Search Results for author: Kemal Davaslioglu

Found 19 papers, 0 papers with code

Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems

no code implementations2 Nov 2022 Ziad El Jamous, Kemal Davaslioglu, Yalin E. Sagduyu

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.

Q-Learning reinforcement-learning +1

Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning

no code implementations7 Jul 2022 Kemal Davaslioglu, Serdar Boztas, Mehmet Can Ertem, Yalin E. Sagduyu, Ender Ayanoglu

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.

Automatic Modulation Recognition Representation Learning +2

End-to-End Autoencoder Communications with Optimized Interference Suppression

no code implementations29 Dec 2021 Kemal Davaslioglu, Tugba Erpek, Yalin E. Sagduyu

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.

Decoder Generative Adversarial Network +1

Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers

no code implementations3 Dec 2020 Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

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 Attack

Adversarial Attacks with Multiple Antennas Against Deep Learning-Based Modulation Classifiers

no code implementations31 Jul 2020 Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Kemal Davaslioglu, Sennur Ulukus

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

no code implementations25 Jun 2020 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu

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.

Inference Attack Membership Inference Attack

How to Make 5G Communications "Invisible": Adversarial Machine Learning for Wireless Privacy

no code implementations15 May 2020 Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

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.

BIG-bench Machine Learning

Over-the-Air Adversarial Attacks on Deep Learning Based Modulation Classifier over Wireless Channels

no code implementations5 Feb 2020 Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus

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.

Adversarial Attack

DeepWiFi: Cognitive WiFi with Deep Learning

no code implementations29 Oct 2019 Kemal Davaslioglu, Sohraab Soltani, Tugba Erpek, Yalin E. Sagduyu

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

no code implementations23 Oct 2019 Kemal Davaslioglu, Yalin E. Sagduyu

A deep learning classifier is considered to classify wireless signals using raw (I/Q) samples as features and modulation types as labels.

BIG-bench Machine Learning Classification +3

QoS and Jamming-Aware Wireless Networking Using Deep Reinforcement Learning

no code implementations13 Oct 2019 Nof Abuzainab, Tugba Erpek, Kemal Davaslioglu, Yalin E. Sagduyu, Yi Shi, Sharon J. Mackey, Mitesh Patel, Frank Panettieri, Muhammad A. Qureshi, Volkan Isler, Aylin Yener

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.

reinforcement-learning Reinforcement Learning (RL)

Real-Time and Embedded Deep Learning on FPGA for RF Signal Classification

no code implementations13 Oct 2019 Sohraab Soltani, Yalin E. Sagduyu, Raqibul Hasan, Kemal Davaslioglu, Hongmei Deng, Tugba Erpek

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.

General Classification

Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments

no code implementations25 Sep 2019 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu, William C. Headley, Michael Fowler, Gilbert Green

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.

blind source separation Classification +5

Generative Adversarial Network for Wireless Signal Spoofing

no code implementations3 May 2019 Yi Shi, Kemal Davaslioglu, Yalin E. Sagduyu

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 Network

Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data

no code implementations25 Jan 2019 Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li

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

no code implementations5 Nov 2018 Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Jason H. Li

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.

Active Learning BIG-bench Machine Learning +1

Generative Adversarial Learning for Spectrum Sensing

no code implementations2 Apr 2018 Kemal Davaslioglu, Yalin E. Sagduyu

A novel approach of training data augmentation and domain adaptation is presented to support machine learning applications for cognitive radio.

BIG-bench Machine Learning Data Augmentation +2

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