Massive MIMO for Internet of Things (IoT) connectivity

Bana, Alexandru-Sabin; Carvalho, Elisabeth de; Soret, Beatriz; Abrão, Taufik; Marinello, José Carlos; Larsson, Erik G.; Popovski, Petar

doi:10.1016/j.phycom.2019.100859

Cited by 97 publications

(66 citation statements)

References 57 publications

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Nan Cheng. are the cornerstones of machine-type traffic and thus enablers of various types of IoT connectivity. URLLC, also known as mission-critical IoT, envisions transmission of moderately small data packets with extremely high-reliability, in which the user plane latency requirement is commonly defined to be 1 ms, including uplink and downlink roundtrip transmission [5]. mMTC aims to provide service to a vast number of devices, which do not have strict delay requirements.…”

Section: And Mmtcmentioning

confidence: 99%

OAM-Based Concentric Spatial Division Multiplexing for Cellular IoT Terminals

et al. 2020

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Internet of Things (IoT) is an emerging network that aims to connect massive devices for exchanging information with each other. There have been various IoT access techniques, such as RFID, Blue Tooth, WiFi, LoRa and cellular IoT, in which cellular IoT has advantages in wide coverage and the economical reuse of exsiting cellular infrastructure. However, cellular base station (BS) consumes most of the energy in wireless communication networks. Therefore, improving the energy efficiency (EE) of the BS is of great importance to make cellular IoT green. In this paper, we propose a novel orbital angular momentum (OAM)-based concentric spatial division multiplexing (CSDM) downlink transmission scheme for cellular IoT terminals. Specifically, the hybrid radio frequency (RF) and baseband CSDM scheme exploits analog OAM beamforming to generate concentric annular beams for covering cell-center, cell-mediate and cell-edge groups of users, and digital space-frequency vector perturbation (SFVP) precoding and orthogonal frequency division multiple access (OFDMA) to mitigate inter-and intra-group interferences. Mathematical analysis and numerical simulations validate that the IoT BS with the proposed CSDM-SFVP scheme has better bit error rate (BER) and EE performance than traditional massive multiple-input multiple-output (MIMO) BS with full-baseband zero-forcing (FBZF) precoding in a IoT cell. INDEX TERMS Internet of things (IoT), orbital angular momentum (OAM), uniform circular array (UCA), massive machine-type communications (mMTC), concentric spatial division multiplexing (CSDM).

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Section: And Mmtcmentioning

confidence: 99%

OAM-Based Concentric Spatial Division Multiplexing for Cellular IoT Terminals

et al. 2020

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“…Massive MIMO is not only beneficial for the eMBB use-case -it also provides some benefits for URLLC [57,58]. Using massive MIMO can help reduce the outage probability, and therefore increase the reliability, compared 1.1.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to massive MIMO being beneficial for eMBB and URLLC, massive MIMO is also beneficial for the mMTC use-case [58,60,61]. The most straight-forward benefit is the increase in SNR due to the beamforming gain [15].…”

Section: Introductionmentioning

confidence: 99%

On Massive MIMO for Massive Machine-Type Communications

Becirovic

2020

Linköping Studies in Science and Technology. Licentiate Thesis

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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 faraway 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.

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“…As the number of antennas at the base station (BS) side is increased and many users are served in a multiuser MIMO way, the sum rate and the energy efficiency are promised to increase simultaneously [3]- [5]. Since a largescale antenna array at the BS leads to channel hardening in a wireless fading channel, the reliability and latency are also enhanced when scaling up a MIMO system with respect to the number of BS antennas [6]. VOLUME 4, 2016 Many of the theoretical works reporting the aforementioned desired improvements assume wireless channels following an i.i.d.…”

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confidence: 99%

“…VOLUME 4, 2016 Many of the theoretical works reporting the aforementioned desired improvements assume wireless channels following an i.i.d. Rayleigh fading behavior [1], [3], [6]. Especially for indoor mobile users, an uncorrelated i.i.d.…”

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Measured User Correlation in Outdoor-to-Indoor Massive MIMO Scenarios

et al. 2020

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The performance of a multiuser multiple-input multiple-output (MIMO) system is mainly determined by the correlation of user channels. Applying channel models without spatial correlation or spatially inconsistent channel models for performance analysis leads to an underestimation of spatial correlation and therefore to overly optimistic system performance. This is especially pronounced when the wireless channel is modeled as i.i.d. Rayleigh fading for users in a rich scattering environment, for example, for indoor users. To analyze the spatial channel correlation in massive MIMO systems, we performed wireless channel measurements in three different scenarios. In each scenario, we measure 148 receiver positions, spread over a length of almost 9 m. Since the wireless channel statistics change with receiver position, we perform a stationarity analysis. We provide a statistical analysis of the measured channel in terms of amplitude distribution and user-side spatial correlation within the region of stationarity. This analysis shows that, even for a deep-indoor user location, the spatial correlation is significantly higher compared to a Rician channel model with the same K factor. We model the measured channel by means of a Rician channel model and a spatially consistent channel model, which is based on the scenario geometry, to provide an insight for the observed propagation phenomena. Results show that the user correlation is not negligible for massive MIMO in outdoor-to-indoor scenarios. The achievable spectral efficiency with linear precoding is 20% lower compared to the Rician channel model, even for large inter-user distances of 6 m. INDEX TERMS MIMO, mobile communications, channel measurements, channel modeling I. INTRODUCTION

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Massive MIMO for Internet of Things (IoT) connectivity

Cited by 97 publications

References 57 publications

OAM-Based Concentric Spatial Division Multiplexing for Cellular IoT Terminals

OAM-Based Concentric Spatial Division Multiplexing for Cellular IoT Terminals

On Massive MIMO for Massive Machine-Type Communications

Measured User Correlation in Outdoor-to-Indoor Massive MIMO Scenarios

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