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.
Type:
Conférence
City:
Kansas
Date:
2018-05-20
Department:
Systèmes de Communication
Eurecom Ref:
5601
Copyright:
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