A reinforcement learning optimization framework for cognitive interference networks

Marco Levorato, Sina Firouzabadi, Andrea Goldsmith

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

1 Scopus citations

Abstract

A Reinforcement Learning (RL) algorithm for the optimization of secondary user's transmission strategies in cognitive networks is presented. The secondary user minimizes a cost function while generating a bounded performance loss to the primary users' network. The state of the primary users' network, defined as a collection of variables describing features of the network (e.g., buffer state, ARQ state) evolves over time according to a homogeneous Markov process. The statistics of the Markov process is dependent on the strategy of the secondary user and, thus, the instantaneous idleness/transmission action of the secondary user has a long-term impact on the temporal evolution of the network. The proposed RL algorithm finds the optimal randomized past-independent policy from a sample-path of state-cost observations without any a priori knowledge of the statistics of the Markov process. The performance and structure of the policy resulting from the proposed RL algorithm is compared to those of the policy identified by the algorithm in [1].

Original languageEnglish (US)
Title of host publication2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
Pages1633-1640
Number of pages8
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011 - Monticello, IL, United States
Duration: Sep 28 2011Sep 30 2011

Publication series

Name2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011

Other

Other2011 49th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2011
CountryUnited States
CityMonticello, IL
Period9/28/119/30/11

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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