no code implementations • 7 Jun 2024 • Antti Koskela, Jafar Mohammadi
Previous auditing methods tightly capture the privacy guarantees of DP-SGD trained models in the white-box setting where the auditor has access to all intermediate models; however, the success of these methods depends on a priori information about the parametric form of the noise and the subsampling ratio used for sampling the gradients.
no code implementations • 5 Jun 2024 • Dileepa Marasinghe, Le Hang Nguyen, Jafar Mohammadi, Yejian Chen, Thorsten Wild, Nandana Rajatheva
The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G.
no code implementations • 8 Jun 2023 • Nuwanthika Rajapaksha, Jafar Mohammadi, Stefan Wesemann, Thorsten Wild, Nandana Rajatheva
In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements.
no code implementations • 30 Sep 2021 • Jafar Mohammadi, Gerhard Schreiber, Thorsten Wild, Yejian Chen
Instead, we propose to use NN only for \textit{blind} coherent combining of the signals in the detector to compensate for the channel effect, thus maximize the signal to noise ratio.
no code implementations • 28 Oct 2020 • Yejian Chen, Jafar Mohammadi, Stefan Wesemann, Thorsten Wild
Furthermore, we propose an iterative training approach, referred to as Turbo-AI.
no code implementations • 1 Jul 2020 • Alessandro Brighente, Jafar Mohammadi, Paolo Baracca
Motivated by the fact that most of the URLLC use cases with most extreme latency and reliability requirements are characterized by semi-deterministic traffic, we propose to exploit the time correlation of the interference to compute useful statistics needed to predict the interference power in the next transmission.
no code implementations • 28 Oct 2019 • Hafiz Imtiaz, Jafar Mohammadi, Rogers Silva, Bradley Baker, Sergey M. Plis, Anand D. Sarwate, Vince Calhoun
In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting.
2 code implementations • 22 Apr 2019 • Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate
CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems.