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.
The objective of this paper aims to benchmark the performance of a proposed collaborative algorithm for the dynamic spectrum allocation in distributed cognitive wireless networks. An algorithm called Collaborative FAHP was developed, it is intended to share the cognitive user's information and from that information select the best spectrum opportunity. The assessment was carried out through the development of simulation software based on the real spectral occupancy data taken from the 1850 MHz to 2000 MHz frequency band. The results were compared with two more algorithms, simple FAHP and completely Random one. The results show that as there is more spectrum information the FAHP-Collaborative algorithm increases its performance level regarding bandwidth. However, it is also noted the results are dependent on the data quantity and quality that is shared among the cognitive users.
Cognitive radio networks enable a more efficient use of the radioelectric spectrum through dynamic access. Decentralized cognitive radio networks have gained popularity due to their advantages over centralized networks. The purpose of this article is to propose the collaboration between secondary users for cognitive Wi-Fi networks, in the form of two multi-criteria decision-making algorithms known as TOPSIS and VIKOR and assess their performance in terms of the number of failed handoffs. The comparative analysis is established under four different scenarios, according to the service class and the traffic level, within the Wi-Fi frequency band. The results show the performance evaluation obtained through simulations and experimental measurements, where the VIKOR algorithm has a better performance in terms of failed handoffs under different scenarios and collaboration levels.
RESUMENObjetivo: El objetivo de este trabajo es estudiar las aplicaciones de las diferentes técnicas de inteligencia artificial y aprendizaje autónomo en la asignación dinámica de espectro para redes inalámbricas cognitivas, es especial las distribuidas. Método: El desarrollo del presente trabajo se realizó a través del estudio y análisis de algunas de las publicaciones más relevantes en la literatura actual por medio de la búsqueda en revistas internacionales indexadas en ISI y Scopus. Resultados: Se determinaron las técnicas de inteligencia artificial y aprendizaje autónomo más relevantes y con mayor aplicación en la asignación de espectro para redes inalámbricas cognitivas. Conclusiones: La implementación de una técnica o del conjunto de las mismas depende de las necesidades en procesamiento de la señal, compensaciones en los tiempos de respuesta, disponibilidad de las muestras, capacidad de almacenamiento, capacidad de aprendizaje, robustez, entre otras.Palabras Clave: aprendizaje autonomo, asignacion dinamica de espectro, inteligencia artificial, radio cognitivas, redes inalambricas.
ABSTRACTObjective: The objective of this work is to study the applications of different techniques of artificial intelligence and autonomous learning in the dynamic allocation of spectrum for cognitive wireless networks, especially the distributed ones. Method: The development of this work was done through the study and analysis of some of the most relevant publications in current literature through the search in indexed international journals in ISI and Scopus. Results: the most relevant techniques of artificial intelligence and autonomous learning were determined. Also, the ones with more applicability in the allocation of spectrum for cognitive wireless networks were determined, too. .
Tecnicas inteligentes en la asignacion de espectro dinamica para redes inalambricas cognitivasSmart techniques in the dynamic spectrum alocation for cognitive wireless networks
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