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Cognitive Radio Networks
Published in K. R. Rao, Zoran S. Bojkovic, Bojan M. Bakmaz, Wireless Multimedia Communication Systems, 2017
K. R. Rao, Zoran S. Bojkovic, Bojan M. Bakmaz
The dynamic spectrum management (DSM) block is responsible for the medium-term and long-term (both technical and economical) management of spectra and, as such, it incorporates functionalities like provisioning of information for spectrum assignments and occupancy evaluation and decision making on spectrum sharing. Dynamic self-organizing network planning and management (DSONPM) caters to medium-term and long-term management at the level of a reconfigurable network segment (e.g., incorporating several BSs). It provides decision-making functionality for QoS assignments, traffic distribution, network performance optimization, RATs activation, configuration of radio parameters, and so on.
Efficient Sensing Schemes for Cognitive Radio
Published in Ashish Bagwari, Geetam Singh Tomar, Jyotshana Bagwari, Advanced Wireless Sensing Techniques for 5G Networks, 2018
The aim of the cognitive radio (CR) technology is to take advantage of the maximum efficient spectrum and to improve its utilization by using Dynamic Spectrum Access (DSA) techniques. The channel of licensed users can be effectively accessed for higher spectral efficiency with spectrum management techniques. In order to achieve this goal, the cognitive radio users (CRU) in the Cognitive Radio Network must the Dynamic Spectrum Management Framework (DSMF), which consists of spectrum sensing, spectrum decision, spectrum sharing, spectrum mobility, and spectrum management.
A comprehensive survey on machine learning approaches for dynamic spectrum access in cognitive radio networks
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2022
An overview of an existing literature survey is presented in Table 1. The survey reveals that most of the authors have shown their interest in the specific issue of spectrum management. Most of the authors provide different types of solutions to address the spectrum scarcity problem. Some of the authors specifically discuss a problem related to spectrum management whereas no work has been presented with machine learning techniques for dynamic spectrum management which includes spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility. In this survey, spectrum management with intelligent techniques, considering learning as an important parameter has been discussed. In particular, we provide an in-depth discussion on different types of intelligent techniques such as Artificial Neural Network, Metaheuristic Algorithms, Support Vector Machine, Bayesian Learning, Game Theory, and Hidden Markov Models. The pros and cons of each technique in the context of spectrum management have also been discussed. We firstly present a spectrum management framework. Then we introduced various intelligent techniques used in CR networks as well as a survey of state-of-the-art achievements of these techniques for dynamic spectrum management in CR networks. The major contributions of this paper are summarised as follows: This paper presents a comprehensive survey of various intelligent techniques and presents their applications in CR networks. The target of this paper is to provide a focused survey of these techniques and evaluates its performance in spectrum management as major CR tasks which include spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility.This paper presented state-of-the-art achievements in applying intelligent techniques to CR networks along with their strength and limitations to provide an overview of active research in the area of CR networks.The paper discusses research issues and challenges that are still an open issue and need the attention of researchers.