Open AccessInterference mitigation in wideband radios using spectrum correlation and neural network
Author(s) -
Toma Andrea,
Nawaz Tassadaq,
Gao Yue,
Marcenaro Lucio,
Regazzoni Carlo S.
Publication year - 2019
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2018.5720
Subject(s) - cyclostationary process , cognitive radio , computer science , universal software radio peripheral , software defined radio , wireless , wideband , naive bayes classifier , artificial neural network , wireless intrusion prevention system , spread spectrum , testbed , classifier (uml) , wireless network , artificial intelligence , computer network , telecommunications , support vector machine , electronic engineering , channel (broadcasting) , engineering , heterogeneous network
Technologies such as cognitive radio and dynamic spectrum access rely on spectrum sensing which provides wireless devices with information about the radio spectrum in the surrounding environment. One of the main challenges in wireless communications is the interference caused by malicious users on the shared spectrum. In this manuscript, an artificial intelligence enabled cognitive radio framework is proposed at system‐level as part of a cyclic spectrum intelligence algorithm for interference mitigation in wideband radios. It exploits the cyclostationary feature of signals to differentiate users with different modulation schemes and an artificial neural network as classifier to detect potential malicious users. A dataset consisting of experimental modulated and dynamic signals is recorded by spectrum measurements with an in‐house software defined radio testbed and then processed. Cyclostationary features are extracted for each detected signal and fed to a neural network classifier as training and testing data in a complex and dynamic scenario. Results highlight a classification rate of ∼ 1 in most of cases, even at low transmission power. A comparison with two previous works with hand‐crafted features, which employ an energy detector‐based classifier and a naive Bayes‐based classifier, respectively, is discussed.