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Shannon's formula C = 1/2log(1+P/N) is the emblematic expression for the information capacity of a communication channel. Hartley's name is often associated with it, owing to Hartley's rule: counting the highest possible number of distinguishable values for a given amplitude Δ and precision ±Δ yields a similar expression C' = log(1+A/Δ). In the information theory community, the following "historical" statements are generally well accepted: (1) Hartley put forth his rule twenty years before Shannon; (2) Shannon's formula as a fundamental tradeoff between transmission rate, bandwidth, and signal-to-noise ratio came unexpected in 1948; (3) Hartley's rule is an imprecise relation while Shannon's formula is exact; (4) Hartley's expression is not an appropriate formula for the capacity of a communication channel. We show that all these four statements are questionable, if not wrong. [ABSTRACT FROM AUTHOR]

We consider lower and upper bounds on the difference of differential entropies of a Gaussian random vector and an approximately Gaussian random vector after they are "smoothed" by an arbitrarily distributed random vector of finite power. These bounds are important to establish the optimality of the corner points in the capacity region of Gaussian interference channels. A problematic issue in a previous attempt to establish these bounds was detected in 2004 and the mentioned corner points have since been dubbed "the missing corner points". The importance of the given bounds comes from the fact that they induce Fano-type inequalities for the Gaussian interference channel. Usual Fano inequalities are based on a communication requirement. In this case, the new inequalities are derived from a non-disturbance constraint. The upper bound on the difference of differential entropies is established by the data processing inequality (DPI). For the lower bound, we do not have a complete proof, but we present an argument based on continuity and the DPI. [ABSTRACT FROM AUTHOR]