Link-level throughput maximization using deep reinforcement learning

Jamshidiha, Saeed; Pourahmadi, Vahid; Mohammadi, Abbas; Bennis, Mehdi (2020-06-05)
 
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https://doi.org/10.1109/LNET.2020.3000334

Jamshidiha, Saeed
Pourahmadi, Vahid
Mohammadi, Abbas
Bennis, Mehdi
Institute of Electrical and Electronics Engineers
05.06.2020

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

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doi:https://doi.org/10.1109/LNET.2020.3000334
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https://urn.fi/URN:NBN:fi-fe202101181977
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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.

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