Predicting Device-to-Device Channels From Cellular Channel Measurements: A Learning Approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00344305" target="_blank" >RIV/68407700:21230/20:00344305 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TWC.2020.3008303" target="_blank" >https://doi.org/10.1109/TWC.2020.3008303</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TWC.2020.3008303" target="_blank" >10.1109/TWC.2020.3008303</a>

Result language

angličtina

Original language name

Predicting Device-to-Device Channels From Cellular Channel Measurements: A Learning Approach

Original language description

Device-to-device (D2D) communication, which ena- bles a direct connection between users while bypassing the cellular channels to base stations (BSs), is a promising way to offload the traffic from conventional cellular networks. In D2D communication, optimizing the resource allocation requires the knowledge of D2D channel gains. However, such knowledge is hard to obtain at reasonable signaling costs. In this paper, we show this problem can be circumvented by tapping into the information provided by the estimated cellular channels between the users and surrounding BSs as these channels are estimated anyway for a normal operation of the network. While the cellular and D2D channel gains exhibit independent fast fading behavior, we show that average gains of the cellular and D2D channels share a non-explicit relation, which is rooted into the network topology, terrain, and buildings setup. We propose a deep learning approach to predict the D2D channel gains from seemingly independent cellular channels. Our results show a high degree of convergence between the true and predicted D2D channel gains. Moreover, we demonstrate the robustness of the proposed scheme against environment changes and inaccuracies during the offline training. The predicted gains allow to reach a near-optimal capacity in many radio resource management algorithms.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20203 - Telecommunications

Project

<a href="/en/project/LTT18007" target="_blank" >LTT18007: Cooperation with the International Research Centre in Area of Communication Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

IEEE Transactions on Wireless Communications

ISSN

1536-1276

e-ISSN

1558-2248

Volume of the periodical

19

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

7124-7138

UT code for WoS article

000589218700009

EID of the result in the Scopus database

2-s2.0-85096243504