CSA-Assisted Gabor Features for Automatic Modulation Classification

• Published in: Circuits, systems, and signal processing Vol. 41; no. 3; pp. 1660 - 1682
• Main Authors: Shah, Syed Ihtesham Hussain, Coronato, Antonio, Ghauri, Sajjad A., Alam, Sheraz, Sarfraz, Mubashar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2022; Springer Nature B.V
• Subjects: Circuits and Systems; Classification; Electrical Engineering; Electronics and Microelectronics; Engineering; Feature extraction; Gabor filters; Heuristic methods; Instrumentation; Phase shift keying; Quadrature amplitude modulation; Search algorithms; Signal processing; Signal to noise ratio; Signal,Image and Speech Processing; Feature extraction; Gabor features (GF); Automatic modulation classification (AMC); Cuckoo search algorithm (CSA)
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-021-01854-y

Automatic modulation classification (AMC) is a process of automatic detection of modulation format imposed on the received signal with no prior information (carrier, signal power, phase offset) of the signal, also known as blind classification. In this paper, we proposed a new AMC algorithm, by combining the synergy of the meta-heuristic technique with Gabor feature extraction mainly used in texture analysis. Gabor filters are used to extract the features that are further optimized using the cuckoo search algorithm to increase the efficiency of the classification procedure. The classification approach is applied on digitally modulated signals having phase-shift keying, frequency-shift keying, and quadrature amplitude modulation schemes of order 2–64 over the nonfading channel (AWGN) and fading channel (Rayleigh). Simulations and performance comparison with the existing literature validate that the proposed solution has better classification accuracy with lower sample size and lower signal-to-noise ratio.