Long-term evolution for machines (LTE-M)

Borkar, Suresh

doi:10.1016/b978-0-12-818880-4.00007-7

Cited by 29 publications

(18 citation statements)

References 3 publications

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“…LTE Cat-M1 [138] is a LPWAN that enables cellular services for the IoT world. Compared to NB-IoT, this technology provides higher data rate and the ability to use voice over the network, but requires more bandwidth and, therefore, the devices are more complex and expensive.…”

Section: Wireless Standards For Ai-enabled Iot Devicesmentioning

confidence: 99%

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Merenda

Porcaro

Iero

2020

Sensors

294

127

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In a few years, the world will be populated by billions of connected devices that will be placed in our homes, cities, vehicles, and industries. Devices with limited resources will interact with the surrounding environment and users. Many of these devices will be based on machine learning models to decode meaning and behavior behind sensors’ data, to implement accurate predictions and make decisions. The bottleneck will be the high level of connected things that could congest the network. Hence, the need to incorporate intelligence on end devices using machine learning algorithms. Deploying machine learning on such edge devices improves the network congestion by allowing computations to be performed close to the data sources. The aim of this work is to provide a review of the main techniques that guarantee the execution of machine learning models on hardware with low performances in the Internet of Things paradigm, paving the way to the Internet of Conscious Things. In this work, a detailed review on models, architecture, and requirements on solutions that implement edge machine learning on Internet of Things devices is presented, with the main goal to define the state of the art and envisioning development requirements. Furthermore, an example of edge machine learning implementation on a microcontroller will be provided, commonly regarded as the machine learning “Hello World”.

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Section: Wireless Standards For Ai-enabled Iot Devicesmentioning

confidence: 99%

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Merenda

Porcaro

Iero

2020

Sensors

294

127

View full text Add to dashboard Cite

show abstract

“…The LTE-M transmissions are limited to a narrowband size of 6 PRBs correspondings to 1.4 MHz including guardbands [3]. As the LTE system has a bandwidth from 1.4 to 20 MHz, some non-overlapping narrowbands (NBs) can be used if the LTE bandwidth exceeds 1.4 MHz [20]. Up to Rel-14, LTE-M device uses QPSK and 16-QAM modulation schemes with a single antenna for both downlink and uplink.…”

Section: Lte-machine (Type Communication): Lte-m(tc)mentioning

confidence: 99%

“…Up to Rel-14, LTE-M device uses QPSK and 16-QAM modulation schemes with a single antenna for both downlink and uplink. Whereas the support of 64-QAM in downlink has been introduced in Rel-15 [20].…”

Section: Lte-machine (Type Communication): Lte-m(tc)mentioning

confidence: 99%

“…Two device categories are defined by LTE-M: Cat-M1 and Cat-M2 corresponding to device categories introduced in Rel-13 and Rel-14 respectively. Cat-M1 has only a maximum channel bandwidth of 1.4 MHz compared to 5 MHz for Cat-M2 [20]. Besides, Cat-M2 supports a larger TBS of 6968 bits and 4008 bits in uplink and downlink respectively, compared to 2984 bits in both downlink and uplink for Cat-M1 [5].…”

Section: Lte-machine (Type Communication): Lte-m(tc)mentioning

confidence: 99%

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NB-IoT and LTE-M towards massive MTC: Complete performance evaluation for 5G mMTC

Hassan¹,

Mehdi²,

Saber³

2021

IJEECS

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Since the emerging 5G wireless network is expected to significantly revolutionize thefield of communication, its standardization and design should regard the internet ofthings (IoT) among the main orientations. Also, emerging IoT applications introducenew requirements other than throughput to support massive machine-type commu-nication (mMTC) where small data packets are occasionally sent. Therefore, moreimportance is attached to coverage, latency, power consumption, and connection den-sity. For this purpose, the third generation partnership project (3GPP) has introducedtwo novel cellular IoT technologies supporting mMTC, known as NB-IoT and LTE-M. This paper aims to determine the system configuration and deployment required forNB-IoT and LTE-M technologies to fully meet the 5G mMTC requirements in termsof coverage, throughput, latency, battery life, and connection density. An overview ofthese technologies and their design principles is also described. A complete evalua-tion of NB-IoT and LTE-M performance against 5G mMTC requirements is presented,and it is shown that these requirements can be met but only under certain conditionsregarding system configuration and deployment. This is followed by a performancecomparative analysis, which is mainly conducted to determine the limits and suitableuse cases of each technology.

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“…First, they demand a dedicated radio in addition to the already existing cellular radio in the user equipment (UE). This results in increased hardware overhead and additional cost, which are undesirable, especially for low-cost and low-power MTC and IoT devices [18], [19]. Secondly, without further enhancements, the technologies such as Bluetooth and Wi-Fi-Direct require manual device pairing and repeated user interventions to re-establish connections when disruptions occur [20]- [22].…”

mentioning

confidence: 99%

SCUBA: An In-Device Multiplexed Protocol for Sidelink Communication on Unlicensed Bands

Rajendran

Prasad

Lampe

et al. 2021

IEEE Internet Things J.

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Device-to-device communication (D2D) is a key enabler for connecting devices together to form the Internet of Things (IoT). A growing issue with IoT networks is the increasing number of IoT devices congesting the spectral resources of the cellular bands. Operating D2D in unlicensed band alleviates this issue by offloading network traffic from the licensed bands, while also reducing the associated licensing costs. To this end, we present a new low-cost radio access technology (RAT) protocol, called Sidelink Communications on Unlicensed BAnds (SCUBA), which can be implemented on cellular devices such that it coexists with the legacy cellular protocol by operating as a secondary RAT in a time division duplex manner using the existing radio hardware. SCUBA is compatible on different types of cellular devices including the low-complexity halfduplex frequency division duplex machine type communication (MTC) user equipments. SCUBA provides flexible sidelink (SL) latency and battery life tradeoff using a discontinuous reception procedure, which ensures that it is applicable across a wide range of use cases. We prove the effectiveness of our protocol with analyses and simulation results of the medium access control layer of SCUBA using different types of MTC traffic for both SL and the underlying cellular communication.

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Long-term evolution for machines (LTE-M)

Cited by 29 publications

References 3 publications

Edge Machine Learning for AI-Enabled IoT Devices: A Review

Edge Machine Learning for AI-Enabled IoT Devices: A Review

NB-IoT and LTE-M towards massive MTC: Complete performance evaluation for 5G mMTC

SCUBA: An In-Device Multiplexed Protocol for Sidelink Communication on Unlicensed Bands

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