no code implementations • 3 Jan 2024 • Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
This paper explores opportunities and challenges of task (goal)-oriented and semantic communications for next-generation (NextG) communication networks through the integration of multi-task learning.
no code implementations • 28 Dec 2023 • Yalin E. Sagduyu, Tugba Erpek, Yi Shi
This paper studies the poisoning attack and defense interactions in a federated learning (FL) system, specifically in the context of wireless signal classification using deep learning for next-generation (NextG) communications.
no code implementations • 27 Dec 2023 • Yalin E. Sagduyu, Tugba Erpek
Results presented in this paper quantify the level of transferability of adversarial attacks on different LoRa signal classification tasks as a major vulnerability and highlight the need to make IoT applications robust to adversarial attacks.
no code implementations • 21 Dec 2023 • Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
Recognizing the computational constraints and trust issues associated with on-device computation, we propose a collaborative system wherein the edge device communicates selectively processed information to a trusted receiver acting as a fusion center, where a decision is made to identify whether a potential transmitter is present, or not.
no code implementations • 11 Dec 2023 • Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task.
no code implementations • 8 Nov 2023 • Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
The transmitter employs a deep neural network, namely an encoder, for joint operations of source coding, channel coding, and modulation, while the receiver utilizes another deep neural network, namely a decoder, for joint operations of demodulation, channel decoding, and source decoding to reconstruct the data samples.
no code implementations • 14 Aug 2023 • Yalin E. Sagduyu, Tugba Erpek, Aylin Yener, Sennur Ulukus
A multi-task deep learning approach that involves training a common encoder at the transmitter and individual decoders at the receivers is presented for joint optimization of completing multiple tasks and communicating with multiple receivers.
no code implementations • 12 Jan 2023 • Yi Shi, Yalin E. Sagduyu, Tugba Erpek
We show that the DFL performance can be significantly reduced by jamming attacks launched in a wireless network and characterize the attack surface as a vulnerability study before the safe deployment of DFL over wireless networks.
no code implementations • 11 Jan 2023 • Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener
This paper studies the notion of age in task-oriented communications that aims to execute a task at a receiver utilizing the data at its transmitter.
no code implementations • 27 Dec 2022 • Yi Shi, Yalin E. Sagduyu
In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier.
no code implementations • 22 Dec 2022 • Yalin E. Sagduyu
The performance in Nash equilibrium is compared to the fixed attack and defense cases, and the resilience of NextG signal classification against attacks is quantified.
no code implementations • 21 Dec 2022 • Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener
The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning.
no code implementations • 21 Dec 2022 • Yalin E. Sagduyu
This tradeoff leads to a non-cooperative game where each client aims to individually maximize its utility as the difference between the global model accuracy and the cost of FL participation.
no code implementations • 20 Dec 2022 • Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus, Aylin Yener
By augmenting the reconstruction loss with a semantic loss, the two deep neural networks (DNNs) of this encoder-decoder pair are interactively trained with the DNN of the semantic task classifier.
no code implementations • 19 Dec 2022 • Yalin E. Sagduyu, Sennur Ulukus, Aylin Yener
In this paper, wireless signal classification is considered as the task for the NextG Radio Access Network (RAN), where edge devices collect wireless signals for spectrum awareness and communicate with the NextG base station (gNodeB) that needs to identify the signal label.
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 • 6 Apr 2022 • Yi Shi, Yalin E. Sagduyu, Tugba Erpek
In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification.
no code implementations • 9 Mar 2022 • Ender Ayanoglu, Kemal Davaslioglu, Yalin E. Sagduyu
GANs have the following advantages.
no code implementations • 13 Jan 2022 • Yi Shi, Yalin E. Sagduyu
Federated learning (FL) offers a decentralized learning environment so that a group of clients can collaborate to train a global model at the server, while keeping their training data confidential.
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 • 21 Dec 2021 • Brian Kim, Tugba Erpek, Yalin E. Sagduyu, Sennur Ulukus
Results from different network topologies show that adversarial perturbation and RIS interaction vector can be jointly designed to effectively increase the signal detection accuracy at the receiver while reducing the detection accuracy at the eavesdropper to enable covert communications.
no code implementations • 8 Dec 2021 • Tugba Erpek, Yalin E. Sagduyu, Ahmed Alkhateeb, Aylin Yener
This paper presents a novel approach for the joint design of a reconfigurable intelligent surface (RIS) and a transmitter-receiver pair that are trained together as a set of deep neural networks (DNNs) to optimize the end-to-end communication performance at the receiver.
no code implementations • 16 Sep 2021 • Brian Kim, Yi Shi, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus
The DNN that corresponds to a regression model is trained with channel gains as the input and returns transmit powers as the output.
no code implementations • 22 Jul 2021 • Yi Shi, Yalin E. Sagduyu
An over-the-air membership inference attack (MIA) is presented to leak private information from a wireless signal classifier.
no code implementations • 9 Apr 2021 • Xueyuan Wang, M. Cenk Gursoy, Tugba Erpek, Yalin E. Sagduyu
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks.
no code implementations • 25 Mar 2021 • Brian Kim, Yalin E. Sagduyu, Tugba Erpek, Sennur Ulukus
Deep learning provides powerful means to learn from spectrum data and solve complex tasks in 5G and beyond such as beam selection for initial access (IA) in mmWave communications.
no code implementations • 22 Feb 2021 • Nof Abuzainab, Muhammad Alrabeiah, Ahmed Alkhateeb, Yalin E. Sagduyu
To integrate RISs into THz drone communications, we propose a novel deep learning solution based on a recurrent neural network, namely the Gated Recurrent Unit (GRU), that proactively predicts the serving base station/RIS and the serving beam for each drone based on the prior observations of drone location/beam trajectories.
Information Theory Networking and Internet Architecture Information Theory
no code implementations • 21 Jan 2021 • Yi Shi, Yalin E. Sagduyu
We show that the portion of the reward achieved by real requests may be much less than the reward that would be achieved when there was no attack.
no code implementations • 14 Jan 2021 • Yi Shi, Yalin E. Sagduyu, Tugba Erpek, M. Cenk Gursoy
In this paper, reinforcement learning (RL) for network slicing is considered in NextG radio access networks, where the base station (gNodeB) allocates resource blocks (RBs) to the requests of user equipments and aims to maximize the total reward of accepted requests over time.
Networking and Internet Architecture
no code implementations • 7 Jan 2021 • Yalin E. Sagduyu, Tugba Erpek, Yi Shi
For the second attack, the adversary spoofs wireless signals with the generative adversarial network (GAN) to infiltrate the physical layer authentication mechanism based on a deep learning classifier that is deployed at the 5G base station.
no code implementations • 6 Jan 2021 • Tarun S. Cousik, Vijay K. Shah, Tugba Erpek, Yalin E. Sagduyu, Jeffrey H. Reed
In LoS conditions, the selection of the beams is consequential and improves the accuracy by up to 70%.
no code implementations • 28 Dec 2020 • Damilola Adesina, Chung-Chu Hsieh, Yalin E. Sagduyu, Lijun Qian
In addition, an holistic survey of existing research on AML attacks for various wireless communication problems as well as the corresponding defense mechanisms in the wireless domain are presented.
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 • 14 Sep 2020 • Yi Shi, Yalin E. Sagduyu, Tugba Erpek
The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio access network slicing.
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 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Yalin E. Sagduyu, Jie Xu
In this paper, we propose an adversarial machine learning based partial-model attack in the data fusion/aggregation process of IoT by only controlling a small part of the sensing devices.
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 • 22 Jun 2020 • Tarun S. Cousik, Vijay K. Shah, Jeffrey H. Reed, Tugba Erpek, Yalin E. Sagduyu
This paper presents DeepIA, a deep learning solution for faster and more accurate initial access (IA) in 5G millimeter wave (mmWave) networks when compared to conventional IA.
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 • 12 May 2020 • Tugba Erpek, Timothy J. O'Shea, Yalin E. Sagduyu, Yi Shi, T. Charles Clancy
Existing communication systems exhibit inherent limitations in translating theory to practice when handling the complexity of optimization for emerging wireless applications with high degrees of freedom.
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 • 24 Jan 2020 • Yalin E. Sagduyu, Yi Shi, Tugba Erpek, William Headley, Bryse Flowers, George Stantchev, Zhuo Lu
Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium.
no code implementations • 1 Nov 2019 • Yalin E. Sagduyu, Yi Shi, Tugba Erpek
A transmitter applies deep learning on its spectrum sensing results to predict idle time slots for data transmission.
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 • 31 May 2019 • Yalin E. Sagduyu, Yi Shi, Tugba Erpek
While there is a surge of interest to understand the security issues of machine learning, their implications have not been understood yet for wireless applications such as those in IoT systems that are susceptible to various attacks due the open and broadcast nature of wireless communications.
no code implementations • 4 May 2019 • Zhengping Luo, Shangqing Zhao, Zhuo Lu, Jie Xu, Yalin E. Sagduyu
In this paper, we revisit this security vulnerability as an adversarial machine learning problem and propose a novel learning-empowered attack framework named Learning-Evaluation-Beating (LEB) to mislead the fusion center.
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 • 26 Jan 2019 • Yi Shi, Tugba Erpek, Yalin E. Sagduyu, Jason H. Li
We consider the case that a cognitive transmitter senses the spectrum and transmits on idle channels determined by a machine learning algorithm.
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 • 3 Jul 2018 • Tugba Erpek, Yalin E. Sagduyu, Yi Shi
An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is presented.
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.