PROJECT TITLE :
Power Loading in Parallel Diversity Channels Based on Statistical Channel Information
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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