Deep reinforcement learning for delay-sensitive LTE downlink scheduling
Document Type
Conference Proceeding
Publication Date
8-1-2020
Abstract
We consider an LTE downlink scheduling system where a base station allocates resource blocks (RBs) to users running delay-sensitive applications. We aim to find a scheduling policy that minimizes the queuing delay experienced by the users. We formulate this problem as a Markov Decision Process (MDP) that integrates the channel quality indicator (CQI) of each user in each RB, and queue status of each user. To solve this complex problem involving high dimensional state and action spaces, we propose a Deep Reinforcement Learning based scheduling framework that utilizes the Deep Deterministic Policy Gradient (DDPG) algorithm to minimize the queuing delay experienced by the users. Our extensive experiments demonstrate that our approach outperforms state-of-the-art benchmarks in terms of average throughput, queuing delay, and fairness, achieving up to 55% lower queuing delay than the best benchmark.
Identifier
85094110161 (Scopus)
ISBN
[9781728144900]
Publication Title
IEEE International Symposium on Personal Indoor and Mobile Radio Communications PIMRC
External Full Text Location
https://doi.org/10.1109/PIMRC48278.2020.9217110
Volume
2020-August
Grant
1711335
Fund Ref
National Science Foundation
Recommended Citation
Sharma, Nikhilesh; Zhang, Sen; Somayajula Venkata, Someshwar Rao; Malandra, Filippo; Mastronarde, Nicholas; and Chakareski, Jacob, "Deep reinforcement learning for delay-sensitive LTE downlink scheduling" (2020). Faculty Publications. 5117.
https://digitalcommons.njit.edu/fac_pubs/5117
