Channel Allocation &amp;#x00026; Power Control for Dynamic Spectrum Cognitive Networks using a Localized Island Genetic Algorithm

Nainay, M.Y. El; Friend, Daniel H.; MacKenzie, Allen B.

doi:10.1109/dyspan.2008.80

Cited by 17 publications

(7 citation statements)

References 9 publications

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“…To overcome the limitation of complexity in DSA-MAC protocols, several approaches have been considered to model network interactions e.g. the localized variation of the island genetic algorithm (El Nainay et al 2008), graph colouring theory (Zheng and Peng 2005;Willkomm et al 2008), game theory (Younis and Krunz 2006;Zou and Chigan 2008), stochastic theory Swami et al (2005), genetic algorithms (Rondeau et al 2004), and swarm intelligence algorithms (Atakan and Akan 2007).…”

Section: Mac Protocols Based On Dynamic Spectrum Allocation (Dsa)mentioning

confidence: 99%

A survey on MAC protocols for complex self-organizing cognitive radio networks

Shah

Zhang

Kamran

et al. 2016

Complex Adapt Syst Model

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Complex self-organizing cognitive radio (CR) networks serve as a framework for accessing the spectrum allocation dynamically where the vacant channels can be used by CR nodes opportunistically. CR devices must be capable of exploiting spectrum opportunities and exchanging control information over a control channel. Moreover, CR nodes should intelligently coordinate their access between different cognitive radios to avoid collisions on the available spectrum channels and to vacate the channel for the licensed user in timely manner. Since inception of CR technology, several MAC protocols have been designed and developed. This paper surveys the state of the art on tools, technologies and taxonomy of complex self-organizing CR networks. A detailed analysis on CR MAC protocols form part of this paper. We group existing approaches for development of CR MAC protocols and classify them into different categories and provide performance analysis and comparison of different protocols. With our categorization, an easy and concise view of underlying models for development of a CR MAC protocol is provided.

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Section: Mac Protocols Based On Dynamic Spectrum Allocation (Dsa)mentioning

confidence: 99%

A survey on MAC protocols for complex self-organizing cognitive radio networks

Shah

Zhang

Kamran

et al. 2016

Complex Adapt Syst Model

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“…We emphasize here that we are planning to use the LiGA in a more general way as the distributed reasoning algorithm for Cognitive Networks and that this demonstration is acting as a proof of concept. Our previous works, using different variations of the iGA in [5] and [6], reveal that the iGA is a promising algorithm to solve more communication and network problems.…”

Section: J Island Genetic Algorithm Channel Allocatormentioning

confidence: 99%

Channel allocation for dynamic spectrum access cognitive networks using Localized island Genetic Algorithm

ElNainay

Feng

Wang

et al. 2009

2009 5th International Conference on Testbeds and Research Infrastructures for the Development of Networks &Amp; Communities An

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In our demonstration, we show how cognitive radios can be organized to form a cooperative ad-hoc cognitive radio network that utilizes the available spectrum opportunistically and efficiently through channel allocation while at the same time avoiding causing interference to primary users as they become active. Our cognitive radios are Software Defined Radios consisting of a Linux laptop and a Universal Software Radio Peripheral integrated with our extended implementation of the GNU Radio software package and our Localized island Genetic Algorithm implementation for the channel allocation.

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“…That is, noting that the signal power of one data streams is interference to other data streams, maximum power may not be an optimal solution for optimization problem (5). Power control problem has been well studied for SISO links and there are many existing solutions based on nonlinear optimization [35]- [37], game theory [38], [39], metaheuristic [40], [41], etc. Given the complexity of power control problem, we leave the extension of existing SISO power control algorithms as a future work.…”

Section: B Joint Beamforming and Network Formation Protocolsmentioning

confidence: 99%

Coherent Communications in Self-Organizing Networks With Distributed Beamforming

Shi

Sagduyu

2020

IEEE Trans. Veh. Technol.

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Coherent communications aim to support higher data rates and extended connectivity at lower power consumption compared with traditional point-to-point transmissions. The typical setting of coherent communication schemes is based on a single data stream with multiple transmitters and a single receiver. This paper studies the case of multiple concurrent data streams, each with multiple transmitters and multiple receivers, in self-organizing wireless networks. A distributed optimization solution based on joint network formation and beamforming is developed for coherent group communications in a network of nodes that need to communicate over long distances. This solution significantly improves the power gain for a single data stream and the signal-to-interference-ratio for multiple data streams compared to point-to-point communications. These gains are in turn translated to improvements in communication range, power efficiency, reliability, and throughput. In this multi-layer network optimization solution, nodes coordinate with each other in a distributed manner to form transmitter and receiver groups, and communicate with each other coherently over long distances. The coherent beamforming protocol is optimized for given transmitter and receiver groups, whereas the network formation protocol is optimized to determine these groups. By using single-antenna communication nodes, the proposed optimization solution provides a major gain in network communications and outperforms other benchmark combinations of beamforming and network formation protocols.

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Channel Allocation & Power Control for Dynamic Spectrum Cognitive Networks using a Localized Island Genetic Algorithm

Cited by 17 publications

References 9 publications

A survey on MAC protocols for complex self-organizing cognitive radio networks

A survey on MAC protocols for complex self-organizing cognitive radio networks

Channel allocation for dynamic spectrum access cognitive networks using Localized island Genetic Algorithm

Coherent Communications in Self-Organizing Networks With Distributed Beamforming

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