17 citations · 65 across the 51 of their papers we have counts for
8 papers · 1 filter
Dynamic and Static Energy Efficient Design of Pinching Antenna Systems
Saba Asaad, Chongjun Ouyang, Ali Bereyhi +1
We study the energy efficiency of pinching-antenna systems (PASSs) by developing a consistent formulation for power distribution in these systems. The per-antenna power distributio…
Revisiting GAN with Bayes-Optimal Discrimination
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
We propose an alternative to the standard GAN training approach, in which the discriminator is a binary classifier trained by cross-entropy to distinguish real samples from generat…
Coding for Computation: Efficient Compression of Neural Networks for Reconfigurable Hardware
Hans Rosenberger, Rodrigo Fischer, Johanna S. Fröhlich +2
As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression s…
MIMO-PASS: Uplink and Downlink Transmission via MIMO Pinching-Antenna Systems
Ali Bereyhi, Chongjun Ouyang, Saba Asaad +2
Pinching-antenna systems (PASSs) are a recent flexible-antenna technology that is realized by attaching simple components, referred to as pinching elements, to dielectric waveguide…
Downlink Beamforming with Pinching-Antenna Assisted MIMO Systems
Ali Bereyhi, Saba Asaad, Chongjun Ouyang +2
Pinching antennas have been recently proposed as a promising flexible-antenna technology, which can be implemented by attaching low-cost pinching elements to dielectric waveguides.…
BOLT: Universal Classifier Training via a Bayes-Error Upper Bound
Mohammadreza Tavasoli Naeini, Ali Bereyhi, Morteza Noshad +2
This work introduces a novel approach to compute a universal upper bound on the Bayes error rate (BER) from classifier-output samples, which serves as a Bayes-error-related perform…
Over-the-Air Fair Federated Learning via Multi-Objective Optimization
Shayan Mohajer Hamidi, Ali Bereyhi, Saba Asaad +1
In federated learning (FL), heterogeneity among the local dataset distributions of clients can result in unsatisfactory performance for some, leading to an unfair model. To address…
Regularized Top-: A Bayesian Framework for Gradient Sparsification
Ali Bereyhi, Ben Liang, Gary Boudreau +1
Error accumulation is effective for gradient sparsification in distributed settings: initially-unselected gradient entries are eventually selected as their accumulated error exceed…