Multi-objective reinforcement learning-based deep neural networks for cognitive space communications

Paulo Victor R. Ferreira, Randy Paffenroth, Alexander M. Wyglinski, Timothy M. Hackett, Sven G. Bilén, Richard C. Reinhart, Dale J. Mortensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through 'virtual environment exploration'. Improvements in the multi-objective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located on-board the International Space Station.

Original languageEnglish (US)
Title of host publication2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538639887
DOIs
StatePublished - Aug 3 2017
Event2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017 - Cleveland, United States
Duration: Jun 27 2017Jun 28 2017

Publication series

Name2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017

Other

Other2017 Cognitive Communications for Aerospace Applications Workshop, CCAA 2017
CountryUnited States
CityCleveland
Period6/27/176/28/17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Aerospace Engineering

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