Machine type communications in 3GPP systems

Jain, Pulkit; Hedman, Peter; Zisimopoulos, H.

doi:10.1109/mcom.2012.6353679

Cited by 71 publications

(55 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This threshold is solely determined by the previously defined risk factors as Ω = CFa CFi . With (1), and the assumption of white Gaussian noise, the likelihood p (y i…”

Section: B Activity Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Compressed sensing Bayes-risk detection for frame based multi-user systems

Monsees

Bockelmann

Dekorsy

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

View full text Add to dashboard Cite

Abstract-Performing joint activity and data detection has recently gained attention for reducing signaling overhead in multi-user Machine-to-Machine Communication systems. In this context, Compressed Sensing has been identified as a good candidate for joint activity and data detection especially in scenarios where the activity probability is very low. This paper augments activity and data detection for frame based multi-user uplink scenarios where nodes are (in)active for the duration of a frame. We propose a two stage detector which first estimates the set of active nodes followed by a data detector. Our detector outperforms symbol-by-symbol Maximum a posteriori detection.

show abstract

“…This threshold is solely determined by the previously defined risk factors as Ω = CFa CFi . With (1), and the assumption of white Gaussian noise, the likelihood p (y i…”

Section: B Activity Estimationmentioning

confidence: 99%

“…Compared to human driven communication, MTC is quite often sporadic and of very low data rate making extensive signaling and complex scheduling inappropriate [1], [2]. Recent research has shown how signaling and scheduling complexity can be reduced by physical layer joint activity and data detection [3]- [5].…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing Bayes-risk detection for frame based multi-user systems

Monsees

Bockelmann

Dekorsy

2013

2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)

View full text Add to dashboard Cite

show abstract

“…In other words, a large number of devices try to access a network within a short period of time, which may cause congestion problems. To solve the problem, an access control mechanism to regulate the access must protect the system's performance and service quality [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Forwarding for Machine Type Communications in Internet of Things Networks

Lim

Park

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

The MTC (Machine Type Communications) system is one of the most promising technologies to provide IoT (Internet of Things) applications. The MTC system suffers from congestion due to limited data transmission capacity and burst traffic. The congestion disturbs data delivery, results in an increase of energy consumption due to data retransmission, and finally poses a threat to the providers of IoT applications. In this paper, we focus on the congestion problem and present an efficient data forwarding mechanism to regulate the burst channel access from a large number of MTC devices. To regulate the communication channel access from the devices, we predict the number of devices by separating traffic load into the prediction of the number of devices that newly attempt to have access and the prediction of the number of devices that retry to access the channel. Through simulations, we show that our mechanism improves access success probability and reduces collision probability and access delay.

show abstract

“…[8][9][10] The 3GPP version of M2M, called ''machine-type communication'' (MTC), was standardized in release 12 . 11 In Rel-12, a new profile, referred to as ''category 0'' (CAT-0) was defined for low-cost MTC operation.…”

Section: Introductionmentioning

confidence: 99%

Synchronization method for long-term evolution-based machine-type communication in low-power cellular Internet of Things

Pec

Choi

Park

et al. 2016

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

The significant growth of machine-to-machine applications for low-power cellular Internet of Things has compelled 3rd Generation Partnership Project to ensure that the future release of long-term evolution can support massive transfer of small, infrequent packets using ultra-low-power and low-cost devices. The 3rd Generation Partnership Project version of machine-to-machine, called ''machine-type communication,'' is currently being standardized for low-cost machine-type communication operations. In this article, a complete synchronization and cell search procedure is described for machine-type communication devices in long-term evolution systems. Low-complexity algorithms for primary synchronization signal and secondary synchronization signal detection, which requires the highest computational complexity in synchronization and cell search period, are also proposed for low-power machine-type communication devices. Through simulation under long-term evolution-based machine-type communication environments, we show that the proposed methods for primary synchronization signal and secondary synchronization signal detection require six and five times less computational complexity than the conventional methods, respectively, while their performance is similar. The proposed algorithms allow machine-type communication devices in a discontinuous reception cycle to resynchronize quickly with less power when synchronization is lost during a deep sleep period.

show abstract

Machine type communications in 3GPP systems

Cited by 71 publications

References 0 publications

Compressed sensing Bayes-risk detection for frame based multi-user systems

Compressed sensing Bayes-risk detection for frame based multi-user systems

Efficient Data Forwarding for Machine Type Communications in Internet of Things Networks

Synchronization method for long-term evolution-based machine-type communication in low-power cellular Internet of Things

Contact Info

Product

Resources

About