Extended Delivery Time Analysis for Secondary Packet Transmission With Adaptive Modulation Under Interweave Cognitive Implementation

Wang, Wenjing; Usman, Muneer; Yang, Hong-Chuan; Alouini, Mohamed‐Slim

doi:10.1109/tccn.2017.2699971

Cited by 5 publications

(1 citation statement)

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“…Specifically, transmission time is closely related to the service time in the queuing setup, which is essential to the delay and throughput analysis of various wireless transmission strategies [5]- [7]. Transmission time was applied to investigate the extended delivery time of secondary transmission with interweave cognitive radio implementation [8], [9]. Transmission time also serves as a characterization of channel occupancy.…”

Section: Introductionmentioning

confidence: 99%

Wireless Transmission of Big Data: A Transmission Time Analysis Over Fading Channel

Wang

Yang

Alouini

2018

IEEE Trans. Wireless Commun.

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In this paper, we investigate the transmission time of a large amount of data over fading wireless channel with adaptive modulation and coding (AMC). Unlike traditional transmission systems, where the transmission time of a fixed amount of data is typically regarded as a constant, the transmission time with AMC becomes a random variable, as the transmission rate varies with the fading channel condition. To facilitate the design and optimization of wireless transmission schemes for big data applications, we present an analytical framework to determine statistical characterizations for the transmission time of big data with AMC. In particular, we derive the exact statistics of transmission time over block fading channels. The probability mass function (PMF) and cumulative distribution function (CDF) of transmission time are obtained for both slow and fast fading scenarios. We further extend our analysis to Markov channel, where transmission time becomes the sum of a sequence of exponentially distributed time slots. Analytical expression for the probability density function (PDF) of transmission time is derived for both fast fading and slow fading scenarios. These analytical results are essential to the optimal design and performance analysis of future wireless transmission systems for big data applications.

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Section: Introductionmentioning

confidence: 99%