Massive MIMO power allocation in millimeter wave networks

Hang Liu, Chinh Tran, Jan Lasota, Son Dinh, Xianfu Chen, Feng Ouyang

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.

Original languageEnglish
Title of host publicationWireless Algorithms, Systems, and Applications, WASA 2018
PublisherSpringer
Pages296-307
Number of pages12
ISBN (Electronic)978-3-319-94268-1
ISBN (Print)978-3-319-94267-4
DOIs
Publication statusPublished - 1 Jan 2018
MoE publication typeA4 Article in a conference publication
Event13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018 - Tianjin, China
Duration: 20 Jun 201822 Jun 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10874
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018
CountryChina
CityTianjin
Period20/06/1822/06/18

Fingerprint

Millimeter Wave
Power Allocation
Beamforming
MIMO systems
Millimeter waves
Multiple-input multiple-output (MIMO)
Telecommunication traffic
Statistics
Optimal systems
Reinforcement learning
Traffic
Base stations
Data communication systems
Wireless networks
Scheduling
Throughput
Traffic Dynamics
Optimal System
Online Learning
Queueing

Keywords

  • Dynamic networks
  • Machine learning
  • Markov decision process
  • Massive MIMO
  • Millimeter wave communications
  • Power allocation

Cite this

Liu, H., Tran, C., Lasota, J., Dinh, S., Chen, X., & Ouyang, F. (2018). Massive MIMO power allocation in millimeter wave networks. In Wireless Algorithms, Systems, and Applications, WASA 2018 (pp. 296-307). Springer. Lecture Notes in Computer Science, Vol.. 10874 https://doi.org/10.1007/978-3-319-94268-1_25
Liu, Hang ; Tran, Chinh ; Lasota, Jan ; Dinh, Son ; Chen, Xianfu ; Ouyang, Feng. / Massive MIMO power allocation in millimeter wave networks. Wireless Algorithms, Systems, and Applications, WASA 2018. Springer, 2018. pp. 296-307 (Lecture Notes in Computer Science, Vol. 10874).
@inproceedings{86e0a4777d614af58a96599a21c867e9,
title = "Massive MIMO power allocation in millimeter wave networks",
abstract = "Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.",
keywords = "Dynamic networks, Machine learning, Markov decision process, Massive MIMO, Millimeter wave communications, Power allocation",
author = "Hang Liu and Chinh Tran and Jan Lasota and Son Dinh and Xianfu Chen and Feng Ouyang",
year = "2018",
month = "1",
day = "1",
doi = "10.1007/978-3-319-94268-1_25",
language = "English",
isbn = "978-3-319-94267-4",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "296--307",
booktitle = "Wireless Algorithms, Systems, and Applications, WASA 2018",
address = "Germany",

}

Liu, H, Tran, C, Lasota, J, Dinh, S, Chen, X & Ouyang, F 2018, Massive MIMO power allocation in millimeter wave networks. in Wireless Algorithms, Systems, and Applications, WASA 2018. Springer, Lecture Notes in Computer Science, vol. 10874, pp. 296-307, 13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018, Tianjin, China, 20/06/18. https://doi.org/10.1007/978-3-319-94268-1_25

Massive MIMO power allocation in millimeter wave networks. / Liu, Hang; Tran, Chinh; Lasota, Jan; Dinh, Son; Chen, Xianfu; Ouyang, Feng.

Wireless Algorithms, Systems, and Applications, WASA 2018. Springer, 2018. p. 296-307 (Lecture Notes in Computer Science, Vol. 10874).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Massive MIMO power allocation in millimeter wave networks

AU - Liu, Hang

AU - Tran, Chinh

AU - Lasota, Jan

AU - Dinh, Son

AU - Chen, Xianfu

AU - Ouyang, Feng

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.

AB - Massive multiple-input multiple-output (MMIMO) is a key technology for 5G mobile communication systems, which enables to simultaneously form and transmit multiple directional signal beams to multiple mobile terminals (MTs) on the same frequency channel with high array beamforming gains and throughput. One of the challenges in MMMIO beamforming is how to allocate the transmit power to multiple beams sent from a MMIMO base station to multiple MTs and schedule data transmissions, given heterogeneous traffic and channel conditions of multiple MTs. Furthermore, the statistics of users’ packet arrivals and channel states may not be known a priori and vary over time. In this paper, we propose a framework to optimize MMIMO beam power allocation and transmission scheduling in millimeter wave networks with time-varying traffic and channel conditions. The optimization problem is formulated as a Markov decision process (MDP) with the objective to minimize the overall queueing delay of multiple MTs by taking their heterogeneous and dynamic traffic and channel states into account. An online reinforcement learning scheme is designed which allows achieving the long-term optimal system performance with no requirement for a priori knowledge of user traffic statistics and wireless network states. Evaluation results show that our proposed scheme outperforms the state-of-the-art baselines.

KW - Dynamic networks

KW - Machine learning

KW - Markov decision process

KW - Massive MIMO

KW - Millimeter wave communications

KW - Power allocation

UR - http://www.scopus.com/inward/record.url?scp=85049054875&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-94268-1_25

DO - 10.1007/978-3-319-94268-1_25

M3 - Conference article in proceedings

SN - 978-3-319-94267-4

T3 - Lecture Notes in Computer Science

SP - 296

EP - 307

BT - Wireless Algorithms, Systems, and Applications, WASA 2018

PB - Springer

ER -

Liu H, Tran C, Lasota J, Dinh S, Chen X, Ouyang F. Massive MIMO power allocation in millimeter wave networks. In Wireless Algorithms, Systems, and Applications, WASA 2018. Springer. 2018. p. 296-307. (Lecture Notes in Computer Science, Vol. 10874). https://doi.org/10.1007/978-3-319-94268-1_25