376 research outputs found
Prioritised Random Access Channel Protocols for Delay Critical M2M Communication over Cellular Networks
With the ever-increasing technological evolution, the current and future generation communication systems are geared towards accommodating Machine to Machine (M2M) communication as a necessary prerequisite for Internet of Things (IoT). Machine Type Communication (MTC) can sustain many promising applications through connecting a huge number of devices into one network. As current studies indicate, the number of devices is escalating at a high rate. Consequently, the network becomes congested because of its lower capacity, when the massive number of devices attempts simultaneous connection through the Random Access Channel (RACH). This results in RACH resource shortage, which can lead to high collision probability and massive access delay. Hence, it is critical to upgrade conventional Random Access (RA) techniques to support a massive number of Machine Type Communication (MTC) devices including Delay-Critical (DC) MTC. This thesis approaches to tackle this problem by modeling and optimising the access throughput and access delay performance of massive random access of M2M communications in Long-Term Evolution (LTE) networks.
This thesis investigates the performance of different random access schemes in different scenarios. The study begins with the design and inspection of a group based 2-step Slotted-Aloha RACH (SA-RACH) scheme considering the coexistence of Human-to-Human (H2H) and M2M communication, the latter of which is categorised as: Delay-Critical user equipments (DC-UEs) and Non-Delay-Critical user equipments (NDC-UEs). Next, a novel RACH scheme termed the Priority-based Dynamic RACH (PD-RACH) model is proposed which utilises a coded preamble based collision probability model. Finally, being a key enabler of IoT, Machine Learning, i.e. a Q-learning based approach has been adopted, and a learning assisted Prioritised RACH scheme has been developed and investigated to prioritise a specific user group. In this work, the performance analysis of these novel RACH schemes show promising results compared to that of conventional RACH
A Tractable Model of the LTE Access Reservation Procedure for Machine-Type Communications
A canonical scenario in Machine-Type Communications (MTC) is the one
featuring a large number of devices, each of them with sporadic traffic. Hence,
the number of served devices in a single LTE cell is not determined by the
available aggregate rate, but rather by the limitations of the LTE access
reservation protocol. Specifically, the limited number of contention preambles
and the limited amount of uplink grants per random access response are crucial
to consider when dimensioning LTE networks for MTC. We propose a low-complexity
model of LTE's access reservation protocol that encompasses these two
limitations and allows us to evaluate the outage probability at click-speed.
The model is based chiefly on closed-form expressions, except for the part with
the feedback impact of retransmissions, which is determined by solving a fixed
point equation. Our model overcomes the incompleteness of the existing models
that are focusing solely on the preamble collisions. A comparison with the
simulated LTE access reservation procedure that follows the 3GPP
specifications, confirms that our model provides an accurate estimation of the
system outage event and the number of supported MTC devices.Comment: Submitted, Revised, to be presented in IEEE Globecom 2015; v3: fixed
error in eq. (4
Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results
On the Reliability of LTE Random Access: Performance Bounds for Machine-to-Machine Burst Resolution Time
Random Access Channel (RACH) has been identified as one of the major
bottlenecks for accommodating massive number of machine-to-machine (M2M) users
in LTE networks, especially for the case of burst arrival of connection
requests. As a consequence, the burst resolution problem has sparked a large
number of works in the area, analyzing and optimizing the average performance
of RACH. However, the understanding of what are the probabilistic performance
limits of RACH is still missing. To address this limitation, in the paper, we
investigate the reliability of RACH with access class barring (ACB). We model
RACH as a queuing system, and apply stochastic network calculus to derive
probabilistic performance bounds for burst resolution time, i.e., the worst
case time it takes to connect a burst of M2M devices to the base station. We
illustrate the accuracy of the proposed methodology and its potential
applications in performance assessment and system dimensioning.Comment: Presented at IEEE International Conference on Communications (ICC),
201
Massive M2M Access with Reliability Guarantees in LTE Systems
Machine-to-Machine (M2M) communications are one of the major drivers of the
cellular network evolution towards 5G systems. One of the key challenges is on
how to provide reliability guarantees to each accessing device in a situation
in which there is a massive number of almost-simultaneous arrivals from a large
set of M2M devices. The existing solutions take a reactive approach in dealing
with massive arrivals, such as non-selective barring when a massive arrival
event occurs, which implies that the devices cannot get individual reliability
guarantees. In this paper we propose a proactive approach, based on a standard
operation of the cellular access. The access procedure is divided into two
phases, an estimation phase and a serving phase. In the estimation phase the
number of arrivals is estimated and this information is used to tune the amount
of resources allocated in the serving phase. Our results show that the
proactive approach is instrumental in delivering high access reliability to the
M2M devices.Comment: Accepted for presentation in ICC 201
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