Abstract
This paper presents a novel hybrid extreme learning machine (ELM) with cuckoo search algorithm (CSA) for the classification purposes of the digitally modulated signals, such as phase shift keying (PSK), frequency shift keying (FSK), and quadrature amplitude modulation (QAM). Nine modulation schemes having different orders have been considered for this paper. First, the Gabor filter is used to extract the key features from the received signal which are then optimized by the CSA. Finally, the ELM is used to classify the modulation schemes using these optimized features. Our proposed CSA-ELM approach is not only fast convergent and robust but also manifests improved percentage classification accuracy at low SNRs and lower sample size for both AWGN and Rayleigh fading channels.
Highlights
Automatic modulation classification (AMC) is a process of automatic detection of modulation format from a received signal with no prior information, termed as blind classification
This section describes the extraction of Gabor features, Optimization of these feature through cuckoo search algorithm (CSA) and Classification of modulation schemes through extreme learning machine (ELM)
SIMULATIONS, RESULTS AND DISCUSSION we analyze the performance of our proposed CSA optimizer and ELM classifier by conducting a range of experiments using exhaustive Monte-Carlo simulations for total nine different variants of phase shift keying (PSK), frequency shift keying (FSK) and quadrature amplitude modulation (QAM)
Summary
Automatic modulation classification (AMC) is a process of automatic detection of modulation format from a received signal with no prior information (carrier, signal power, phase offset), termed as blind classification. Research work presented in this article is based on FB approach where first the reference features are extracted from the received signal and the decision is made using the calculated features based on the theoretical reference values for different modulation parameters [1]. The Gabor filtering technique is used here to extract distinct features which are used to classify the modulation formats These extracted features are further optimized with a cuckoo search algorithm (CSA), a new meta-heuristics computational technique motivated by parasitism behavior of some species known cuckoo [6]. The main contribution of our research work is the novel approach where instead of using Gabor features directly, we have optimized the features using a meta-heuristic algorithm before classification by a fast-convergent ELM classifier. This section describes the extraction of Gabor features, Optimization of these feature through CSA and Classification of modulation schemes through ELM.
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