no code implementations • 22 Jan 2024 • Jiayu Mao, Aylin Yener
Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels.
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 • 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 • 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 • 19 Sep 2023 • Emrecan Kutay, Aylin Yener
We study semantic compression for text where meanings contained in the text are conveyed to a source decoder, e. g., for classification.
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 • 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 • 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 • 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 • 19 Jul 2022 • Deniz Gunduz, Zhijin Qin, Inaki Estella Aguerri, Harpreet S. Dhillon, Zhaohui Yang, Aylin Yener, Kai Kit Wong, Chan-Byoung Chae
Communication systems to date primarily aim at reliably communicating bit sequences.
no code implementations • 12 May 2022 • Haibo Yang, Peiwen Qiu, Jia Liu, Aylin Yener
In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device.
no code implementations • 22 Feb 2022 • Onur Günlü, Matthieu Bloch, Rafael F. Schaefer, Aylin Yener
For independent and identically distributed states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed.
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 • 22 Feb 2021 • Basak Guler, Aylin Yener
Potential environmental impact of machine learning by large-scale wireless networks is a major challenge for the sustainability of future smart ecosystems.
no code implementations • 10 Feb 2021 • Basak Guler, Aylin Yener
This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks.
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.