@article{Koch2013a,

title = {On Noncoherent Fading Relay Channels at High Signal-to-Noise Ratio},

author = {Tobias Koch and Gerhard Kramer},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6378474},

issn = {0018-9448},

year  = {2013},

date = {2013-01-01},

journal = {IEEE Transactions on Information Theory},

volume = {59},

number = {4},

pages = {2221--2241},

abstract = {The capacity of noncoherent regular-fading relay channels is studied where all terminals are aware of the fading statistics but not of their realizations. It is shown that if the fading coefficient of the channel between the transmitter and the receiver can be predicted more accurately from its infinite past than the fading coefficient of the channel between the relay and the receiver, then at high signal-to-noise ratio (SNR), the relay does not increase capacity. It is further shown that if the fading coefficient of the channel between the transmitter and the relay can be predicted more accurately from its infinite past than the fading coefficient of the channel between the relay and the receiver, then at high SNR, one can achieve communication rates that are within one bit of the capacity of the multiple-input single-output fading channel that results when the transmitter and the relay can cooperate.},

keywords = {channel capacity, Channel models, Fading, fading channels, MIMO communication, multiple-input single-output fading channel statis, noncoherent, noncoherent fading relay channel capacity, radio receiver, radio receivers, radio transmitter, radio transmitters, Receivers, relay channels, relay networks (telecommunication), Relays, Signal to noise ratio, signal-to-noise ratio, SNR, statistics, time selective, Transmitters, Upper bound},

pubstate = {published},

tppubtype = {article}

}

@article{Djuric2010a,

title = {Assessment of Nonlinear Dynamic Models by Kolmogorov\textendashSmirnov Statistics},

author = {Petar M Djuric and Joaquin Miguez},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5491124},

issn = {1053-587X},

year  = {2010},

date = {2010-01-01},

journal = {IEEE Transactions on Signal Processing},

volume = {58},

number = {10},

pages = {5069--5079},

abstract = {Model assessment is a fundamental problem in science and engineering and it addresses the question of the validity of a model in the light of empirical evidence. In this paper, we propose a method for the assessment of dynamic nonlinear models based on empirical and predictive cumulative distributions of data and the Kolmogorov-Smirnov statistics. The technique is based on the generation of discrete random variables that come from a known discrete distribution if the entertained model is correct. We provide simulation examples that demonstrate the performance of the proposed method.},

keywords = {Cumulative distributions, discrete random variables, dynamic nonlinear models, Electrical capacitance tomography, Filtering, filtering theory, Iron, Kolmogorov-Smirnov statistics, Kolomogorov\textendashSmirnov statistics, model assessment, nonlinear dynamic models, nonlinear dynamical systems, Permission, predictive cumulative distributions, predictive distributions, Predictive models, Random variables, Robots, statistical analysis, statistical distributions, statistics, Telecommunication control},

pubstate = {published},

tppubtype = {article}

}