Deep Learning for Spectral Filling in Radio Frequency Applications

• URL: http://arxiv.org/abs/2204.01536v1
• Date: Thu, 31 Mar 2022 20:31:54 GMT
• Title: Deep Learning for Spectral Filling in Radio Frequency Applications
• Authors: Matthew Setzler, Elizabeth Coda, Jeremiah Rounds, Michael Vann, and Michael Girard
• Abstract summary: We present methods for applying deep neural networks for spectral filling. We learn novel modulation schemes for sending extra information, in the form of additional messages, "around" the fixed-modulation signals. In so doing, we effectively increase channel capacity without increasing bandwidth.
• Score: 0.7829352305480285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF) channels are increasingly congested with new kinds of devices, which carry unique and diverse communication needs. This poses complex challenges in modern digital communications, and calls for the development of technological innovations that (i) optimize capacity (bitrate) in limited bandwidth environments, (ii) integrate cooperatively with already-deployed RF protocols, and (iii) are adaptive to the ever-changing demands in modern digital communications. In this paper we present methods for applying deep neural networks for spectral filling. Given an RF channel transmitting digital messages with a pre-established modulation scheme, we automatically learn novel modulation schemes for sending extra information, in the form of additional messages, "around" the fixed-modulation signals (i.e., without interfering with them). In so doing, we effectively increase channel capacity without increasing bandwidth. We further demonstrate the ability to generate signals that closely resemble the original modulations, such that the presence of extra messages is undetectable to third-party listeners. We present three computational experiments demonstrating the efficacy of our methods, and conclude by discussing the implications of our results for modern RF applications.

Related papers

• Augmenting Training Data with Vector-Quantized Variational Autoencoder for Classifying RF Signals [9.99212997328053]
  This paper proposes the use of a Vector-Quantized Variational Autoencoder (VQ-VAE) to augment training data. The VQ-VAE model generates high-fidelity synthetic RF signals, increasing the diversity and fidelity of the training dataset. Our experimental results show that incorporating VQ-VAE-generated data significantly improves the classification accuracy of the baseline model.
  arXiv  Detail & Related papers  (2024-10-23T21:17:45Z)
• Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning [68.63990729719369]
  The wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. This paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals. We develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters.
  arXiv  Detail & Related papers  (2024-10-23T15:36:43Z)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Diff-GO: Diffusion Goal-Oriented Communications to Achieve Ultra-High Spectrum Efficiency [46.92279990929111]
  This work presents an ultra-efficient communication design by utilizing generative AI-based on diffusion models. We propose a new low-dimensional noise space for the training of diffusion models, which significantly reduces the communication overhead. Our experimental results demonstrate that the proposed noise space and the diffusion-based generative model achieve ultra-high spectrum efficiency and accurate recovery of transmitted image signals.
  arXiv  Detail & Related papers  (2023-11-13T17:52:44Z)
• Deep Reinforcement Learning for Interference Management in UAV-based 3D Networks: Potentials and Challenges [137.47736805685457]
  We show that interference can still be effectively mitigated even without knowing its channel information. By harnessing interference, the proposed solutions enable the continued growth of civilian UAVs.
  arXiv  Detail & Related papers  (2023-05-11T18:06:46Z)
• Multi-task Learning Approach for Modulation and Wireless Signal Classification for 5G and Beyond: Edge Deployment via Model Compression [1.218340575383456]
  Future communication networks must address the scarce spectrum to accommodate growth of heterogeneous wireless devices. We exploit the potential of deep neural networks based multi-task learning framework to simultaneously learn modulation and signal classification tasks. We provide a comprehensive heterogeneous wireless signals dataset for public use.
  arXiv  Detail & Related papers  (2022-02-26T14:51:02Z)
• Three-Way Deep Neural Network for Radio Frequency Map Generation and Source Localization [67.93423427193055]
  Monitoring wireless spectrum over spatial, temporal, and frequency domains will become a critical feature in beyond-5G and 6G communication technologies. In this paper, we present a Generative Adversarial Network (GAN) machine learning model to interpolate irregularly distributed measurements across the spatial domain.
  arXiv  Detail & Related papers  (2021-11-23T22:25:10Z)
• Digital Signal Processing Using Deep Neural Networks [2.624902795082451]
  We present a custom deep neural network (DNN) specially designed to solve problems in the RF domain. Our model leverages the mechanisms of feature extraction and attention through the combination of an autoencoder convolutional network with a transformer network. We also present a new open dataset and physical data augmentation model that enables training of DNNs that can perform automatic modulation classification, infer and correct transmission channel effects, and directly demodulate baseband RF signals.
  arXiv  Detail & Related papers  (2021-09-21T18:59:32Z)
• ChaRRNets: Channel Robust Representation Networks for RF Fingerprinting [0.0]
  We present complex-valued Convolutional Neural Networks (CNNs) for RF fingerprinting. We focus on the problem of fingerprinting wireless IoT devices in-the-wild using Deep Learning (DL) techniques.
  arXiv  Detail & Related papers  (2021-05-08T03:03:21Z)
• Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder [29.590446724625693]
  We present a learning framework based on an LSTM denoising auto-encoder designed to automatically extract stable and robust features from noisy radio signals. Results on realistic synthetic as well as over-the-air radio data demonstrate that the proposed framework reliably and efficiently classifies received radio signals.
  arXiv  Detail & Related papers  (2020-11-16T21:41:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.