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 20 of 26 for “"modulation classification"”.
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Modulation classification of digital communication signals
Modulation classification of digital communications signals plays an important role in both military and civilian sectors. It has the potential of replacing several receivers with one universal receiver. An automatic modulation classifier can be defined as a system that automatically identifies the …
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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
… 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
… 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 (SNRs) …
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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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Radar intra-pulse modulation classification using convolutional neural networks
… presents a detailed investigation into the classification of radar intra-pulse modulation schemes. Recent years have seen increased waveform diversity in radar systems which, while making many aspects of pulse analysis more challenging, have presented new opportunities and features for the …
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Signal Detection and Modulation Classification in Non-Gaussian Noise Environments
Signal detection and modulation classification are becoming increasingly important in a variety of wireless communication systems such as those involving spectrum management and electronic warfare and surveillance, among others. The majority of the signal detection and modulation classification …
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Spectrum Sensing and Blind Automatic Modulation Classification in Real-Time
… 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 signal …
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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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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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Rapid Radio: Analysis-Based Receiver Deployment
… 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 perfect …
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Blind recognition of analog modulation schemes for software defined radio
… 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. Hence, the detection …
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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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Spectrum Sensing in the Presence of Channel and Tx/Rx Impairments
… will be necessary. Furthermore, detection and classification among the secondary users themselves will be important for efficient spectrum usage in these systems. As another example, spectrum sensing is also of critical importance in many military applications. This is due to the inherent …
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Applications of Sensor Fusion to Classification, Localization and Mapping
… 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 applications. Although …
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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 …
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On the Use of Convolutional Neural Networks for Specific Emitter Identification
… to perform signal processing tasks such as modulation classification, without the need for pre-defined expert features. Given this success, the work presented in this thesis investigates the ability to use CNNs, in place of a traditional expert-defined feature extraction process, to improve …
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Approaches to Multiple-source Localization and Signal Classification
… For the second technique, the process of signal classification is considered as another approach to the data association problem. Environments in which each signal possesses unique features can be exploited to separate signals at each sensor by their characteristics, which mitigates the …
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Combining RF Machine Learning and RF Photonics to Enable New Analog Communications Architectures
… signal estimation as well as a 3-layer DNN for modulation classification of raw RF signals, thus being the first recorded analog hardware accelerator to ever perform deep learning directly on raw RF signals. We also present a system-level analysis to quantitatively show that the MAFT …
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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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