Distributionally Robust Power Policies for Wireless Systems under Power Fluctuation Risk

TL;DR

This paper introduces a distributionally robust resource allocation method for wireless systems that minimizes power fluctuation risk using CVaR, ensuring operational reliability with minimal performance loss.

Contribution

It develops a novel CVaR-based robust power policy framework with closed-form solutions and dual-domain algorithms for practical implementation in wireless systems.

Findings

CVaR-optimal policies are nearly constant across fading conditions.

The proposed methods outperform traditional policies in fluctuation risk management.

Minimal ergodic rate loss observed with the new approach.

Abstract

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.