Search Results for author: Mahdi Boloursaz Mashhadi

Found 10 papers, 3 papers with code

DRF Codes: Deep SNR-Robust Feedback Codes

no code implementations22 Dec 2021 Mahdi Boloursaz Mashhadi, Deniz Gunduz, Alberto Perotti, Branislav Popovic

We present a new deep-neural-network (DNN) based error correction code for fading channels with output feedback, called deep SNR-robust feedback (DRF) code.

Deep Extended Feedback Codes

no code implementations4 May 2021 Anahid Robert Safavi, Alberto G. Perotti, Branislav M. Popovic, Mahdi Boloursaz Mashhadi, Deniz Gunduz

A new deep-neural-network (DNN) based error correction encoder architecture for channels with feedback, called Deep Extended Feedback (DEF), is presented in this paper.

LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training

1 code implementation29 Apr 2021 Matteo Zecchin, Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Deniz Gunduz, Marios Kountouris, David Gesbert

Efficient millimeter wave (mmWave) beam selection in vehicle-to-infrastructure (V2I) communication is a crucial yet challenging task due to the narrow mmWave beamwidth and high user mobility.

Knowledge Distillation

Federated mmWave Beam Selection Utilizing LIDAR Data

3 code implementations4 Feb 2021 Mahdi Boloursaz Mashhadi, Mikolaj Jankowski, Tze-Yang Tung, Szymon Kobus, Deniz Gunduz

Efficient link configuration in millimeter wave (mmWave) communication systems is a crucial yet challenging task due to the overhead imposed by beam selection.

Pruning the Pilots: Deep Learning-Based Pilot Design and Channel Estimation for MIMO-OFDM Systems

no code implementations21 Jun 2020 Mahdi Boloursaz Mashhadi, Deniz Gunduz

Our pruning-based pilot reduction technique reduces the overhead by allocating pilots across subcarriers non-uniformly and exploiting the inter-frequency and inter-antenna correlations in the channel matrix efficiently through convolutional layers and attention module.

Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback

no code implementations7 Mar 2020 Mahdi Boloursaz Mashhadi, Qianqian Yang, Deniz Gunduz

We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner.

Quantization

CNN-based Analog CSI Feedback in FDD MIMO-OFDM Systems

no code implementations23 Oct 2019 Mahdi Boloursaz Mashhadi, Qianqian Yang, Deniz Gunduz

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to better utilize the available spatial diversity and multiplexing gains.

Quantization

Deep Convolutional Compression for Massive MIMO CSI Feedback

no code implementations2 Jul 2019 Qianqian Yang, Mahdi Boloursaz Mashhadi, Deniz Gündüz

In comparison with previous works, the main contributions of DeepCMC are two-fold: i) DeepCMC is fully convolutional, and it can be used in a wide range of scenarios with various numbers of sub-channels and transmit antennas; ii) DeepCMC includes quantization and entropy coding blocks and minimizes a cost function that accounts for both the rate of compression and the reconstruction quality of the channel matrix at the BS.

Quantization

Cannot find the paper you are looking for? You can Submit a new open access paper.