no code implementations • 6 Sep 2023 • Ziv Aharoni, Bashar Huleihel, Henry D. Pfister, Haim H. Permuter
The proposed method leverages the structure of the successive cancellation (SC) decoder to devise a neural SC (NSC) decoder.
no code implementations • 11 Jan 2023 • Yara Huleihel, Haim H. Permuter
An enhanced framework for peak-to-average power ratio ($\mathsf{PAPR}$) reduction and waveform design for Multiple-Input-Multiple-Output ($\mathsf{MIMO}$) orthogonal frequency-division multiplexing ($\mathsf{OFDM}$) systems, based on a convolutional-autoencoder ($\mathsf{CAE}$) architecture, is presented.
no code implementations • 7 Jan 2023 • Stefan Feintuch, Joseph Tabrikian, Igal Bilik, Haim H. Permuter
Therefore, this work proposes a neural network (NN) based DOA estimation approach for spatial spectrum estimation in multi-source scenarios with a-priori unknown number of sources in the presence of non-Gaussian spatially-colored interference.
no code implementations • 21 Oct 2022 • Stefan Feintuch, Haim H. Permuter, Igal Bilik, Joseph Tabrikian
This work addresses the problem of range-Doppler multiple target detection in a radar system in the presence of slow-time correlated and heavy-tailed distributed clutter.
no code implementations • 12 Nov 2020 • Yara Huleihel, Eilam Ben-Dror, Haim H. Permuter
This paper introduces the architecture of a convolutional autoencoder (CAE) for the task of peak-to-average power ratio (PAPR) reduction and waveform design, for orthogonal frequency division multiplexing (OFDM) systems.
3 code implementations • 11 Jan 2012 • Jiantao Jiao, Haim H. Permuter, Lei Zhao, Young-Han Kim, Tsachy Weissman
Four estimators of the directed information rate between a pair of jointly stationary ergodic finite-alphabet processes are proposed, based on universal probability assignments.
Information Theory Information Theory
