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

CIF：小型：具有风险意识的资源分配，实现强大的无线自治

• 批准号: 2242215
• 负责人: Dionysios Kalogerias
• 金额: $ 60万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242215&HistoricalAwards=false
• 关键词: CIF; Small; Risk; Aware; Resource

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.

无线自治网络系统 (WANS) 实际上无处不在，执行各种复杂且普遍的以数据为中心的操作，例如传感、处理、学习和行动（即决策）。例如，现代无线通信网络（例如基于 5G/6G、毫米波/太赫兹技术）、无人机群、移动或机器人网络、无人机 (UAV)、自动驾驶汽车和物联网 (IoT)。虽然广域网及其应用对社会和经济增长具有巨大潜力，但此类系统的运行不仅需要高效并由实际可观测数据驱动，而且还需要满足严格的设计规范。这些规范是由于需要保持性能稳健性和弹性而产生的，而这反过来又转化为延迟、可靠性、公平性和可信度保证。这些标准和限制从根本上与无线系统运行相关的内在风险有关；这些风险是由于自然发生的现象造成的固有不确定性造成的，例如无线介质的重要统计分散性以及多个用户和设备行为的随机性，通常具有复杂和异构的特征和目标。该项目提出了一种新的原则方法框架，用于无线系统中系统的风险感知资源分配，弥合了遍历风险中性和极小极大保守性之间的操作差距。该研究不仅关注新的受限风险感知资源分配问题的表述和双域变分分析，还关注基于模型的合成和无模型强化学习的最优风险的理论和有效方法的发展- 意识政策。预计这项工作将在无线系统资源分配中建立一个新的范例。基本的程式化资源分配问题的初步结果——就像单用户功率受限的速率最大化一样简单——证明了风险意识策略相对于它们的明显优势。遍历（即风险中性）和极小极大对应物。然而，在更现实和更有用的环境中获得最佳的风险意识策略并非易事：在风险意识问题中，期望的作用是由更一般的函数（称为风险度量）发挥的，其中期望的基本属性 - 例如线性、同质性，或塔楼财产 - 通常不存在。在受限的优化设置中，这种复杂性自然会被放大。该项目集中于无线系统受限资源分配背景下的此类风险意识问题，并分为三个主要重点：1）风险意识资源分配中的拉格朗日对偶性，2）基于模型的数据驱动的风险综合有意识的资源分配政策和3）无模型学习有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风险意识环境下的固有挑战，例如基于风险测量的变分随机约束的存在、资源策略的无限维、随机服务的非凸性以及通道/系统-模型可用性，最终使具有风险意识的无线系统资源分配成为一项智力上可访问且计算上负担得起的任务。该项目还将涉及无线自治之外的多个领域，例如金融、经济、能源和机器人技术，并可能引发通信、信息论、统计和优化交叉领域的新发展，并激发新工具的使用。风险意识和有限学习。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。