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Cognitive Radio Network. Low Complexity Channel Decision Modelling (en Inglés)
Rahul V. Awathankar (Autor)
·
Eliva Press
· Tapa Blanda
Cognitive Radio Network. Low Complexity Channel Decision Modelling (en Inglés) - Rahul V. Awathankar
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Reseña del libro "Cognitive Radio Network. Low Complexity Channel Decision Modelling (en Inglés)"
Cognitive Radio Networks (CRNs) offer a promising solution to the problem of spectrum insufficiency. In CRNs, licensed spectrum channels are either exclusively reserved for licensed users or temporarily accessed by unlicensed users. A key requirement for unlicensed users is to avoid harmful interference with licensed users' transmissions. Consequently, unlicensed users must determine which available channels to select. This process, known as spectrum decision, aims to optimize the transmission performance of unlicensed users.
I proposes an efficient free-channel selection scheme that significantly reduces the number of comparison computations at the fusion center. This reduction enhances the overall performance of the fusion center in cooperative cognitive radio networks. The proposed channel decision model is developed as an integrated approach using Multi-Criteria Decision-Making (MCDM) techniques, specifically Multi-Objective Optimization on the basis of Ratio Analysis (MOORA).
In cognitive radio networks, channel decision is a critical task that enables secondary users to utilize unused spectrum bands without interfering with primary users. To establish reliable connectivity for data transmission, multiple spectrum-related criteria are evaluated according to the requirements of secondary users. As a result, a utility-based decision problem involving multiple criteria and alternatives must be addressed. This work focuses on four essential aspects: selection of alternatives based on criteria, normalization, optimization, and assignment of weights to objectives. The MOORA method effectively satisfies these requirements.
The MOORA algorithm is employed as a multi-objective MCDM technique for channel decision in the cognitive radio environment. It efficiently manages conflicting criteria and multiple alternatives by constructing a decision matrix and applying ratio analysis to rank available channels. Simulation results demonstrate that the proposed MCDM-based approach outperforms existing methods in terms of handling diverse criteria, reduced computational complexity, and practical applicability.
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