A Reinforcement Learning Approach to Age of Information in Multi-User Networks
| Autor: | Elif Tugce Ceran, Deniz Gunduz, András György |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
060102 archaeology business.industry Stochastic process Computer science Automatic repeat request Hybrid automatic repeat request 020206 networking & telecommunications Machine Learning (stat.ML) 06 humanities and the arts 02 engineering and technology Data_CODINGANDINFORMATIONTHEORY Multi-user Scheduling (computing) Machine Learning (cs.LG) Computer Science - Learning Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Reinforcement learning 0601 history and archaeology business Computer network Communication channel |
| Zdroj: | PIMRC |
| DOI: | 10.48550/arxiv.1806.00336 |
| Popis: | Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the longterm average age of information (AoI) at the users under a constraint on the average number of transmissions. The source can transmit only to a single user at each time slot, and after each transmission, it receives an instantaneous ACK/NACK feedback from the intended receiver, and decides on when and to which user to transmit the next update. The optimal scheduling policy is first studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Then a reinforcement learning (RL) approach is introduced, which does not assume any a priori information on the random processes governing the channel states. Different RL methods are applied and compared through numerical simulations. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|