TY - GEN
T1 - Q-Learning Based Predictive Relay Selection for Optimal Relay Beamforming
AU - Dimas, Anastasios
AU - Diamantaras, Konstantinos
AU - Petropulu, Athina P.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - Wireless Autonomous Networks are expected to support communication between a source and a receiver, by constantly self-adapting to changes in their communication environment. This paper considers a scenario of relay beamforming, in which relays collaboratively retransmit the source signal so that they maximize the average signal-to-interference+noise ratio (SINR) at the destination. The relays are grouped into clusters, with each cluster having a single active relay at a time. The system evolves in time slots; in each time slot the clusters beamform to the destination, and at the same time, each cluster selects the relay to be active in the subsequent time slot. Relay selection is performed locally within each cluster, using a reinforcement learning approach, namely Q-learning. Compared to prior methods, the proposed scheme does not require any statistical knowledge on the channels, and achieves similar average SINR performance while involving lower complexity.
AB - Wireless Autonomous Networks are expected to support communication between a source and a receiver, by constantly self-adapting to changes in their communication environment. This paper considers a scenario of relay beamforming, in which relays collaboratively retransmit the source signal so that they maximize the average signal-to-interference+noise ratio (SINR) at the destination. The relays are grouped into clusters, with each cluster having a single active relay at a time. The system evolves in time slots; in each time slot the clusters beamform to the destination, and at the same time, each cluster selects the relay to be active in the subsequent time slot. Relay selection is performed locally within each cluster, using a reinforcement learning approach, namely Q-learning. Compared to prior methods, the proposed scheme does not require any statistical knowledge on the channels, and achieves similar average SINR performance while involving lower complexity.
KW - Q-learning
KW - Reinforcement Learning
KW - Spatially Controlled Relay Beamforming
KW - WANs
UR - https://www.scopus.com/pages/publications/85089238966
UR - https://www.scopus.com/pages/publications/85089238966#tab=citedBy
U2 - 10.1109/ICASSP40776.2020.9054173
DO - 10.1109/ICASSP40776.2020.9054173
M3 - Conference contribution
AN - SCOPUS:85089238966
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5030
EP - 5034
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -