interference power is carefully controlled to avoid impacting the operation of the primary network. The mathematical model is presented and subsequently formulated as a mixed integer non-linear programming (MINLP) problem, which is non-deterministic polynomial-time hard. Therefore, a greedy algorithm is designed to approximate the optimal performance. The MINLP problem is then relaxed and an upper bound is developed. Simulation results are presented to compare the performance of the greedy algorithm and the upper bound, which demonstrates the efficacy of the greedy algorithm as well as the tightness of the upper bound. He previously worked at the Wireless Advanced Technology Laboratory, Bell Labs of Lucent Technologies. His research interests are in circuits and systems design for embedded computing, including computer architectures, reconfigurable computing, embedded systems, and wireless networks.Wirel. Commun. Mob. Comput. 2015; 15:260-275

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A Dynamic Channel Assignment Strategy via Power Control for Ad-Hoc Network Systems

Cited by 8 publications

References 16 publications

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Evaluation of Dynamic Channel and Power Assignment for Cognitive Networks

Multicast communications in cognitive radio networks using directional antennas

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