A Reinforcement Learning Approach to Age of Information in Multi-User Networks with HARQ

TL;DR

This paper develops reinforcement learning-based scheduling policies to minimize the age of information in multi-user networks with error-prone channels, considering HARQ protocols and unknown channel dynamics.

Contribution

It introduces RL methods for AoI optimization in multi-user networks with HARQ, without requiring prior channel knowledge, and compares their performance.

Findings

RL methods outperform traditional policies in AoI reduction.

Deep Q-network achieves near-optimal performance in simulations.

The approach effectively handles unknown channel dynamics.

Abstract

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 SARSAwith linear function approximation (LFA), upper confidence reinforcement learning (UCRL2), and deep Q-network (DQN) are applied and compared through numerical simulations