1 research outputs found
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning
In this work, we develop practical user scheduling algorithms for downlink
bursty traffic with emphasis on user fairness. In contrast to the conventional
scheduling algorithms that either equally divides the transmission time slots
among users or maximizing some ratios without physcial meanings, we propose to
use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness.
Since it is difficult to directly optimize 5TUDR, we first cast the problem
into the stochastic game framework and subsequently propose a Multi-Agent
Reinforcement Learning (MARL)-based algorithm to perform distributed
optimization on the resource block group (RBG) allocation. Furthermore, each
MARL agent is designed to take information measured by network counters from
multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the
input states while the RBG allocation as action with a proposed reward function
designed to maximize 5TUDR. Extensive simulation is performed to show that the
proposed MARL-based scheduler can achieve fair scheduling while maintaining
good average network throughput as compared to conventional schedulers.Comment: 30 pages, 13 figure