@inproceedings{01168275092a4497874c6d859498e030,
title = "Age of information minimization for wireless ad hoc networks: A deep reinforcement learning approach",
abstract = "Age of information (AoI) has been recently considered as a performance metric to measure the freshness of information for time-critical wireless communications application. In this paper, we consider AoI minimization in a wireless ad hoc network, where nodes exchange status updates with one another over shared spectrum. The network needs to be formed in a dynamic fashion in the sense that each node either broadcasts or receives updates in a slot and attempts to keep the updates in both directions fresh. We aim to minimize the average AoI of each node by a joint broadcast scheduling and power control policy. Each node decides its transmitting/receiving mode and the transmission power based on its local observation of the system state. We formulate a Markov game and develop a multi-agent deep reinforcement learning algorithm based on deep recurrent Q-network. The simulation results show that the proposed approach outperforms the baselines significantly.",
author = "Shiyang Leng and Aylin Yener",
year = "2019",
month = dec,
doi = "10.1109/GLOBECOM38437.2019.9013454",
language = "English (US)",
series = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",
address = "United States",
note = "2019 IEEE Global Communications Conference, GLOBECOM 2019 ; Conference date: 09-12-2019 Through 13-12-2019",
}