Wireless OFDM links with equalizers based on extreme learning machines

Rivelli Malco, Juan Pablo; Zabala-Blanco, David; Daniel Breslin, Roberto; Palacios Jativa, Pablo; Dehghan Firoozabadi, Ali; Flores-Calero, Marco; IEEE

Abstract

Technologies used in wireless access telecommunications networks, particularly those of mobile telephony, are constantly advancing, achieving this through the development of new techniques and methods that allow reaching an adequate level in very important aspects for a communication system. This article presents the extreme learning machine (ELM) with regularized parameter as a suitable alternative to perform channel equalization in orthogonal frequency division multiplexing (OFDM) schemes subject to standard wireless communications. To study their performance, other extreme learning machines proposed as equalizers are also considered. For various signal-to-noise ratios (SNR) and diverse model channels, the bit error rates are exposed, by showing that the superiority of a certain method depends on the adopted wireless link.

Más información

Título según WOS: Wireless OFDM links with equalizers based on extreme learning machines
Título de la Revista: 2021 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (IEEE CHILECON 2021)
Editorial: IEEE
Fecha de publicación: 2021
Página de inicio: 232
Página final: 237
DOI:

10.1109/CHILECON54041.2021.9703008

Notas: ISI