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ردیف | عنوان | نوع |
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1 |
Delay-aware dynamic access control for mMTC in wireless networks using deep reinforcement learning
تاخیر در کنترل دسترسی پویا برای mMTC در شبکه های بی سیم با استفاده از یادگیری تقویتی عمیق-2020 The success of the applications based on the Internet of Things (IoT) relies heavily on the ability to process large
amounts of data with different Quality-of-Service (QoS) requirements. Access control remains an important
issue in scenarios where massive Machine-Type Communications (mMTC) prevail, and as a consequence,
several mechanisms such as Access Class Barring (ACB) have been designed aiming at reducing congestion.
Although this mechanism can effectively increase the total number of User Equipments (UEs) that can access
the system, it can also harm the access delay, limiting its usability in some scenarios. In this work, we propose
a delay-aware double deep reinforcement learning mechanism that can dynamically adapt two parameters
of the system in order to enhance the probability of successful access using ACB, while at the same time
reducing the expected delay by modifying the Random Access Opportunity (RAO) periodicity. Results show
that our system can accept a simultaneously massive number of machine-type and human-type UEs while at
the same time reducing the mean delay when compared to previously known solutions. This mechanism can
work adequately under varying load conditions and can be trained with real data traces, which facilitates its
implementation in real scenarios. Keywords: Delay | Double Deep Q-Learning | Access Class Barring | Massive machine type communications |
مقاله انگلیسی |
2 |
Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access
به حداقل رساندن احتمال برخورد و تضمین تاخیر بسته برای ابر انتقال داده های بزرگ در 4G LTE-A با دسترسی تصادفی بسته-2017 For transmitting Explosive Bursts Big Data of Mobile Cloud Computing applications, the 4G LTE/LTE-A
standards are specified to provide extreme high data rate and low access delay for various real-time
demanded cloud services. In the uplink, data packet transmissions of different classes of traffic of vari
ous UEs need randomly contend for the limited number of preambles through the Uplink RACH channel
time slots. Clearly, the extremely explosive data contentions certainly yield serious collisions, and then
significantly increase access delay and packet dropping rate. That is, the quality of service (QoS) of the
delay-sensitive-based real-time traffic and the loss-sensitive-based non-real-time traffic cannot be guar
anteed satisfyingly. For overcoming the critical random access issue in cloud services over 4G LTE-A, 3GPP
specifies the Uniform Distribution Backoff Procedure and Access Class Barring (ACB) as the random ac
cess mechanism. The Random Access CHannel (RACH) for random contentions in 3GPP LTE-A neglects
some key factors: 1) different classes of traffic requiring different delay bounds, 2) how to reducing col
lision probability, 3) intensive congestion traffic and 4) differentiating the collision domains. This paper
thus proposes an adaptive random contention approach (ARC) that consists of three phases: 1) Sigmoid
based Access Class Barring algorithm, 2) Dynamic Preamble Selection Range (DPSR) algorithm, and 3)
Dynamic Initial Backoff (DIB) algorithm. The main contribution of ARC is based on the adaptive Sigmoid
feature analysis of Cumulative Distribution Function of Normal Distribution according to the successful
contention probability and the RACH congestion state. Numerical results demonstrate that the proposed
approach outperforms the compared approaches in collision probability, goodput and access delay. Fur
thermore, the mathematical analytical model for the proposed approach is analyzed. The analysis result
is very close the simulation result. It justifies the correctness and efficiency of the proposed approach.
Keywords:Big data cloud service|Random access channel (RACH)|LTE-A|Differentiate preamble collision domains|Collision probability |
مقاله انگلیسی |
3 |
Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access
به حداقل رساندن احتمال برخورد و تضمین تاخیر بسته برای ابر انتقال داده های بزرگ در 4G LTE-A با دسترسی تصادفی بسته-2017 For transmitting Explosive Bursts Big Data of Mobile Cloud Computing applications, the 4G LTE/LTE-A
standards are specified to provide extreme high data rate and low access delay for various real-time
demanded cloud services. In the uplink, data packet transmissions of different classes of traffic of vari
ous UEs need randomly contend for the limited number of preambles through the Uplink RACH channel
time slots. Clearly, the extremely explosive data contentions certainly yield serious collisions, and then
significantly increase access delay and packet dropping rate. That is, the quality of service (QoS) of the
delay-sensitive-based real-time traffic and the loss-sensitive-based non-real-time traffic cannot be guar
anteed satisfyingly. For overcoming the critical random access issue in cloud services over 4G LTE-A, 3GPP
specifies the Uniform Distribution Backoff Procedure and Access Class Barring (ACB) as the random ac
cess mechanism. The Random Access CHannel (RACH) for random contentions in 3GPP LTE-A neglects
some key factors: 1) different classes of traffic requiring different delay bounds, 2) how to reducing col
lision probability, 3) intensive congestion traffic and 4) differentiating the collision domains. This paper
thus proposes an adaptive random contention approach (ARC) that consists of three phases: 1) Sigmoid
based Access Class Barring algorithm, 2) Dynamic Preamble Selection Range (DPSR) algorithm, and 3)
Dynamic Initial Backoff (DIB) algorithm. The main contribution of ARC is based on the adaptive Sigmoid
feature analysis of Cumulative Distribution Function of Normal Distribution according to the successful
contention probability and the RACH congestion state. Numerical results demonstrate that the proposed
approach outperforms the compared approaches in collision probability, goodput and access delay. Fur
thermore, the mathematical analytical model for the proposed approach is analyzed. The analysis result
is very close the simulation result. It justifies the correctness and efficiency of the proposed approach.
Keywords: Big data cloud service | Random access channel (RACH) | LTE-A | Differentiate preamble collision domains | Collision probability |
مقاله انگلیسی |
4 |
Efficient cooperative access class barring with load balancing and traffic adaptive radio resource management for M2M communications over LTE-A
Efficient cooperative access class barring with load balancing and traffic adaptive radio resource management for M2M communications over LTE-A-2014 We propose two efficient cooperative access class barring with load balancing (CACB-LB)
and traffic adaptive radio resource management (TARRM) schemes for M2M communications over LTE-A. The proposed CACB-LB uses the percentage of the number of MachineType Communication (MTC) devices that can only access one eNB between two adjacent
eNBs as a criterion to allocate those MTC devices that are located in the overlapped coverage area to each eNB. Note that an eNB is a base station of LTE-A. In this way, the proposed
CACB-LB can achieve better load balancing among eNBs than CACB, which is the best available related work. The proposed CACB-LB also uses the ratio of the channel quality indication that an MTC device received from an eNB over the number of MTC devices that attach
to the eNB as a criterion to adjust the estimated number of MTC devices that may access
the eNB. As a result, the proposed CACB-LB can have a better set of barring rates of access
class barring than CACB and can reduce random access delay experienced by an MTC
device, which is also applicable to user equipment (UE). After an MTC device successfully
accesses to an eNB, the eNB needs to allocate radio resources for the MTC device. In addition, the proposed TARRM allocates radio resources for an MTC device based on the random
access rate of the MTC device and the amount of data uploaded and downloaded by the
MTC device in a homogeneous MTC device network, and the priority of an MTC device in
a heterogeneous MTC device network. Furthermore, we use the concept from cognitive
radio networks such that if there are unused physical resource blocks (PRBs) of UEs, an
eNB can schedule MTC devices to use these PRBs to enhance the throughput performance.
Simulation results show that either in a homogeneous MTC device network or in a heterogeneous MTC device network, the proposed CACB-LB’s average access delay of UEs/MTC
devices and average throughput from UEs/MTC devices are better than CACB’s. The proposed CACB-LB with TARRM’s average throughput from UEs/MTC devices is also higher
than CACB’s. Therefore, the proposed CACB-LB with TARRM is feasible for M2M communications over LTE-A.
Keywords:
Access class barring
LTE-A
M2M communications
Random access
Radio resource management |
مقاله انگلیسی |