Stochastic allocation of resources in the context of wireless systems ultimately demands reactive decision making for meaningfully optimizing network-wide random utilities, while respecting certain resource constraints. Standard ergodic-optimal policies are however susceptible to the statistical variability of fading, often leading to systems which are severely unreliable and spectrally wasteful. On the flip side, minimax/outage-optimal policies are too pessimistic and often hard to determine. We propose a new risk-aware formulation of the resource allocation problem for standard multi-user point-to-point power-constrained communication with no cross-interference, by employing the Conditional Value-at-Risk (CV@R) as a measure of fading risk. A remarkable feature of this approach is that it is a convex generalization of the ergodic setting while inducing robustness and reliability in a fully tunable way, thus bridging the gap between the (naive) ergodic and (conservative) minimax approaches. We provide a closed-form expression for the CV@R-optimal policy given primal/dual variables, extending the classical stochastic waterfilling policy. We then develop a primal-dual tail-waterfilling scheme to recursively learn a globally optimal risk-aware policy. The effectiveness of the approach is verified via detailed simulations.

翻译：在无线系统背景下的随机资源分配，最终需要反应性决策，以便在满足特定资源约束的同时，有意义地优化网络范围的随机效用。然而，标准的遍历最优策略易受信道衰落统计变异性的影响，常常导致系统严重不可靠且频谱利用效率低下。另一方面，最小最大/中断最优策略过于悲观且往往难以确定。本文提出一种新的风险感知资源分配公式，用于标准无交叉干扰的多用户点对点功率受限通信，采用条件风险价值（Conditional Value-at-Risk, CV@R）作为衰落风险的度量。该方法的一个显著特点是：它在完全可调的方式下，作为遍历设置的一种凸推广，同时引入了稳健性和可靠性，从而弥合了（天真的）遍历方法与（保守的）最小最大方法之间的差距。我们给出了给定原始/对偶变量条件下CV@R最优策略的闭式表达式，扩展了经典的随机注水策略。然后，我们开发了一种原始-对偶尾部注水方案，以递归方式学习全局最优的风险感知策略。通过详细仿真验证了该方法的有效性。