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On Massive MIMO for Massive Machine-Type Communications
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, Faculty of Science & Engineering.
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

To cover all the needs and requirements of mobile networks in the future, the predicted usage of the mobile networks has been split into three use-cases: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. In this thesis we focus on the massive machine-type communication use-case which is intended to facilitate the ever increasing number of smart devices and sensors.

In the massive machine-type communication use-case, the main challenges are to accommodate a huge number of devices while keeping the battery lives of the devices long, and allowing them to be placed in far-away locations. However, these devices are not concerned about other features such as latency, high data rate, or mobility.

In this thesis we study the application of massive MIMO (multiple-input multiple-output) technology for the massive machine-type communication use-case. Massive MIMO has been on the radar as an enabler for future communication networks in the last decade and is now firmly rooted in both academia and industry. The main idea of massive MIMO is to utilize a base station with a massive number of antennas which gives the ability to spatially direct signals and serve multiple devices in the same time- and frequency resource.

More specifically, in this thesis we study A) a scenario where the base station takes advantage of a device's low mobility to improve its channel estimate, B) a random access scheme for massive machine-type communication which can accommodate a huge number of devices, and C) a case study where the benefits of massive MIMO for long range devices are quantified. The results are that the base station can significantly improve the channel estimates for a low mobility user such that it can tolerate lower SNR while still achieving the same rate. Additionally, the properties of massive MIMO greatly helps to detect users in random access scenarios and increase link-budgets compared to single-antenna base stations.  

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. , p. 34
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1868
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-162586DOI: 10.3384/lic.diva-162586ISBN: 9789179299156 (print)OAI: oai:DiVA.org:liu-162586DiVA, id: diva2:1376417
Presentation
2020-02-27, Systemet, B-Building, Campus Valla, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2020-02-03 Created: 2019-12-09 Last updated: 2020-02-06Bibliographically approved
List of papers
1. DETECTION OF PILOT-HOPPING SEQUENCES FOR GRANT-FREE RANDOM ACCESS IN MASSIVE MIMO SYSTEMS
Open this publication in new window or tab >>DETECTION OF PILOT-HOPPING SEQUENCES FOR GRANT-FREE RANDOM ACCESS IN MASSIVE MIMO SYSTEMS
2019 (English)In: 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), IEEE , 2019, p. 8380-8384Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we study an active user detection problem for massive machine type communications (mMTC). The users transmit pilot-hopping sequences and detection of active users is performed based on the received energy. We utilize the channel hardening and favorable propagation properties of massive multiple- input multipleoutput (MIMO) to simplify the user detection. We propose and compare a number of different user detection methods and find that using non- negative least squares (NNLS) is well suited for the task at hand as it achieves good results as well as having the benefit of not having to specify further parameters.

Place, publisher, year, edition, pages
IEEE, 2019
Series
International Conference on Acoustics Speech and Signal Processing ICASSP, ISSN 1520-6149
Keywords
massive MIMO; machine type communication; compressed sensing
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-160640 (URN)000482554008124 ()978-1-4799-8131-1 (ISBN)
Conference
44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Note

Funding Agencies|ELLIIT; Swedish Research Council (VR)Swedish Research Council

Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2020-02-03
2. How Much Will Tiny IoT Nodes Profit from Massive Base Station Arrays?
Open this publication in new window or tab >>How Much Will Tiny IoT Nodes Profit from Massive Base Station Arrays?
2018 (English)In: 2018 26TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), IEEE COMPUTER SOC , 2018, p. 832-836Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we study the benefits that Internet-of-Things (IoT) devices will have from connecting to a massive multiple-input-multiple-output (MIMO) base station. In particular, we study how many users that could be simultaneously spatially multiplexed and how much the range can be increased by deploying massive base station arrays. We also investigate how the devices can scale down their uplink power as the number of antennas grows with retained rates. We consider the uplink and utilize upper and lower bounds on known achievable rate expressions to study the effects of the massive arrays. We conduct a case study where we use simulations in the settings of existing IoT systems to draw realistic conclusions. We find that the gains which ultra narrowband systems get from utilizing massive MIMO are limited by the bandwidth and therefore those systems will not be able to spatially multiplex any significant number of users. We also conclude that the power scaling is highly dependent on the nominal signal-to-noise ratio (SNR) in the single-antenna case.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
European Signal Processing Conference, ISSN 2076-1465
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-154134 (URN)10.23919/EUSIPCO.2018.8553057 (DOI)000455614900168 ()978-90-827970-1-5 (ISBN)
Conference
European Signal Processing Conference (EUSIPCO)
Note

Funding Agencies|Swedish Research Council (VR); ELLIIT

Available from: 2019-01-29 Created: 2019-01-29 Last updated: 2020-02-03

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