3 citations · 3 across the 5 of their papers we have counts for
5 papers
GMM-based Codebook Construction and Feedback Encoding in FDD Systems
Nurettin Turan, Michael Koller, Benedikt Fesl +3
We propose a precoder codebook construction and feedback encoding scheme which is based on Gaussian mixture models (GMMs). In an offline phase, the base station (BS) first fits a G…
Learning a Compressive Sensing Matrix with Structural Constraints via Maximum Mean Discrepancy Optimization
Michael Koller, Wolfgang Utschick
We introduce a learning-based algorithm to obtain a measurement matrix for compressive sensing related recovery problems. The focus lies on matrices with a constant modulus constra…
Centralized Learning of the Distributed Downlink Channel Estimators in FDD Systems using Uplink Data
B. Fesl, N. Turan, M. Koller +2
In this work, we propose a convolutional neural network (CNN) based low-complexity approach for downlink (DL) channel estimation (CE) in frequency division duplex (FDD) systems. In…
Unsupervised Learning of Adaptive Codebooks for Deep Feedback Encoding in FDD Systems
Nurettin Turan, Michael Koller, Samer Bazzi +2
In this work, we propose a joint adaptive codebook construction and feedback generation scheme in frequency division duplex (FDD) systems. Both unsupervised and supervised deep lea…
A Low-Complexity MIMO Channel Estimator with Implicit Structure of a Convolutional Neural Network
B. Fesl, N. Turan, M. Koller +1
A low-complexity convolutional neural network estimator which learns the minimum mean squared error channel estimator for single-antenna users was recently proposed. We generalize…