Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"Automatic modulation classification"”.
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Automatic modulation classification of communication signals
The automatic modulation recognition (AMR) plays an important role in various civilian and military applications. Most of the existing AMR algorithms assume that the input signal is only of analog modulation or is only of digital modulation. In blind environments, however, it is impossible to know …
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Automatic Modulation Classification Using Grey Relational Analysis
… and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that …
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Gated Transformer-Based Architecture for Automatic Modulation Classification
… at the radio receiver through AI-driven automatic modulation classification. Our methodology is centered around the transformer encoder architecture incorporating a multi-head self-attention mechanism. We train our architecture extensively across a diverse range of signal-to-noise ratios …
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Advanced methods in automatic modulation classification for emerging technologies
Modulation classification (MC) is of large importance in both military and commercial communication applications. It is a challenging problem, especially in non-cooperative wireless environments, where channel fading and no prior knowledge on the incoming signal are major factors that deteriorate …
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Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time
… implementation of a scanning signal detector and automatic modulation classification system. The classification technique is a completely blind method, with no prior knowledge of the signal's center frequency, bandwidth, or symbol rate. An energy detector forms the initial approximations of the …
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Real-World Considerations for Deep Learning in Spectrum Sensing
Recently, automatic modulation classification techniques using deep neural networks on raw IQ samples have been investigated and show promise when compared to more traditional likelihood-based or feature-based techniques. While likelihood-based and feature-based techniques are effective, making …
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Applications of Sensor Fusion to Classification, Localization and Mapping
… greatly benefit from sensor fusion algorithms: Automatic Modulation Classification (AMC) and indoor localization and mapping based on smartphone sensors. Automatic Modulation Classification is a key technology in Cognitive Radio (CR) networks, spectrum sharing, and wireless military …
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Rapid Radio: Analysis-Based Receiver Deployment
… implementation time and increased exibility. Automatic modulation classification is done with blind parameter estimation. Unlike other contemporary work, no a priori knowledge about the signal being classified is assumed. This leads to the development of a system that does not depend on …
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Blind recognition of analog modulation schemes for software defined radio
… timing, and signal to noise ratio (SNR), and automatically identify modulation schemes. In this dissertation research, several fundamental SDR tasks for analog modulations are investigated, since analog radios are often used by civil government agencies and some unconventional military forces. …
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Advanced classification of OFDM and MIMO signals with enhanced second order cyclostationarity detection
… of cognitive radio and the introduction of new modulation techniques such as OFDM and MIMO, the problem of Modulation Classification (MC) becomes more challenging and complicated. In the first part of the thesis, we explore the automatic modulation classification to blindly distinguish OFDM from …
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Sensitivity Analysis of RFML-based SEI Algorithms
… Machine Learning (RFML) techniques for the classification tasks of Specific Emitter Identification (SEI) and Automatic Modulation Classification (AMC) have seen rapid improvements in recent years. The applications of SEI, a technique used to associate a received signal to an emitter, and …
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Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods
… (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum …
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Heterogeneous Sensor Signal Processing for Inference with Nonlinear Dependence
… the system design particularly. We consider the classification of discrete random signals in Wireless Sensor Networks (WSNs), where, for communication efficiency, only local decisions are transmitted. We derive the necessary conditions for the optimal decision rules at the sensors and the FC by …
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Cyclostationarity Feature-Based Detection and Classification
… feature-based (C-FB) detection and classification is a large field of research that has promising applications to intelligent receiver design. Cyclostationarity FB classification and detection algorithms have been applied to a breadth of wireless communication signals — analog and …
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Real-World Considerations for RFML Applications
… identity of a received RF signal, and automated modulation classification (AMC), determining the modulation scheme of a received RF transmission. Both tasks have a number of algorithms that are effective on simulated data, but struggle to generalize to data collected in the real-world, partially …