Implementation of modulation classifier over software defined radio. Issue 9 (1st June 2020)

• Record Type: Journal Article
• Authors: Jajoo, Gaurav; Kumar, Yogesh; Kumar, Ashok; Yadav, Sandeep Kumar
• Is Part Of: IET communications. Volume 14:Issue 9(2020)
• Page Start: 1467
• Page End: 1475
• Publication Date: 2020-06-01
• Subjects: virtual instrumentation -- phase shift keying -- cognitive radio -- field programmable gate arrays -- quadrature amplitude modulation -- AWGN channels -- software radio -- amplitude shift keying -- pattern classification -- pattern clustering; BSMR -- modulation classifier -- blind signal modulation recognition -- cognitive radio -- software‐defined radio -- blind signal modulation recogniser -- field‐programmable gate array -- received signal -- carrier frequency offset -- symbol rate -- symbol timing offset -- constellation points -- constellation signature -- NI‐FlexRIO‐7975 FPGA module -- NI‐5791 adapter -- clustering structure -- ASK -- PSK -- QAM -- LabVIEW -- reconfigurable platform -- RF transmitter -- additive white Gaussian noise channel; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0922
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Blind signal modulation recognition is an essential block for designing a cognitive radio. Different algorithms are developed in the literature, but few are given with detailed implementation. This study proposes a software‐defined radio based implementation of blind signal modulation recogniser (BSMR) on field‐programmable gate array (FPGA), which works without any prior knowledge of the received signal. The algorithm estimates carrier frequency offset, symbol rate, symbol timing offset, and corrects the signal for these offsets to extract constellation points. It uses clustering structure formed by constellation signature in I/Q plane to detect the modulation for different orders of ASK, PSK, and QAM. The proposed algorithm is deployed on FPGA, using LabVIEW, for a reliable and reconfigurable platform. The algorithm is optimised to use minimum hardware resources and facilitate future up‐gradation. The system developed by implementing the algorithm on NI‐FlexRIO‐7975 FPGA module with NI‐5791 adapter detects modulation type in real time without any training. Signals for testing are generated using NI‐PXIe‐5673 (RF transmitter), and BSMR identifies the modulation type in 81.451 ms under additive white Gaussian noise channel.