IEICE TRANSACTIONS on Communications
Adversarial Reinforcement Learning-Based Coordinated Robust Spatial Reuse in Broadcast-Overlaid WLANs
Summary :
Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.2 pp.203-212
- Publication Date
- 2023/02/01
- Publicized
- 2022/08/02
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2022EBP3026
- Type of Manuscript
- PAPER
- Category
- Terrestrial Wireless Communication/Broadcasting Technologies
Authors
Yuto KIHIRA
https://orcid.org/0000-0003-3344-7370
Kyoto University
Yusuke KODA
https://orcid.org/0000-0003-4106-3983
University of Oulu
Koji YAMAMOTO
https://orcid.org/0000-0003-1026-319X
Kyoto University
Takayuki NISHIO
Kyoto University,Tokyo Institute of Technology
Keyword
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Yuto KIHIRA, Yusuke KODA, Koji YAMAMOTO, Takayuki NISHIO, "Adversarial Reinforcement Learning-Based Coordinated Robust Spatial Reuse in Broadcast-Overlaid WLANs" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 2, pp. 203-212, February 2023, doi: 10.1587/transcom.2022EBP3026.
Abstract: Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2022EBP3026/_p
Copy
@ARTICLE{e106-b_2_203,
author={Yuto KIHIRA, Yusuke KODA, Koji YAMAMOTO, Takayuki NISHIO, },
journal={IEICE TRANSACTIONS on Communications},
title={Adversarial Reinforcement Learning-Based Coordinated Robust Spatial Reuse in Broadcast-Overlaid WLANs},
year={2023},
volume={E106-B},
number={2},
pages={203-212},
abstract={Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.},
keywords={},
doi={10.1587/transcom.2022EBP3026},
ISSN={1745-1345},
month={February},}
Copy
TY - JOUR
TI - Adversarial Reinforcement Learning-Based Coordinated Robust Spatial Reuse in Broadcast-Overlaid WLANs
T2 - IEICE TRANSACTIONS on Communications
SP - 203
EP - 212
AU - Yuto KIHIRA
AU - Yusuke KODA
AU - Koji YAMAMOTO
AU - Takayuki NISHIO
PY - 2023
DO - 10.1587/transcom.2022EBP3026
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2023
AB - Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.
ER -
English
