CIF: Small: Risk-Aware Resource Allocation for Robust Wireless Autonomy

无线自主网络系统（WANS）几乎无处不在我们周围，执行各种通常复杂且普遍的以数据为中心的操纵，例如感测、处理、学习和行动（即，决策）。示例包括现代无线通信网络（例如，基于5G/6 G、毫米波/太赫兹技术）、无人机群、移动的或机器人网络、无人机（UAV）、自动驾驶汽车和物联网（IoT）。虽然广域网及其应用为社会和经济增长带来了巨大的潜力，但此类系统的运行不仅需要高效并由实际可观察的数据驱动，而且还需要满足通常严格的设计规范。这些规范是由保持性能鲁棒性和弹性的需求引起的，这反过来又转化为延迟，可靠性，公平性和可信度保证。这样的标准和约束从根本上连接到与无线系统的操作相关联的固有风险;这些风险是由于由自然发生的现象引起的固有不确定性，例如无线介质的非平凡统计分散以及多个用户和设备的行为的随机性，通常具有复杂和异构的特征和目标。本计画提出一个新的无线系统系统风险感知资源分配原则方法框架，弥补遍历风险中性与极小极大保守性之间的操作差距。调查的重点不仅是制定和双域变分分析新的约束风险意识的资源分配问题，但也对理论的发展，以及有效的方法，基于模型的合成和无模型的强化学习的最佳风险意识的政策。预计这项工作将建立一个新的范例，在无线系统的资源allocation.Preliminary结果基本程式化的资源分配问题-简单的单用户功率约束速率最大化-证明了明显的优势，风险意识的政策对他们的遍历（即，风险中性）和极大极小对应。然而，在更现实和更有用的环境中获得最优的风险感知策略是不平凡的：在风险感知问题中，期望的作用是由更一般的泛函（称为风险度量）发挥的，对于这些泛函，期望的基本属性（如线性，同质性或塔属性）通常不存在。这种复杂性在约束优化设置中自然被放大。该项目集中在无线系统的资源分配约束的背景下，这样的风险意识的问题，并分为三个主要的推力：1）拉格朗日对偶在风险意识的资源分配，2）基于模型的数据驱动的综合风险意识的资源分配政策和3）无模型学习的风险意识的资源分配政策。首席研究员预计，该项目将有助于改善固有的挑战下的风险意识的设置，如存在的风险度量为基础的变分随机约束，无限维度的资源政策，随机服务的非凸性，和信道/系统模型的可用性，最终呈现风险意识的无线系统资源分配的智力和计算负担得起的任务。该项目还将涉及无线自治以外的几个领域，如金融、经济、能源和机器人技术，并可能引发通信、信息理论、统计和优化交叉领域的新发展。同时也激发了新的风险管理工具该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准。

Wireless autonomous networked systems (WANS) are virtually everywhere around us, performing all kinds of often complex and pervasive data-centric manipulations, such as sensing, processing, learning, and acting (i.e., decision-making). Examples include modern wireless communication networks (e.g., based on 5G/6G, mmWave/THz technologies), drone swarms, mobile or robotic networks, unmanned aerial vehicles (UAVs), self-driving cars, and the Internet of Things (IoT). While WANS and their applications present a high potential for societal and economic growth, the operation of such systems requires not only to be efficient and driven by actual, observable data but also to meet often strict design specifications. These specifications are induced by the need to maintain performance robustness and resilience, which, in turn, translates into latency, reliability, fairness, and trustworthiness guarantees. Such criteria and constraints are fundamentally connected to intrinsic risks associated with the operation of wireless systems; those risks are due to inherent uncertainties caused by naturally occurring phenomena such as nontrivial statistical dispersion of the wireless medium as well as randomness in the behavior of multiple users and devices, often with complex and heterogeneous features and objectives. This project puts forward a new principled methodological framework for systematic risk-aware resource allocation in wireless systems, bridging the operational gap between ergodic risk neutrality and minimax conservativeness. The investigation focuses not only on formulation and dual-domain variational analysis of new constrained risk-aware resource-allocation problems but also on the development of theory as well as efficient methods for both model-based synthesis and model-free reinforcement learning of optimal risk-aware policies. It is expected that this work will establish a new paradigm in wireless systems resource allocation.Preliminary results on basic stylized resource-allocation problems - as simple as single-user power-constrained rate maximization - demonstrate clear advantages of risk-aware policies against both their ergodic (i.e., risk-neutral) and minimax counterparts. However, obtaining optimal risk-aware policies in more realistic and useful settings is nontrivial: in risk-aware problems, the role of expectations is played by more general functionals, called risk measures, for which fundamental properties of expectation - such as linearity, homogeneity, or the tower property - are generally absent. Such complications are naturally amplified within a constrained optimization setup. This project concentrates on such risk-aware problems within the context of constrained resource allocation for wireless systems and is divided into three main thrusts: 1) Lagrangian duality in risk-aware resource allocation, 2) model-based data-driven synthesis of risk-aware resource-allocation policies and 3) model-free learning of risk-aware resource-allocation policies. The principal investigator anticipates that the project will be instrumental in ameliorating inherent challenges under the risk-aware setting, such as the presence of risk-measure-based variational stochastic constraints, infinite dimensionality of resource policies, nonconvexity of random services, and channel/system-model availability, ultimately rendering risk-aware wireless-system resource allocation an intellectually accessible and computationally affordable task. The project will also be relevant to several areas beyond wireless autonomy - such as finance, economics, energy, and robotics - and may trigger new developments in the intersection of communications, information theory, statistics, and optimization, as well as inspire new tools in risk-aware and constrained learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.