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Found 19 papers, 0 papers with code
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems
By approximating the Q-values with a DQN, DRL is implemented for the embedded platform of each node combining an ARM processor and a WiFi transceiver for 802. 11n.
Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning
Self-supervised learning (SSL) enables the learning of useful representations from Radio Frequency (RF) signals themselves even when only limited training data samples with labels are available.
Machine Learning in NextG Networks via Generative Adversarial Networks
GANs have the following advantages.
End-to-End Autoencoder Communications with Optimized Interference Suppression
An end-to-end communications system based on Orthogonal Frequency Division Multiplexing (OFDM) is modeled as an autoencoder (AE) for which the transmitter (coding and modulation) and receiver (demodulation and decoding) are represented as deep neural networks (DNNs) of the encoder and decoder, respectively.
Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers
The transmitter is equipped with a deep neural network (DNN) classifier for detecting the ongoing transmissions from the background emitter and transmits a signal if the spectrum is idle.
Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers
First, we show that multiple independent adversaries, each with a single antenna cannot improve the attack performance compared to a single adversary with multiple antennas using the same total power.
Over-the-Air Membership Inference Attacks as Privacy Threats for Deep Learning-based Wireless Signal Classifiers
As machine learning (ML) algorithms are used to process wireless signals to make decisions such as PHY-layer authentication, the training data characteristics (e. g., device-level information) and the environment conditions (e. g., channel information) under which the data is collected may leak to the ML model.
How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy
We consider the problem of hiding wireless communications from an eavesdropper that employs a deep learning (DL) classifier to detect whether any transmission of interest is present or not.
Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers
There is a transmitter that transmits signals with different modulation types.
Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels
In the meantime, the adversary makes over-the-air transmissions that are received as superimposed with the transmitter's signals to fool the classifier at the receiver into making errors.
DeepWiFi: Cognitive WiFi with Deep Learning
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802. 11ac) with deep learning and sustains high throughput by mitigating out-of-network interference.
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning
A deep learning classifier is considered to classify wireless signals using raw (I/Q) samples as features and modulation types as labels.
Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification
We designed and implemented a deep learning based RF signal classifier on the Field Programmable Gate Array (FPGA) of an embedded software-defined radio platform, DeepRadio, that classifies the signals received through the RF front end to different modulation types in real time and with low power.
QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning
The problem of quality of service (QoS) and jamming-aware communications is considered in an adversarial wireless network subject to external eavesdropping and jamming attacks.
Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments
Dynamic spectrum access (DSA) benefits from detection and classification of interference sources including in-network users, out-network users, and jammers that may all coexist in a wireless network.
Generative Adversarial Network for Wireless Signal Spoofing
Building upon deep learning techniques, this paper introduces a spoofing attack by an adversary pair of a transmitter and a receiver that assume the generator and discriminator roles in the GAN and play a minimax game to generate the best spoofing signals that aim to fool the best trained defense mechanism.
Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
The exploratory attack with limited training data is shown to fail to reliably infer the target classifier of a real text classifier API that is available online to the public.
BIG-bench Machine Learning
Generative Adversarial Network
+1
Active Deep Learning Attacks under Strict Rate Limitations for Online API Calls
To mitigate the impact of limited training data, we develop an active learning approach that first builds a classifier based on a small number of API calls and uses this classifier to select samples to further collect their labels.
Generative Adversarial Learning for Spectrum Sensing
A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio.
