Method for Evaluating k-Means Clustering for Increased Reliability in Cognitive Radio Networks

McLean, Ryan K.; Silvius, Mark D.; Hopkinson, Kenneth M.

doi:10.1109/sere.2013.25

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…In their previous work [3][4][5], the authors proposed a new architecture for an adaptable FH-CR. The system contains novel design features, such as the use of a distributed REM, a clustering algorithm to group nodes into geographic regions of similar REM, as well as a FPGA-based circuit for merging REM data on each radio.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…In other words, the EmuNet and WARPnet could be combined into a single, consolidated Mixed Emulation-Wireless Network Testbed (MEWiNet), as shown in Figure 8. The MEWiNet would also receive emulated spectrum and whitespace data through the Dynamic Spectrum Emulator (DySE) system [3][4][5].…”

Section: Future Workmentioning

confidence: 99%

“…In previous work [3][4][5], the authors presented a more detailed OODA loop and introduced a new architecture to support a network of frequency-hopping (FH) CRs. The network is capable of co-existing with primary users, competing secondary users, and sources of interference in the band.…”

Section: Introductionmentioning

confidence: 99%

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Totally-Ordered, Reliable Multicast over Cognitive Radio Networks

Tolson

Dalton

Silvius

et al. 2014

2014 47th Hawaii International Conference on System Sciences

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This paper summarizes the evaluation of a totallyordered, reliable multicast protocol over a cognitive radio (CR) network. Multicast enables a group of nodes to subscribe to broadcast messages. Reliability ensures that all nodes receive messages correctly; totally-ordered ensures that all nodes receive messages in sequential order. In the proposed architecture, CR nodes employ multicast to exchange radio environment maps and to perform dynamic spectrum access. Emulation and wireless testbeds were constructed and baseline unicast quality of service (QoS) measured. The Spread open-source multicast software toolkit was installed on all nodes and the resulting multicast QoS measured. Channel effects were applied to the emulation testbed, so that its QoS modeled that of the wireless testbed. This paper demonstrates that it is feasible to characterize multicast QoS over a CR wireless network, through the use of an equivalent emulated network.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

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Totally-Ordered, Reliable Multicast over Cognitive Radio Networks

Tolson

Dalton

Silvius

et al. 2014

2014 47th Hawaii International Conference on System Sciences

Self Cite

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“…The simulation results show that the algorithm for grouping K-means is a method of normalization which can ameliorate the performance of the Space-Time Adaptive Processing (STAP) in a no homogenous environment. (McLean, Silvius, & Hopkinson, 2013) presents an innovative model that determines the grouping configurations for different network types and in various RF environments. A software based on K-Means developed by the University of Maryland that demonstrates the possibility of using K-Means in cognitive radio through simulations in Matlab.…”

Section: Related Workmentioning

confidence: 99%

Multichannel assignment using K-Means in cognitive radio networks

Marquez

Salgado

Hernández

2017

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Context: The developed scheme allows carrying out the assignment of several frequency channels (both contiguous and not contiguous) available to the secondary users that require a higher bandwidth, under an environment of equality. Objective: The following paper develops a multichannel assignment which allows taking in a more efficient way the spectral opportunities in cognitive radio networks. Method: The developed assignment model is composed by the K-Means algorithm, which is in charged of carrying out the grouping of channels through clusters for the best parameters, and another algorithm in charge of establishing an equal criteria for all the secondary users that wish to transmit. The reached results were evaluated with experimental spectral occupancy data taken from the GSM frequency band. The developed model was compared with the MFA-CRN algorithm. Results: The obtained measurements correspond to the average bandwidth, the average delay, and the fairness calculation in the assignment of several channels. The developed assignment model shows an improvement in the assignment of a higher transmission average bandwidth for each secondary user while maintaining the fairness criteria in the channel assignments. Conclusion: Despite the increasing in the number of handoffs, it is also observed that metrics such as average bandwidth, average throughput and average delay are never negatively impacted by this increase in handoffs.

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“…States O-4 and O-5 are specifically addressed in [3] where the authors implement map merging and adaptive hopset selection (in the form of frequency hopping) on an field-programmable gate array (FPGA). In [4] the same author addresses the forming of network partitions via a k-means clustering algorithm based on spectral and spatial proximity. …”

Section: A Related Workmentioning

confidence: 99%

Opportunistic access in frequency hopping cognitive radio networks

Hennessey

Hopkinson

Silvius

2014

2014 Wireless Telecommunications Symposium

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Researchers in the area of cognitive radio often investigate the utility of dynamic spectrum access as a means to make more efficient use of the radio frequency spectrum. Many studies have been conducted to find ways in which a secondary user can occupy spectrum licensed to a primary user in a manner which does not disrupt the primary user's performance. This article investigates the use of opportunistic access in a frequency hopping radio to mitigate the interference caused by other transmitters in a contentious environment such as the unlicensed 2.4 GHz region. This research demonstrates how dynamic spectrum access techniques can be used not only to prevent interfering with other users but also improve the robustness of a communication system.

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Method for Evaluating k-Means Clustering for Increased Reliability in Cognitive Radio Networks

Cited by 6 publications

References 2 publications

Totally-Ordered, Reliable Multicast over Cognitive Radio Networks

Totally-Ordered, Reliable Multicast over Cognitive Radio Networks

Multichannel assignment using K-Means in cognitive radio networks

Opportunistic access in frequency hopping cognitive radio networks

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