Channel adaptive transmission of big data: a complete temporal characterization and its application




Wang, Wen-Jing

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We investigate the statistics of transmission time of wireless systems employing adaptive transmission. Unlike traditional transmission systems where the transmission time of a fixed amount of data is typically regarded as a constant, the transmission time with adaptive transmission systems becomes a random variable, as the transmission rate varies with the fading channel condition. To facilitate the design and optimization of wireless transmission schemes, we present an analytical framework to determine statistical characterizations for the transmission time with adaptive transmission. 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 channels, where the transmission time becomes a sequence of exponentially distributed random-length time slots. Analytical expression for the probability density function (PDF) of transmission time is derived for both fast and slow fading scenarios. Since the energy consumption can be characterized by the product of power consumption and transmission time, we also evaluate the energy consumption for wireless systems with adaptive transmission. Cognitive radio communication can opportunistically access underutilized spectrum for emerging wireless applications. With interweave cognitive implementation, a secondary user (SU) transmits only if a primary user does not occupy the channel and waits for transmission otherwise. Therefore, secondary packet transmission involves both transmission and waiting periods. The resulting extended delivery time (EDT) is critical to the throughput analysis of secondary system. With the statistical results of transmission time, we derive the PDF of EDT considering random-length SU transmission and waiting periods for continuous spectrum sensing and semi-periodic spectrum sensing. Taking spectrum sensing errors into account, we propose a discrete Markov chain modeling slotted secondary transmission coupled with periodic spectrum sensing. Markov modeling is applied to energy efficiency optimization and queuing performance evaluation.



Wireless communication, Transmission time, Cognitive radio, Statistical characterization