activity
20142023
most citedEnergy Efficient Extreme MIMO: Design Goals and Directions

42 citations · 54 across the 8 of their papers we have counts for

collaborators

8 papers

eess.SP2023

Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss

Nuwanthika Rajapaksha, Jafar Mohammadi, Stefan Wesemann +2

Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizin…

eess.SP2023★ 42 cited

Energy Efficient Extreme MIMO: Design Goals and Directions

Stefan Wesemann, Jinfeng Du, Harish Viswanathan

Ever since the invention of Bell Laboratories Layer Space-Time (BLAST) in mid 1990s, the focus of MIMO research and development has been largely on pushing the limit of spectral ef…

cs.IT2022★ 3 cited

Design of an Efficient CSI Feedback Mechanism in Massive MIMO Systems: A Machine Learning Approach using Empirical Data

Muhammad Karam Shehzad, Luca Rose, Stefan Wesemann +2

Massive multiple-input multiple-output (mMIMO) regime reaps the benefits of spatial diversity and multiplexing gains, subject to precise channel state information (CSI) acquisition…

eess.SP2020

A Joint Wireless-Optical Front-haul Solution for Multi-user Massive MIMO 5G RAN

Son T. Le, Stefan Wesemann, Roman Dischler +1

We demonstrate a high capacity IF-over-fiber mobile fronthaul solution for multi-user massive MIMO 5G RAN. Using this scheme, a record aggregated radio bandwidth of 25.6 GHz was tr…

eess.SP2020★ 1 cited

Turbo-AI: Iterative Machine Learning Based Channel Estimation for 2D Massive Arrays

Yejian Chen, Jafar Mohammadi, Stefan Wesemann +1

Recently, Machine Learning (ML) is recognized as an effective tool for wireless communications and plays an evolutionary role to enhance Physical Layer (PHY) of 5th Generation (5G)…

cs.IT2017★ 4 cited

Quantized Precoding for Multi-Antenna Downlink Channels with MAGIQ

Andrei Nedelcu, Fabian Steiner, Markus Staudacher +5

A multi-antenna, greedy, iterative, and quantized (MAGIQ) precoding algorithm is proposed for downlink channels. MAGIQ allows a straightforward integration with orthogonal frequenc…

cs.IT2016★ 2 cited

Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér-Rao Bounds

Stefan Wesemann, Thomas L. Marzetta

For frequency division duplex channels, a simple pilot loop-back procedure has been proposed that allows the estimation of the UL & DL channels at an antenna array without relying…

cs.IT2014★ 2 cited

Distributed Asynchronous Optimization Framework for the MISO Interference Channel

Stefan Wesemann, Gerhard Fettweis

We study the distributed optimization of transmit strategies in a multiple-input, single-output (MISO) interference channel (IFC). Existing distributed algorithms rely on stricly s…