  A sum of lognormal random variables (RVs) appears in many problems of science
and engineering. For example, it is invloved in computing the distribution of
recevied signal and interference powers for radio channels subject to lognormal
shadow fading. Its distribution has no closed-from expression and it is
typically characterized by approximations, asymptotes or bounds. We give a
novel upper bound on the cumulative distribution function (CDF) of a sum of $N$
lognormal RVs. The bound is derived from the tangential mean-arithmetic mean
inequality. By using the tangential mean, our method replaces the sum of $N$
lognormal RVs with a product of $N$ shifted lognormal RVs. It is shown that the
bound can be made arbitrarily close to the desired CDF, and thus it becomes
more accurate than any other bound or approximation, as the shift approaches
infinity. The bound is computed by numerical integration, for which we
introduce the Mellin transform, which is applicable to products of RVs. At the
left tail of the CDF, the bound can be expressed by a single Q-function.
Moreover, we derive simple new approximations to the CDF, expressed as a
product $N$ Q-functions, which are more accurate than the previous method of
Farley.
