Automatic channel selection for cognitive radio systems

Bennai, Mustapha; Sydor, John; Rahman, Mahmudur

doi:10.1109/pimrc.2010.5671634

Cited by 18 publications

(15 citation statements)

References 7 publications

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“…The authors in [24] propose a channel-selection algorithm considering the quality of service requirements of the CR users; this is based on measuring the channel utilization. The algorithm is flexible to achieve different degrees of compromise to establish a trade-off between the system data rate and users fairness.…”

Section: Related Workmentioning

confidence: 99%

“…The simulation results show that the proposed algorithm can achieve up to a 30 % performance increase in the data rate, while ensuring a higher level of equality to a scheme in which the channel selection is random. The algorithm presented in [24] shows a trade-off between the data rate and the user's fairness. This balances the resources among SUs, but its decisions are limited to measurements of the occupation of the channel only.…”

Section: Related Workmentioning

confidence: 99%

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Multivariable algorithm for dynamic channel selection in cognitive radio networks

Hernández

Salgado

Garcı́a

et al. 2015

J Wireless Com Network

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The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work presents a multivariable algorithm for dynamic channel selection used in cognitive wireless networks. The channel selection is based on the fuzzy analytical hierarchical process (FAHP) method. The selected criteria for choosing the best backup channel are probability of channel availability, estimated channel time availability, signal to noise plus interference ratio, and bandwidth. These criteria are determined by means of a customized Delphi Method and using the FAHP technique; the corresponding weight and significance is calculated for two applications classified as best effort (BE) and real time (RT). The insertion of the fuzzy logic in the AHP algorithm allows better handling of inaccurate information because, as shown the results, consider more options to evaluate in contrast to a conventional AHP. As a difference with related work, the performance of our proposed FAHP method was validated with captured data in experiments realized at the GSM frequency band (824-849 MHz). This is due to the challenge of finding white spaces to communicate in this frequency band. This band represents more disputes in accessing spectral opportunities than other radio frequency (RF) bands because of the high demand for mobile phone communications. The proposed FAHP algorithm has a practical computational complexity and provides an effective frequency-channel selection. This proposed FAHP algorithm presents a new methodology to select and classify the variables based on a modified version of the Delphi method. The results of the proposed method were contrasted numerically with other three methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multivariable algorithm for dynamic channel selection in cognitive radio networks

Hernández

Salgado

Garcı́a

et al. 2015

J Wireless Com Network

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“…This sensed data is stored in the REAM which the engine examines to select candidate channels. The algorithm trades fairness against systemic throughput prior to choosing common channel for the network and updates regularly to select switching times in a manner least disruptive to the network [10].…”

Section: H Cognitive Engines and The Reammentioning

confidence: 99%

A generic cognitive radio based on commodity hardware

Sydor

Roberts

Doray

et al. 2011

2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)

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Abstract-In this paper we describe the process that we undertook to build a configurable wireless platform that can be used to implement cognitive radio network (CRN) architectures. Consisting of a commodity IEEE 802.11 a/b/g (WiFi) router at the physical (PHY) layer and RF signal processing and IP traffic shaping circuitry, the resultant hybrid terminal (called the WiFi CR) becomes a building block that can implement cognitive femtocells, point to multipoint, mesh, and relay wireless networks. The IP addressable WiFi CR terminals can sense their radio environment, schedule IEEE 802.11 packet transmissions in space and time, and select channel, modulation rates, and transmit power. Intelligent operation is undertaken by a cognitive network management system (CR NMS) which controls a number of WiFi CR terminals and solicits sensor information from them. The CR NMS gathers the spectrum and sensed interference information and builds a memory map with such knowledge, thereby creating radio environment awareness for the CRN. Cognitive engines (the intelligent control algorithms) within the CR NMS use radio environment knowledge and other information (such as spectrum policy) to give the system a capability to seek white space spectrum, avoid interference, identify primary users, and take on other tasks associated with cognitive radio (CR). Designed around ISM band operation, this generic terminal, with proper RF modifications, can work in the TV bands, 3.65-3.70 GHz, and up to 60 GHz.

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“…Many solutions have been proposed. There are solutions for centralized and distributed systems, addressing the frequency allocation problem in WLANs [2][3][4] [15][16]. There are examples of using game theoretical approach as solutions [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…There are examples of using game theoretical approach as solutions [5][6][7]. Even there are several proposed frameworks for load balancing [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A distributed load balancing algorithm for adaptive cognitive radio network

Elahi

Siddique

2014

2013 International Conference on Electrical Information and Communication Technology (EICT)

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Cognitive radio network, which deals with dynamic frequency assignment, is one of the most popular research topic nowadays in the context of cellular networks, WLANs and mesh networks as well. The growing interest in the cognitive radio technology and its capability to offer more efficient spectrum usage attracts researchers to find efficient algorithms to accommodate more users and to provide better service in wireless communication. In this paper, we present a distributed algorithm for selecting channels, in cognitive radio environment, so that the load is distributed over them for more efficient service. Simulations and experimental results show the efficacy of proposed approach.

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Automatic channel selection for cognitive radio systems

Cited by 18 publications

References 7 publications

Multivariable algorithm for dynamic channel selection in cognitive radio networks

Multivariable algorithm for dynamic channel selection in cognitive radio networks

A generic cognitive radio based on commodity hardware

A distributed load balancing algorithm for adaptive cognitive radio network

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