Experimental Validation of Coherent Joint Transmission in a Distributed-MIMO System with Analog Fronthaul for 6G

no code implementations • 4 May 2023 • Rafael Puerta, Mahdieh Joharifar, Mengyao Han, Anders Djupsjöbacka, Vjaceslavs Bobrovs, Sergei Popov, Oskars Ozolins, Xiaodan Pang

The sixth-generation (6G) mobile networks must increase coverage and improve spectral efficiency, especially for cell-edge users.

NR Conformance Testing of Analog Radio-over-LWIR FSO Fronthaul link for 6G Distributed MIMO Networks

no code implementations • 9 Feb 2023 • Rafael Puerta, Mengyao Han, Mahdieh Joharifar, Richard Schatz, Yan-Ting Sun, Yuchuan Fan, Anders Djupsjöbacka, Grégory Maisons, Johan Abautret, Roland Teissier, Lu Zhang, Sandis Spolitis, Muguang Wang, Vjaceslavs Bobrovs, Sebastian Lourdudoss, Xianbin Yu, Sergei Popov, Oskars Ozolins, Xiaodan Pang

We experimentally test the compliance with 5G/NR 3GPP technical specifications of an analog radio-over-FSO link at 9 {\mu}m. The ACLR and EVM transmitter requirements are fulfilled validating the suitability of LWIR FSO for 6G fronthaul.

Symbolic expression generation via Variational Auto-Encoder

no code implementations • 15 Jan 2023 • Sergei Popov, Mikhail Lazarev, Vladislav Belavin, Denis Derkach, Andrey Ustyuzhanin

There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature.

regression Symbolic Regression

Embedding Words in Non-Vector Space with Unsupervised Graph Learning

1 code implementation • EMNLP 2020 • Max Ryabinin, Sergei Popov, Liudmila Prokhorenkova, Elena Voita

We adopt a recent method learning a representation of data in the form of a differentiable weighted graph and use it to modify the GloVe training algorithm.

Graph Learning Word Embeddings +1

Editable Neural Networks

4 code implementations • ICLR 2020 • Anton Sinitsin, Vsevolod Plokhotnyuk, Dmitriy Pyrkin, Sergei Popov, Artem Babenko

We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.

Face Identification General Classification +4

Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

5 code implementations • ICLR 2020 • Sergei Popov, Stanislav Morozov, Artem Babenko

In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.

BIG-bench Machine Learning Representation Learning