Soft Covering Through the Lens of Hypothesis Testing

Abstract: We study the soft covering phenomenon through the lens of Neyman--Pearson hypothesis testing: given a channel output sequence $y<sup>n$, can one decide whether it was produced when the channel was driven by a random codeword, or generated independently from the output marginal? We derive exact exponential decay rates for the jointly averaged false-alarm (FA) probability $α<em>n(τ,R)$ and missed-detection (MD) probability $β_n(τ,R)$, as functions of the decision threshold $τ$ and the codebook rate $R$. The derived single-letter formulas of the exponents $\EFA(τ,R)=-\lim</em>{n\to\infty}\frac{1}{n}\lnα<em>n(τ,R)$ and $\EMD(τ,R)=-\lim</em>{n\to\infty}\frac{1}{n}\lnβ<em>n(τ,R)$ are tight in the random coding sense. The analysis reveals a rich phase structure. For $R &lt; I(X;Y)$, there is a genuine exponential tradeoff between the two error types over the interval $τ\in (0, I(X;Y)-R)$. At $R = I(X;Y)$, this tradeoff interval collapses to the single point $τ= 0$, where both error exponents simultaneously vanish, a fact which manifests the soft covering phenomenon in the Neyman--Pearson sense. For $R &gt; I(X;Y)$, the same instantaneous collapse persists at $τ= 0$; moreover, for every $τ$ at least one exponent is zero: the FA exponent is zero for $τ\le 0$ (FA probability does not decay exponentially), and the MD exponent is zero for $τ\ge 0$ (and finite, channel-specific for $τ&lt;0$; see Remark~\ref{rem:jump}). There is no interval of $τ$ where both exponents are simultaneously positive. A sharp phase transition in the MD exponent occurs at $τ^* = [I(X;Y)-R]</em>+$ for all rates.