Graduate School and Research Center in Digital Sciences

Learning to cooperate in decentralized wireless networks

Kim, Minhoe; de Kerret, Paul; Gesbert, David

ASILOMAR 2018, 52nd Asilomar Conference on Signals, Systems and Computers, 28-31 October 2018, Pacific Grove, USA

Several key wireless communication setups call for coordination capabilities between otherwise interfering transmitters. Coordination or cooperation can be achieved at the expense of channel state information exchange. When such information is noisy, the derivation of robust decision-making algorithms is unfortunately known to be very challenging via conventional optimization method. In this paper we introduce a learning-based framework which allows the agents, aka. the transmitters, to produce as-relevant-as-possible messages to each other on the basis of arbitrarily partial and noisy local channel state information. The messages are produced via distributed deep neural networks (DNNs) which are trained for a specific coordination purpose. The message-passing DNNs are completed with decision-making DNNs which are trained for a network metric maximization. Promising preliminary results are obtained in the context of sum-rate maximizing decentralized power control.

Document Doi Bibtex

Title:Learning to cooperate in decentralized wireless networks
Keywords:Deep learning, rate maximization, power control, decentralized wireless network, information sharing, coordination, cooperation
City:Pacific Grove
Department:Communication systems
Eurecom ref:5763
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Bibtex: @inproceedings{EURECOM+5763, doi = {}, year = {2018}, title = {{L}earning to cooperate in decentralized wireless networks}, author = {{K}im, {M}inhoe and de {K}erret, {P}aul and {G}esbert, {D}avid}, booktitle = {{ASILOMAR} 2018, 52nd {A}silomar {C}onference on {S}ignals, {S}ystems and {C}omputers, 28-31 {O}ctober 2018, {P}acific {G}rove, {USA}}, address = {{P}acific {G}rove, {UNITED} {STATES}}, month = {10}, url = {} }
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