• Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access
  

  سال انتشار:

  2017

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  عنوان انگلیسی مقاله:

  Minimizing contention collision probability and guaranteeing packet delay for cloud big data transmissions in 4G LTE-A packet random access

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  ترجمه فارسی عنوان مقاله:

  به حداقل رساندن احتمال برخورد و تضمین تاخیر بسته برای ابر انتقال داده های بزرگ در 4G LTE-A با دسترسی تصادفی بسته

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  منبع:

  Sciencedirect - Elsevier - Computer Networks 112 (2017) 329–344

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  نویسنده:

  Ben-Jye Chang∗, Gunag-Jie Jhang

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  چکیده انگلیسی:

  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

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  سطح: متوسط

  تعداد صفحات فایل pdf انگلیسی: 16

  حجم فایل: 3258 کیلوبایت

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  قیمت: رایگان

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  توضیحات اضافی:

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  سفارش ترجمه مقاله

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  کلمات کلیدی:
  Big data cloud service , Random access channel (RACH) , LTE-A , Differentiate preamble collision domains , Collision probability , سرویس ابری داده های بزرگ , کانال دسترسی تصادفی (RACH) , تمایز دامنه برخورد , احتمال برخورد , دانلود مقاله isi داده های بزرگ رایگان , دانلود مقاله big data رایگان , دانلود مقاله انگلیسی داده های بزرگ رایگان , دانلود مقاله انگلیسی big data رایگان , دانلود مقاله انگلیسی داده های بزرگ 2017 رایگان , دانلود مقاله انگلیسی big data 2017 رایگان

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  بازدید:[1] بازدید کننده:[1] نظرات : [0]

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