Bandit-based relay selection in cooperative networks over unknown stationary channels

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Standard

Bandit-based relay selection in cooperative networks over unknown stationary channels. / Nomikos, Nikolaos; Talebi, Sadegh; Wichman, Risto; Charalambous, Themistoklis.

Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE, 2020. 9231604.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Harvard

Nomikos, N, Talebi, S, Wichman, R & Charalambous, T 2020, Bandit-based relay selection in cooperative networks over unknown stationary channels. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231604, IEEE, 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Virtual, Espoo, Finland, 21/09/2020. https://doi.org/10.1109/MLSP49062.2020.9231604

APA

Nomikos, N., Talebi, S., Wichman, R., & Charalambous, T. (2020). Bandit-based relay selection in cooperative networks over unknown stationary channels. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020 [9231604] IEEE. https://doi.org/10.1109/MLSP49062.2020.9231604

Vancouver

Nomikos N, Talebi S, Wichman R, Charalambous T. Bandit-based relay selection in cooperative networks over unknown stationary channels. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE. 2020. 9231604 https://doi.org/10.1109/MLSP49062.2020.9231604

Author

Nomikos, Nikolaos ; Talebi, Sadegh ; Wichman, Risto ; Charalambous, Themistoklis. / Bandit-based relay selection in cooperative networks over unknown stationary channels. Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE, 2020.

Bibtex

@inproceedings{f4340cf7405044a28e8480812816739a,
title = "Bandit-based relay selection in cooperative networks over unknown stationary channels",
abstract = "In recent years, wireless node density has increased rapidly, as more base stations, users, and machines coexist. Exploiting this node density, cooperative relaying has been deployed to improve connectivity throughout the network. Such a configuration, however, often demands relay scheduling, which comes with increased channel estimation and signaling overheads. To reduce these overheads, in this paper, we propose low-complexity relay scheduling mechanisms with the aid of a multi-armed bandit (MAB) framework. More specifically, this MAB framework is used for relay scheduling, based only on observing the acknowledgements/negative-acknow-ledgements (ACK/NACK) of packet transmissions. Hence, a bandit-based opportunistic relay selection (BB - ORS) mechanism is developed, recovering eventually the performance of classical opportunistic relay selection (0RS) when channel state information (CSI) is available without requiring any CSI. In addition, a distributed implementation of BB - ORS is presented, herein called d - BB - ORS, where distributed timers are used at the relays for relay selection, thus reducing the signaling overhead significantly. BB - ORS is compared to optimal scheduling with full CSI and the negligible performance gap is compensated by the low-complexity low-overhead implementation, while it surpasses the performance of ORS with outdated CSI. ",
keywords = "Machine learning, Multi-armed bandits, Relay selection, Upper confidence bound policies",
author = "Nikolaos Nomikos and Sadegh Talebi and Risto Wichman and Themistoklis Charalambous",
year = "2020",
doi = "10.1109/MLSP49062.2020.9231604",
language = "English",
booktitle = "Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020",
publisher = "IEEE",
note = "30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 ; Conference date: 21-09-2020 Through 24-09-2020",

}

RIS

TY - GEN

T1 - Bandit-based relay selection in cooperative networks over unknown stationary channels

AU - Nomikos, Nikolaos

AU - Talebi, Sadegh

AU - Wichman, Risto

AU - Charalambous, Themistoklis

PY - 2020

Y1 - 2020

N2 - In recent years, wireless node density has increased rapidly, as more base stations, users, and machines coexist. Exploiting this node density, cooperative relaying has been deployed to improve connectivity throughout the network. Such a configuration, however, often demands relay scheduling, which comes with increased channel estimation and signaling overheads. To reduce these overheads, in this paper, we propose low-complexity relay scheduling mechanisms with the aid of a multi-armed bandit (MAB) framework. More specifically, this MAB framework is used for relay scheduling, based only on observing the acknowledgements/negative-acknow-ledgements (ACK/NACK) of packet transmissions. Hence, a bandit-based opportunistic relay selection (BB - ORS) mechanism is developed, recovering eventually the performance of classical opportunistic relay selection (0RS) when channel state information (CSI) is available without requiring any CSI. In addition, a distributed implementation of BB - ORS is presented, herein called d - BB - ORS, where distributed timers are used at the relays for relay selection, thus reducing the signaling overhead significantly. BB - ORS is compared to optimal scheduling with full CSI and the negligible performance gap is compensated by the low-complexity low-overhead implementation, while it surpasses the performance of ORS with outdated CSI.

AB - In recent years, wireless node density has increased rapidly, as more base stations, users, and machines coexist. Exploiting this node density, cooperative relaying has been deployed to improve connectivity throughout the network. Such a configuration, however, often demands relay scheduling, which comes with increased channel estimation and signaling overheads. To reduce these overheads, in this paper, we propose low-complexity relay scheduling mechanisms with the aid of a multi-armed bandit (MAB) framework. More specifically, this MAB framework is used for relay scheduling, based only on observing the acknowledgements/negative-acknow-ledgements (ACK/NACK) of packet transmissions. Hence, a bandit-based opportunistic relay selection (BB - ORS) mechanism is developed, recovering eventually the performance of classical opportunistic relay selection (0RS) when channel state information (CSI) is available without requiring any CSI. In addition, a distributed implementation of BB - ORS is presented, herein called d - BB - ORS, where distributed timers are used at the relays for relay selection, thus reducing the signaling overhead significantly. BB - ORS is compared to optimal scheduling with full CSI and the negligible performance gap is compensated by the low-complexity low-overhead implementation, while it surpasses the performance of ORS with outdated CSI.

KW - Machine learning

KW - Multi-armed bandits

KW - Relay selection

KW - Upper confidence bound policies

U2 - 10.1109/MLSP49062.2020.9231604

DO - 10.1109/MLSP49062.2020.9231604

M3 - Article in proceedings

AN - SCOPUS:85096503271

BT - Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020

PB - IEEE

T2 - 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020

Y2 - 21 September 2020 through 24 September 2020

ER -

ID: 255502821