no code implementations • 2 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.
no code implementations • 7 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.
no code implementations • 9 Mar 2022 • Ender Ayanoglu, Kemal Davaslioglu, Yalin E. Sagduyu
GANs have the following advantages.
no code implementations • 29 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.
no code implementations • 3 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.
no code implementations • 31 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.
no code implementations • 25 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.
no code implementations • 15 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.
no code implementations • 11 May 2020 • Brian Kim, Yalin E. Sagduyu, Kemal Davaslioglu, Tugba Erpek, Sennur Ulukus
There is a transmitter that transmits signals with different modulation types.
no code implementations • 5 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.
no code implementations • 29 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.
no code implementations • 23 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.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 25 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.
no code implementations • 3 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.
no code implementations • 25 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
no code implementations • 5 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.
no code implementations • 2 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.