PROJECT TITLE :

Power Loading in Parallel Diversity Channels Based on Statistical Channel Information

ABSTRACT:

In this paper, we show that there exists an arbitrary number of power allocation schemes that achieve capacity in systems operating in parallel channels comprised of single-input multiple-output (SIMO) Nakagami-m fading subchannels when the amount of degrees of freedom L (e.g., the quantity of receive antennas) tends to infinity. Statistical waterfilling — i.e., waterfilling using channel statistics instead of instantaneous channel data — is one such theme. We further prove that the convergence of statistical waterfilling to the optimal power loading theme is a minimum of mathcalO( one/( L log L) ) , whereas convergence of other schemes is at worst mathcalO( one/ log L) . To validate and demonstrate the practical use of our findings, we tend to evaluate the mutual info of example SIMO parallel channels using simulations plus new measured ultrawideband channel knowledge.


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