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http://dx.doi.org/10.3837/tiis.2022.11.016

Modulation Recognition of BPSK/QPSK Signals based on Features in the Graph Domain  

Yang, Li (College of Electronic and Information Engineering, Jinling Institute of Technology)
Hu, Guobing (College of Electronic and Information Engineering, Jinling Institute of Technology)
Xu, Xiaoyang (College of Network Communication Engineering, Jinling Institute of Technology)
Zhao, Pinjiao (College of Electronic and Information Engineering, Jinling Institute of Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.16, no.11, 2022 , pp. 3761-3779 More about this Journal
Abstract
The performance of existing recognition algorithms for binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals degrade under conditions of low signal-to-noise ratios (SNR). Hence, a novel recognition algorithm based on features in the graph domain is proposed in this study. First, the power spectrum of the squared candidate signal is truncated by a rectangular window. Thereafter, the graph representation of the truncated spectrum is obtained via normalization, quantization, and edge construction. Based on the analysis of the connectivity difference of the graphs under different hypotheses, the sum of degree (SD) of the graphs is utilized as a discriminate feature to classify BPSK and QPSK signals. Moreover, we prove that the SD is a Schur-concave function with respect to the probability vector of the vertices (PVV). Extensive simulations confirm the effectiveness of the proposed algorithm, and its superiority to the listed model-driven-based (MDB) algorithms in terms of recognition performance under low SNRs and computational complexity. As it is confirmed that the proposed method reduces the computational complexity of existing graph-based algorithms, it can be applied in modulation recognition of radar or communication signals in real-time processing, and does not require any prior knowledge about the training sets, channel coefficients, or noise power.
Keywords
Graph representation; Modulation recognition; Majorization inequality; Phase modulation signal; Schur concavity;
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