Link-level throughput maximization using deep reinforcement learning
Jamshidiha, Saeed; Pourahmadi, Vahid; Mohammadi, Abbas; Bennis, Mehdi (2020-06-05)
S. Jamshidiha, V. Pourahmadi, A. Mohammadi and M. Bennis, "Link-Level Throughput Maximization Using Deep Reinforcement Learning," in IEEE Networking Letters, vol. 2, no. 3, pp. 101-105, Sept. 2020, doi: 10.1109/LNET.2020.3000334
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe202101181977
Tiivistelmä
Abstract
A multi-agent deep reinforcement learning framework is proposed to address link level throughput maximization by power allocation and modulation and coding scheme (MCS) selection. Given the complex problem space, reward shaping is utilized instead of classical training procedures. The time-frame utilities are decomposed into subframe rewards, and a stepwise training procedure is proposed, starting from a simplified power allocation setup without MCS selection, incorporating MCS selection gradually, as the agents learn optimal power allocation. The proposed method outperforms both weighted minimum mean squared error (WMMSE) and Fractional Programming (FP) with idealized MCS selections.
Kokoelmat
- Avoin saatavuus [31995]