Graduate School and Research Center in Digital Sciences

Team deep neural networks for interference channels

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

ML4COM 2018, Workshop on Promises and Challenges of Machine Learning in Communication Networks, in IEEE International Conference on Communications (ICC 2018), 20-24 May 2018, Kansas City, MO, USA

In this paper1, we propose to use Deep Neural Networks (DNNs) to solve so-called Team Decision (TD) problems, in which decentralized Decision Makers (DMs) aim at maximizing a common utility on the basis of locally available Channel State Information (CSI) without any additional communication or iteration. In the proposed configuration -coined Team DNNs (T-DNNs)-, the decision at each DM is approximated using a DNN and the weights of all DNNs are jointly trained, even though the implementation remains fundamentally decentralized. Turning to a practical application, the problem of decentralized link scheduling in Interference Channels (IC) is reformulated as a TD problem so that the T-DNNs approach can be applied. After adequate training, the scheduling obtained using the TDNNs flexibly adapts to the decentralized CSI configuration to outperform other scheduling algorithms, thus proposing a novel efficient solution to a problem that has remained elusive for years.

Document Doi Bibtex

Title:Team deep neural networks for interference channels
Department:Communication systems
Eurecom ref:5601
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Bibtex: @inproceedings{EURECOM+5601, doi = {}, year = {2018}, title = {{T}eam deep neural networks for interference channels}, author = {de {K}erret, {P}aul and {G}esbert,{D}avid and {F}ilippone, {M}aurizio}, booktitle = {{ML}4{COM} 2018, {W}orkshop on {P}romises and {C}hallenges of {M}achine {L}earning in {C}ommunication {N}etworks, in {IEEE} {I}nternational {C}onference on {C}ommunications ({ICC} 2018), 20-24 {M}ay 2018, {K}ansas {C}ity, {MO}, {USA}}, address = {{K}ansas, {UNITED} {STATES}}, month = {05}, url = {} }
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