@inproceedings{0afb6af052e84d2aa99593f3032ed3b4,
title = "MmWave Channel Estimation via Atomic Norm Minimization for Multi-User Hybrid Precoding",
abstract = "To perform multi-user multiple-input and multiple-output transmission in millimeter-wave (mmWave) cellular systems, the high-dimensional channels need to be estimated for designing the multi-user precoder. Conventional grid-based Compressed Sensing (CS) methods for mmWave channel estimation suffer from the basis mismatch problem, which prevents accurate channel reconstruction and degrades the precoding performance. This paper formulates mmWave channel estimation as an Atomic Norm Minimization (ANM) problem. In contrast to grid-based CS methods which use discrete dictionaries, ANM uses a continuous dictionary for representing the mmWave channel. We consider a continuous dictionary based on sub-sampling in the antenna domain via a small number of radio frequency chains. We show that mmWave channel estimation using ANM can be formulated as a Semi-detinite Programming (SDP) problem, and the channel can be accurately estimated via off-the-shelf SDP solvers in polynomial time. Simulation results indicate that ANM can achieve much better estimation accuracy compared to grid-based CS, and significantly improves the spectral efficiency provided by multi-user precoding.",
keywords = "Compressed Sensing, channel estimation, mm-wave, Massive MIMO",
author = "Junquan Deng and Olav Tirkkonen and Christoph Studer",
year = "2018",
month = apr,
day = "15",
doi = "10.1109/WCNC.2018.8377093",
language = "English",
series = "IEEE Wireless Communications and Networking Conference",
publisher = "IEEE",
pages = "1--6",
booktitle = "2018 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)",
address = "United States",
note = "IEEE Wireless Communications and Networking Conference, WCNC ; Conference date: 15-04-2018 Through 18-04-2018",
}