273 research outputs found
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
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
Deep Reinforcement Learning for Real-Time Optimization in NB-IoT Networks
NarrowBand-Internet of Things (NB-IoT) is an emerging cellular-based
technology that offers a range of flexible configurations for massive IoT radio
access from groups of devices with heterogeneous requirements. A configuration
specifies the amount of radio resource allocated to each group of devices for
random access and for data transmission. Assuming no knowledge of the traffic
statistics, there exists an important challenge in "how to determine the
configuration that maximizes the long-term average number of served IoT devices
at each Transmission Time Interval (TTI) in an online fashion". Given the
complexity of searching for optimal configuration, we first develop real-time
configuration selection based on the tabular Q-learning (tabular-Q), the Linear
Approximation based Q-learning (LA-Q), and the Deep Neural Network based
Q-learning (DQN) in the single-parameter single-group scenario. Our results
show that the proposed reinforcement learning based approaches considerably
outperform the conventional heuristic approaches based on load estimation
(LE-URC) in terms of the number of served IoT devices. This result also
indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve
almost the same performance with much less training time. We further advance
LA-Q and DQN via Actions Aggregation (AA-LA-Q and AA-DQN) and via Cooperative
Multi-Agent learning (CMA-DQN) for the multi-parameter multi-group scenario,
thereby solve the problem that Q-learning agents do not converge in
high-dimensional configurations. In this scenario, the superiority of the
proposed Q-learning approaches over the conventional LE-URC approach
significantly improves with the increase of configuration dimensions, and the
CMA-DQN approach outperforms the other approaches in both throughput and
training efficiency
Code-Expanded Random Access for Machine-Type Communications
The random access methods used for support of machine-type communications
(MTC) in current cellular standards are derivatives of traditional framed
slotted ALOHA and therefore do not support high user loads efficiently.
Motivated by the random access method employed in LTE, we propose a novel
approach that is able to sustain a wide random access load range, while
preserving the physical layer unchanged and incurring minor changes in the
medium access control layer. The proposed scheme increases the amount of
available contention resources, without resorting to the increase of system
resources, such as contention sub-frames and preambles. This increase is
accomplished by expanding the contention space to the code domain, through the
creation of random access codewords. Specifically, in the proposed scheme,
users perform random access by transmitting one or none of the available LTE
orthogonal preambles in multiple random access sub-frames, thus creating access
codewords that are used for contention. In this way, for the same number of
random access sub-frames and orthogonal preambles, the amount of available
contention resources is drastically increased, enabling the support of an
increased number of MTC users. We present the framework and analysis of the
proposed code-expanded random access method and show that our approach supports
load regions that are beyond the reach of current systems.Comment: 6 Pages, 7 figures, This paper has been submitted to GC'12 Workshop:
Second International Workshop on Machine-to-Machine Communications 'Key' to
the Future Internet of Thing
Enhancing Radio Access Network Performance over LTE-A for Machine-to-Machine Communications under Massive Access
The expected tremendous growth of machine-to-machine (M2M) devices will require solutions to improve random access channel (RACH) performance. Recent studies have shown that radio access network (RAN) performance is degraded under the high density of devices. In this paper, we propose three methods to enhance RAN performance for M2M communications over the LTE-A standard. The first method employs a different value for the physical RACH configuration index to increase random access opportunities. The second method addresses a heterogeneous network by using a number of picocells to increase resources and offload control traffic from the macro base station. The third method involves aggregation points and addresses their effect on RAN performance. Based on evaluation results, our methods improved RACH performance in terms of the access success probability and average access delay
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