A Reinforcement Learning Approach for the Multichannel Rendezvous Problem

no code implementations • 2 Jul 2019 • Jen-Hung Wang, Ping-En Lu, Cheng-Shang Chang, Duan-Shin Lee

For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of {\em dynamic blind rendezvous policies}, i. e., at the $t^{th}$ time slot each user selects channel $i$ independently with probability $p_i(t)$, $i=1, 2, \ldots, N$.

reinforcement-learning Reinforcement Learning (RL)

ETTR Bounds and Approximation Solutions of Blind Rendezvous Policies in Cognitive Radio Networks with Random Channel States

no code implementations • 25 Jun 2019 • Cheng-Shang Chang, Duan-Shin Lee, Yu-Lun Lin, Jen-Hung Wang

We first consider two channel models: (i) the fast time-varying channel model (where the channel states are assumed to be independent and identically distributed in each time slot), and (ii) the slow time-varying channel model (where the channel states remain unchanged over time).

Information Theory Information Theory