A Reinforcement Learning Approach to Age of Information in Multi-User Networks With HARQ
| Autor: | András György, Elif Tugce Ceran, Deniz Gunduz |
|---|---|
| Přispěvatelé: | Commission of the European Communities, Engineering & Physical Science Research Council (EPSRC) |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Networks and Communications Computer science Computer Science - Information Theory Automatic repeat request Hybrid automatic repeat request 0805 Distributed Computing Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology Age of information Multi-user Constrained Markov decision process Machine Learning (cs.LG) Scheduling (computing) Reinforcement learning 1005 Communications Technologies 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering business.industry Information Theory (cs.IT) Node (networking) Hybrid automatic repeat request (HARQ) Whittle index 020206 networking & telecommunications 0906 Electrical and Electronic Engineering Transmission (telecommunications) Networking & Telecommunications business Computer network Communication channel |
| Zdroj: | IEEE Journal on Selected Areas in Communications. 39:1412-1426 |
| ISSN: | 1558-0008 0733-8716 |
| DOI: | 10.1109/jsac.2021.3065057 |
| Popis: | Scheduling the transmission of time-sensitive information from a source node to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users. A long-term average resource constraint is imposed on the source, which limits 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 when and to which user to transmit the next update. Assuming the channel statistics are known, the optimal scheduling policy is studied for both the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a reinforcement learning (RL) approach is introduced to find a near-optimal policy, which does not assume any a priori information on the random processes governing the channel states. Different RL methods including average-cost SARSA with linear function approximation (LFA), upper confidence reinforcement learning (UCRL2), and deep Q-network (DQN) are applied and compared through numerical simulations. |
| Databáze: | OpenAIRE |
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