Journal ArticleDOI
On the likelihood-based approach to modulation classification
TLDR
Findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution, and an upper bound on the performance of QHL RT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed.Abstract:
In this paper, likelihood-based algorithms are explored for linear digital modulation classification. Hybrid likelihood ratio test (HLRT)- and quasi HLRT (QHLRT)- based algorithms are examined, with signal amplitude, phase, and noise power as unknown parameters. The algorithm complexity is first investigated, and findings show that the HLRT suffers from very high complexity, whereas the QHLRT provides a reasonable solution. An upper bound on the performance of QHLRT-based algorithms, which employ unbiased and normally distributed non-data aided estimates of the unknown parameters, is proposed. This is referred to as the QHLRT-Upper Bound (QHLRT-UB). Classification of binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals is presented as a case study. The Cramer-Rao Lower Bounds (CRBs) of non-data aided joint estimates of signal amplitude and phase, and noise power are derived for BPSK and QPSK signals, and further employed to obtain the QHLRT-UB. An upper bound on classification performance of any likelihood-based algorithms is also introduced. Method-of-moments (MoM) estimates of the unknown parameters are investigated and used to develop the QHLRT-based algorithm. Classification performance of this algorithm is compared with the upper bounds, as well as with the quasi Log-Likelihood Ratio (qLLR) and fourth-order cumulant based algorithms.read more
Citations
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Journal ArticleDOI
Likelihood-Ratio Approaches to Automatic Modulation Classification
TL;DR: This survey paper focuses on the automatic modulation classification methods based on likelihood functions, studies various classification solutions derived from likelihood ratio test, and discusses the detailed characteristics associated with all major algorithms.
Journal ArticleDOI
Signal identification for emerging intelligent radios: classical problems and new challenges
TL;DR: A snapshot of the status of signal identification algorithms is presented, starting from a general description of maximum likelihood (ML) and feature based (FB) approaches to a more detailed discussion of a practical methodology using cyclostationarity-based features.
Journal ArticleDOI
Specific Emitter Identification via Hilbert–Huang Transform in Single-Hop and Relaying Scenarios
TL;DR: This paper investigates the specific emitter identification (SEI) problem, which distinguishes different emitters using features generated by the nonlinearity of the power amplifiers of emitters, and three algorithms based on the Hilbert spectrum are proposed that show effectiveness in both single-hop and relaying scenarios, as well as under different channel conditions.
Journal ArticleDOI
MCNet: An Efficient CNN Architecture for Robust Automatic Modulation Classification
TL;DR: A cost-efficient convolutional neural network for robust automatic modulation classification (AMC) deployed for cognitive radio services of modern communication systems and achieves the overall 24-modulation classification rate of 93.59% at 20 dB SNR on the well-known DeepSig dataset.
Journal ArticleDOI
Automatic Modulation Classification Using Convolutional Neural Network With Features Fusion of SPWVD and BJD
TL;DR: This paper presents a scheme of features fusion for AMC using convolutional neural network (CNN) to fuse different images and handcrafted features of signals to obtain more discriminating features.
References
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Journal ArticleDOI
Fundamentals of statistical signal processing: estimation theory
TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Journal ArticleDOI
Survey of automatic modulation classification techniques: classical approaches and new trends
TL;DR: The authors provide a comprehensive survey of different modulation recognition techniques in a systematic way, and simulated some major techniques under the same conditions, which allows a fair comparison among different methodologies.
Book
Synchronization Techniques for Digital Receivers
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TL;DR: The Principles, Methods and Performance Limits of Carrier Frequency Recovery with Linear Modulations and Timing Recovery with CPM Modulations are presented.
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