No abstract

Orthogonal frequency division multiplexing (OFDM) is a promising framework for future wireless communication systems. One of the main impediments that has limited the applicability of OFDM systems in low-power wireless communication systems is the highly variable amplitude of the baseband transmitted signal; thus, a number of recent analyses have characterized this variation. These analyses have generally employed the following two components: (1) the assumption that the complex envelope of the OFDM signal converges to a Gaussian random process in some sense as the number of subcarriers becomes large, and (2) Rice's classical results on level-crossing rates for the envelope of Gaussian random processes. In this work, we improve on both of these components to arrive at a simple, accurate, and rigorouslyestablished expression for the peak distribution of the OFDM envelope. In particular, using a rigorous (and non-trivial) proof establishing the convergence in (1) above as justification, the modern extreme value theory for chi-squared processes is applied to the problem. Numerical results for both uncoded and coded systems establish that the simple expression obtained for the distribution of the peaks of the envelope process is extremely accurate, even for a modest number of subcarriers.

Abstract-The increasing complexity of the Internet demands continued improvements to measurement techniques and data analysis methods to aid our understanding of network operation. The availability of accurate measurement data is necessary in many areas ranging from attack detection, novel pricing schemes, buffer dimensioning and switch design to general network management. In this paper, we develop a theory for accurate and unbiased Internet traffic measurement using the tools of Poisson random sampling. We show how this approach helps in storing, managing, and aggregating data from different sources with independent clocks and sampling rates. We present results that show that stochastic sampling maintains important information about network measurements that would be lost when using conventional uniform sampling.