University of Oulu

L. Miuccio, S. Riolo, S. Samarakoon, D. Panno and M. Bennis, "Learning Generalized Wireless MAC Communication Protocols via Abstraction," GLOBECOM 2022 - 2022 IEEE Global Communications Conference, Rio de Janeiro, Brazil, 2022, pp. 2322-2327, doi: 10.1109/GLOBECOM48099.2022.10000805

Learning generalized wireless MAC communication protocols via abstraction

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Author: Miuccio, Luciano1; Riolo, Salvatore1; Samarakoon, Sumudu2;
Organizations: 1Department of Electrical, Electronics and Computer Engineering, University of Catania, Italy
2Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.8 MB)
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Language: English
Published: IEEE, 2022
Publish Date: 2023-02-10


To tackle the heterogeneous requirements of beyond 5G (B5G) and future 6G wireless networks, conventional medium access control (MAC) procedures need to evolve to enable base stations (BSs) and user equipments (UEs) to automatically learn innovative MAC protocols catering to extremely diverse services. This topic has received significant attention, and several reinforcement learning (RL) algorithms, in which BSs and UEs are cast as agents, are available with the aim of learning a communication policy based on agents’ local observations. However, current approaches are typically overfitted to the environment they are trained in, and lack robustness against unseen conditions, failing to generalize in different environments. To overcome this problem, in this work, instead of learning a policy in the high dimensional and redundant observation space, we leverage the concept of observation abstraction (OA) rooted in extracting useful information from the environment. This in turn allows learning communication protocols that are more robust and with much better generalization capabilities than current baselines. To learn the abstracted information from observations, we propose an architecture based on autoencoder (AE) and imbue it into a multi-agent proximal policy optimization (MAPPO) framework. Simulation results corroborate the effectiveness of leveraging abstraction when learning protocols by generalizing across environments, in terms of number of UEs, number of data packets to transmit, and channel conditions.

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ISBN: 978-1-6654-3540-6
ISBN Print: 978-1-6654-3541-3
Pages: 2322 - 2327
DOI: 10.1109/globecom48099.2022.10000805
Host publication: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
Conference: IEEE Global Communications Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been partially funded by UNICT under project Pia.ce.ri. 2020/2022 - Linea 2D, by the MUR under Project PON R&I 2014-2020 Azioni IV.4 “Dottorati e contratti di ricerca su tematiche dell’innovazione”, by CHIST-ERA CONNECT project, by Horizon EU-CENTRIC project, and by FutureWei gift funding.
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