Radial Basis Function Networks and their Application in Communication Systems

Ascensiòn Gallardo Antolìn (), Juan Pascual Garcìa () and Josè Luis Sancho Gòmez ()
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Ascensiòn Gallardo Antolìn: EPS-Universidad Carlos III de Madrid, Avda. de la Universidad, Departamento de Teorìa de la Senal y Comunicaciones
Juan Pascual Garcìa: Universidad Politècnica de Cartagena Campus de la Muralla del Mar, Departamento de las Tecnologìas de la Informaciòn y las Comunicaciones
Josè Luis Sancho Gòmez: Universidad Politècnica de Cartagena Campus de la Muralla del Mar, Departamento de las Tecnologìas de la Informaciòn y las Comunicaciones

Chapter Chapter 5 in Computational Intelligence, 2007, pp 109-130 from Springer

Abstract: Among the different types of Neural Networks (NN), the most popular and frequently used architectures are the Multilayer Perceptron (MLP) and Radial Basis Functions (RBF) due to their approximation capabilities. In this chapter we discuss the use of RBF networks to solve problems in different areas related to communications systems. In the first part of the chapter, we revise the structure of the RBF networks and the main procedures to train them. In the second part, the main applications of RBF networks in communication systems are presented and described. In particular, we will focus our attention in antenna array signal processing (direction-of-arrival estimation and beamforming) and channel equalization (intersymbol interferences and co-channel interferences). Other applications such as coding/decoding, system identification, fault detection in access networks and automatic recognition of wireless standards are also mentioned

Keywords: Neural networks; radial basis functions; communication systems; channel equalization; antenna array signal processing (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-0-387-37452-9_5

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DOI: 10.1007/0-387-37452-3_5

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