Optimal Mobile Relay Beamforming Via Reinforcement Learning

Konstantinos Diamantaras; Athina Petropulu

doi:10.1109/MLSP.2019.8918745

Optimal Mobile Relay Beamforming Via Reinforcement Learning

Konstantinos Diamantaras
, Athina Petropulu

School of Engineering, Electrical & Computer Engineering

Rutgers, The State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

We consider the communication of a source-destination pair in a dynamic flat fading channel environment. The communication is aided by mobile relays, which beamform the source signal to the destination. The beamforming weights and the relay positions are optimally selected so that the average Signal-to-Interference and Noise ratio (SINR) at the destination is maximized subject to total relay power constraints. We assume a time slotted system, and model the channels as random fields with a deterministic path loss component with unknown exponent, and random log-normal shadowing and multipath fading components with unknown model parameters. In the beginning of each slot, the relays optimally beamform based on source and destination channel estimates obtained up to their current positions and current time. Then, for the remainder of the slot, the relays optimally select their positions for the next slot beamforming. The relay positions are computed using a reinforcement learning approach, which recursively estimates the channel map in time and space.

Original language	American English
Title of host publication	2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728108247
DOIs	https://doi.org/10.1109/MLSP.2019.8918745
State	Published - Oct 2019
Event	29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 - Pittsburgh, United States Duration: Oct 13 2019 → Oct 16 2019

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2019-October

Conference

Conference	29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019
Country/Territory	United States
City	Pittsburgh
Period	10/13/19 → 10/16/19

ASJC Scopus subject areas

Signal Processing
Human-Computer Interaction

Keywords

Beamforming
Mobile relay network
Multi-armed bandits
Reinforcement Learning

Access to Document

10.1109/MLSP.2019.8918745

Cite this

Diamantaras, K., & Petropulu, A. (2019). Optimal Mobile Relay Beamforming Via Reinforcement Learning. In 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019 Article 8918745 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2019-October). IEEE Computer Society. https://doi.org/10.1109/MLSP.2019.8918745

@inproceedings{0803df7e4f644fb69d86d3c418dcdb7a,

title = "Optimal Mobile Relay Beamforming Via Reinforcement Learning",

abstract = "We consider the communication of a source-destination pair in a dynamic flat fading channel environment. The communication is aided by mobile relays, which beamform the source signal to the destination. The beamforming weights and the relay positions are optimally selected so that the average Signal-to-Interference and Noise ratio (SINR) at the destination is maximized subject to total relay power constraints. We assume a time slotted system, and model the channels as random fields with a deterministic path loss component with unknown exponent, and random log-normal shadowing and multipath fading components with unknown model parameters. In the beginning of each slot, the relays optimally beamform based on source and destination channel estimates obtained up to their current positions and current time. Then, for the remainder of the slot, the relays optimally select their positions for the next slot beamforming. The relay positions are computed using a reinforcement learning approach, which recursively estimates the channel map in time and space.",

keywords = "Beamforming, Mobile relay network, Multi-armed bandits, Reinforcement Learning",

author = "Konstantinos Diamantaras and Athina Petropulu",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019 ; Conference date: 13-10-2019 Through 16-10-2019",

year = "2019",

month = oct,

doi = "10.1109/MLSP.2019.8918745",

language = "American English",

series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",

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booktitle = "2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019",

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Diamantaras, K & Petropulu, A 2019, Optimal Mobile Relay Beamforming Via Reinforcement Learning. in 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019., 8918745, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2019-October, IEEE Computer Society, 29th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2019, Pittsburgh, United States, 10/13/19. https://doi.org/10.1109/MLSP.2019.8918745

Optimal Mobile Relay Beamforming Via Reinforcement Learning. / Diamantaras, Konstantinos; Petropulu, Athina.
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019. IEEE Computer Society, 2019. 8918745 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2019-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Optimal Mobile Relay Beamforming Via Reinforcement Learning

AU - Diamantaras, Konstantinos

AU - Petropulu, Athina

PY - 2019/10

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N2 - We consider the communication of a source-destination pair in a dynamic flat fading channel environment. The communication is aided by mobile relays, which beamform the source signal to the destination. The beamforming weights and the relay positions are optimally selected so that the average Signal-to-Interference and Noise ratio (SINR) at the destination is maximized subject to total relay power constraints. We assume a time slotted system, and model the channels as random fields with a deterministic path loss component with unknown exponent, and random log-normal shadowing and multipath fading components with unknown model parameters. In the beginning of each slot, the relays optimally beamform based on source and destination channel estimates obtained up to their current positions and current time. Then, for the remainder of the slot, the relays optimally select their positions for the next slot beamforming. The relay positions are computed using a reinforcement learning approach, which recursively estimates the channel map in time and space.

AB - We consider the communication of a source-destination pair in a dynamic flat fading channel environment. The communication is aided by mobile relays, which beamform the source signal to the destination. The beamforming weights and the relay positions are optimally selected so that the average Signal-to-Interference and Noise ratio (SINR) at the destination is maximized subject to total relay power constraints. We assume a time slotted system, and model the channels as random fields with a deterministic path loss component with unknown exponent, and random log-normal shadowing and multipath fading components with unknown model parameters. In the beginning of each slot, the relays optimally beamform based on source and destination channel estimates obtained up to their current positions and current time. Then, for the remainder of the slot, the relays optimally select their positions for the next slot beamforming. The relay positions are computed using a reinforcement learning approach, which recursively estimates the channel map in time and space.

KW - Beamforming

KW - Mobile relay network

KW - Multi-armed bandits

KW - Reinforcement Learning

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M3 - Conference contribution

T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing, MLSP 2019

PB - IEEE Computer Society

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Y2 - 13 October 2019 through 16 October 2019

ER -

Optimal Mobile Relay Beamforming Via Reinforcement Learning

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ASJC Scopus subject areas

Keywords

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