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ردیف | عنوان | نوع |
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1 |
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 |
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 |