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Distributionally Robust Power Policies for Wireless Systems under Power Fluctuation Risk

Modern wireless communication systems necessitate the development of cost-effective resource allocation strategies, while ensuring maximal system performance. While commonly realizable via efficient waterfilling schemes, ergodic-optimal policies often exhibit instantaneous resource constraint fluctuations as a result of fading variability, violating prescribed specifications possibly within unacceptable margins, inducing further operational challenges and/or costs. On the other extent, short-term-optimal policies -- commonly based on deterministic waterfilling-- while strictly maintaining operational specifications, are not only impractical and computationally demanding, but also suboptimal in a long-term sense. To address these challenges, we introduce a novel distributionally robust version of a classical point-to-point interference-free multi-terminal constrained stochastic resource allocation problem, by leveraging the Conditional Value-at-Risk (CVaR) as a coherent measure of power policy fluctuation risk. We derive closed-form dual-parameterized expressions for the CVaR-optimal resource policy, along with corresponding optimal CVaR quantile levels by capitalizing on (sampling) the underlying fading distribution. We subsequently develop two dual-domain schemes -- one model-based and one model-free -- to iteratively determine a globally-optimal resource policy. Our numerical simulations confirm the remarkable effectiveness of the proposed approach, also revealing an almost-constant character of the CVaR-optimal policy and at rather minimal ergodic rate optimality loss.

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