We develop a unified framework for the instantaneous total level crossing rate (LCR) of products of independent positive nonstationary fading processes. Universal upper and lower bounds are first derived from an exact decomposition of the product derivative and signed crossing imbalance, requiring only factor-wise crossing statistics. We then introduce the drifting Gaussian-derivative (DGD) class, in which each conditional derivative is Gaussian with possibly nonzero drift. For products of DGD factors, an exact Gaussian-mixture representation yields bounds that jointly capture aggregate drift and derivative fluctuations. The lower bound reduces under stationarity to a known $\ell_2$ bound, whereas, to the best of the author's knowledge, the upper bound is new even in the stationary case. Furthermore, the bounds are asymptotically exact in drift-dominated regimes. The framework is specialized to nonstationary Double-Rayleigh fading and to quasi-identically distributed DGD factors, with closed forms for products of $α$-$μ$ envelopes. Numerical results show that the DGD bounds closely bracket the exact LCR.
Decentralized networks can learn collision-free periodic transmission schedules, but acquisition can consume much of a finite operating horizon. We characterize acquisition in a reservation protocol for a fixed population of $n$ nodes and a period of $Γ_n\ge n$ slots. Initially, each node attempts independently in each slot with probability proportional to $1/n$. A success reserves a slot that the node reuses every $Γ_n$ slots, retaining it after collisions. Nodes use only their own acknowledgments and small local memory. An exact finite-population Markov reduction, independent of the arrangement of reserved slots, characterizes the time until every node has a distinct reservation. As $n\to\infty$, mean acquisition takes $O(n^2)$ slots when $Γ_n=n$ and $O(n\log n)$ slots when $Γ_n\sim(1+θ)n$, for fixed $θ>0$. With a fixed number $Γ_n-n$ of spare slots, the standard deviation of acquisition time has the same asymptotic order as its mean. If that number diverges while $Γ_n/n$ remains bounded, the ratio of standard deviation to mean tends to zero. A diverging sublinear number of spare slots yields $o(n^2)$ mean acquisition while long-run utilization, the fraction $n/Γ_n$ of slots with successful transmissions, tends to one. Simulations validate these laws and compare acquisition and delivered packets per slot with existing access protocols.