Octavia A. Dobre

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_ The automatic recognition of the modulation format of a detected signal, the intermediate step between signal detection and demodulation, is a major task of an intelligent receiver, with various civilian and military applications. Obviously, with no knowledge of the transmitted data and many unknown parameters at the receiver, such as the signal power,(More)
In this paper we address the problem of identifying the modulation format of an incoming signal. We review many existing techniques for digital modulation recognition in a systematic way, which helps the reader to see the main features of each technique. The goal is to provide useful guidelines for choosing appropriate classification algorithms for(More)
Modulation classification is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications. In this correspondence, higher-order cyclic cumulants (CCs) are explored to discriminate linear digital modulations in flat fading channels. Singleandmulti-antenna CC-based classifiers are(More)
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(More)
Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, with both military and civilian applications. MC is a challenging task, especially in a non-cooperative environment, as no prior information on the incoming signal is available at the receiver. In this paper, we investigate classification of linear(More)
Blind modulation classification (MC) is an intermediate step between signal detection and demodulation, and plays a key role in various civilian and military applications In this paper, first we provide an overview of decision-theoretic MC approaches. Then we derive the average likelihood ratio (ALR) based classifier for linear and nonlinear modulations, in(More)
Non-orthogonal multiple access (NOMA) is one of the promising radio access techniques for performance enhancement in next-generation cellular communications. Compared to orthogonal frequency division multiple access (OFDMA), which is a well-known high-capacity orthogonal multiple access (OMA) technique, NOMA offers a set of desirable benefits, including(More)
Spectrum sensing and awareness are challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to detect the presence and classify the on-the-air signals. The wireless industry has shown great interest in orthogonal frequency division multiplexing (OFDM) technology. Hence,(More)