Graduate School and Research Center in Digital Sciences

Decentralized deep scheduling for interference channels

de Kerret, Paul; Gesbert, David; Filippone, Maurizio

Submitted on ArXiV on November 2nd, 2017

In this paper, we study the problem of decentralized scheduling in Interference Channels (IC). In this setting, each Transmitter (TX) receives an arbitrary amount of feedback regarding the global multi-user channel state based on which it decides whether to transmit or to stay silent without any form of communication with the other TXs. While many methods have been proposed to tackle the problem of link scheduling in the presence of reliable Channel State Information (CSI), finding the optimally robust transmission strategy in the presence of arbitrary channel uncertainties at each TX has remained elusive for the past years. In this work, we recast the link scheduling problem as a decentralized classification problem and we propose the use of Collaborative Deep Neural Networks (C-DNNs) to solve this problem. After adequate training, the scheduling obtained using the C-DNNs flexibly adapts to the decentralized CSI configuration to outperform other scheduling algorithms.

Arxiv Bibtex

Title:Decentralized deep scheduling for interference channels
Department:Communication systems
Eurecom ref:5379
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted on ArXiV on November 2nd, 2017 and is available at :
Bibtex: @inproceedings{EURECOM+5379, year = {2017}, title = {{D}ecentralized deep scheduling for interference channels}, author = {de {K}erret, {P}aul and {G}esbert, {D}avid and {F}ilippone, {M}aurizio}, booktitle = {{S}ubmitted on {A}r{X}i{V} on {N}ovember 2nd, 2017}, address = {}, month = {11}, url = {} }
See also: