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Spectrum Sensing and Signal Classification |
Device Identification |
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Identify signals in a wideband spectrum using deep learning techniques. Perform waveform modulation classification using deep learning networks.
💡 Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals
💡 Modulation Classification with Deep Learning
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Develop radio frequency (RF) fingerprinting methods to identify various devices and detect device impersonators.
💡 Design a Deep Neural Network with Simulated Data to Detect WLAN Router Impersonation 💡 Test a Deep Neural Network with Captured Data to Detect WLAN Router Impersonation
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Digital Pre-DistortionApply neural network-based digital predistortion (DPD) to offset the effects of nonlinearities in a power amplifier (PA).
💡 Neural Network for Digital Predistortion Design – Offline Training
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Beam Management and Channel EstimationUse a neural network to reduce the computational complexity in the 5G NR beam selection task. Train a CNN for 5G NR channel estimation.
💡 Neural Network for Beam Selection
💡 Deep Learning Data Synthesis for 5G Channel Estimation
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Localization and PositioningUse generated IEEE ® 802.11az™ data to train a CNN for localization and positioning.
💡 Three-Dimensional Indoor Positioning with 802.11az Fingerprinting and Deep Learning
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Transceiver DesignUse an unsupervised neural network that learns how to efficiently compress and decompress data, forming an autoencoder. Train and test a neural network to estimate likelihood ratios (LLR).
📖 Autoencoders for Wireless Communications
📖 Training and Testing a Neural Network for LLR Estimation
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ProductsLearn about the products used with AI for wireless applications.
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