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.
Cognitive radio networks promote better spectral efficiency of the electric radio spectrum. The vast majority of current spectral decision models for cognitive radio networks evaluate their performance based on a single secondary user. In reality, the network can experience multiple requests from spectral opportunities. Based on this, the intent of this article is to present and evaluate a spectral decision model for cognitive radio networks in a multi-user environment taking into account the effect of the decisions of the SU on the usefulness of the other SU. To achieve this, a spectral decision model was developed that allows secondary users to share relevant information before accessing the spectrum so that they can select the most appropriate spectral opportunities. The evaluation and validation of the model was performed using three multicriteria decision-making algorithms under the metric of the number of total handoffs in a conventional scenario and a real scenario, in the conventional scenario, only users that match the input of the multiuser module are included; in the real scenario, in addition to the conventional users, users that enter and leave at random times are included, a feature that alters the models for estimating the behavior of the radio environment. The results show better performance of the TOPSIS algorithm over VIKOR and SAW. The most important contribution of this work is the evaluation of the performance of the spectral decision algorithms implemented in a multi-user environment that allows multiple access and exchange of information between users, with experimental spectral occupation data.
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