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**Harvard**

Riegler, E., Koliander, G., Yang, W. och Durisi, G. (2013) *How costly is it to learn fading channels?*.

** BibTeX **

@conference{

Riegler2013,

author={Riegler, Erwin and Koliander, Günther and Yang, Wei and Durisi, Giuseppe},

title={How costly is it to learn fading channels?},

booktitle={2013 1st International Black Sea Conference on Communications and Networking, BlackSeaCom 2013; Batumi; Georgia; 3 July 2013 through 5 July 2013},

isbn={978-147990857-8},

pages={18-22},

abstract={Recent results in communication theory suggest that substantial throughput gains in wireless fading networks can be achieved by exploiting network coordination (e.g., CoMP, network MIMO, interference alignment). However, these results are often based on the simplifying assumption that each node in the network has perfect channel knowledge and ignore the channel-estimation overhead.
In this tutorial paper, we take a fresh look at the problem of learning fading channels. By focusing on simple channel models, we will illustrate how to quantify rigorously the throughput loss due to channel-estimation overhead. Specifically, by exploiting that in the absence of a priori channel knowledge at the receiver, the noiseless received signal is a nonlinear function of the transmitted signals and the propagation channel, we will show how to unveil the geometric structure underlying the channel input output relation, and how to use this geometry to characterize capacity at high SNR. We will also demonstrate that this approach is useful to determine the largest rate achievable at finite SNR and finite blocklength.},

year={2013},

}

** RefWorks **

RT Conference Proceedings

SR Electronic

ID 177868

A1 Riegler, Erwin

A1 Koliander, Günther

A1 Yang, Wei

A1 Durisi, Giuseppe

T1 How costly is it to learn fading channels?

YR 2013

T2 2013 1st International Black Sea Conference on Communications and Networking, BlackSeaCom 2013; Batumi; Georgia; 3 July 2013 through 5 July 2013

SN 978-147990857-8

SP 18

OP 22

AB Recent results in communication theory suggest that substantial throughput gains in wireless fading networks can be achieved by exploiting network coordination (e.g., CoMP, network MIMO, interference alignment). However, these results are often based on the simplifying assumption that each node in the network has perfect channel knowledge and ignore the channel-estimation overhead.
In this tutorial paper, we take a fresh look at the problem of learning fading channels. By focusing on simple channel models, we will illustrate how to quantify rigorously the throughput loss due to channel-estimation overhead. Specifically, by exploiting that in the absence of a priori channel knowledge at the receiver, the noiseless received signal is a nonlinear function of the transmitted signals and the propagation channel, we will show how to unveil the geometric structure underlying the channel input output relation, and how to use this geometry to characterize capacity at high SNR. We will also demonstrate that this approach is useful to determine the largest rate achievable at finite SNR and finite blocklength.

LA eng

DO 10.1109/BlackSeaCom.2013.6623374

LK http://dx.doi.org/10.1109/BlackSeaCom.2013.6623374

LK http://publications.lib.chalmers.se/records/fulltext/177868/local_177868.pdf

OL 30