CAREER: Rethinking Dynamic Wireless Networks through the Lens of Riemannian Geometry

职业：通过黎曼几何的视角重新思考动态无线网络

• 批准号: 2144297
• 负责人: Ahmed Mohamed
• 金额: $ 50万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144297&HistoricalAwards=false
• 关键词: CAREER; Rethinking; Dynamic; Wireless; Networks

The current revolution of wireless systems aims to support massive connectivity along with emerging applications, such as connected vehicles and extended reality, which simultaneously require high data rate and low latency. Dynamic network architectures, which include moving relays and flying drones, can support such requirements thanks to their adaptive nature. Maximizing data rate in such relay-assisted networks can be accomplished via 1) designing environment-aware and relay-based beamforming codebooks, 2) managing wireless interference that is caused by or towards relays, and 3) adaptive placement of relays. Unlike existing solutions for these problems, this project starts with a transformative modeling of the underlying data structures of wireless channels and network topologies over curved surfaces, known as Riemannian manifolds. The novel design of wireless networks through the lens of Riemannian manifolds leads to developing low-latency structure-aware (i.e., “gray-box”) machine learning (ML) models and low-complexity optimized solutions for these three problems. This project serves the national interest by promoting the progress of foundational science of wireless communications and networks along with providing the research community nationwide with fast ML models and efficient optimization tools, which will have far-reaching impact on addressing many challenges in wireless networks and alike. Furthermore, this project equips engineering students nationwide with new mathematical tools and hands-on educational platforms that uniquely integrate Riemannian geometry into wireless networks. Multiple undergraduate and graduate students, including underrepresented minorities, are mentored through this project. Finally, this project gravitates high-school students, including students with disabilities such as autism spectrum disorder, towards STEM through active engagement in interdisciplinary activities of non-Euclidean geometry, engineering, and neuroscience. This project aims to lay the foundations of utilizing Riemannian-geometric tools, particularly, geometric machine learning (G-ML) and conic geometric optimization (CGO), to advance the knowledge of designing dynamic networks with low latency, low complexity, and high data rate. Such goal is accomplished through synergistic integration of three components, which span multiple network layers (i.e., PHY, MAC, NET), as follows. First, environment-aware beamforming codebooks are developed using low-latency unsupervised G-ML models, thanks to representing the covariance matrices of spatially-correlated wireless channels over Riemannian manifolds. Second, wireless link scheduling and resource allocation mechanisms are developed using low-latency supervised G-ML models, which are based on jointly modeling the Riemannian-geometric and temporal structures of interference-aware network topologies over Riemannian manifolds. Finally, locations of relays are optimized for maximizing the network flow rate using low-complexity CGO, given the representation of relay-enhanced network topologies over Riemannian manifolds.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.

当前的无线系统革命旨在支持大规模连接沿着新兴应用，例如连接车辆和延展实境，这些应用同时需要高数据速率和低延迟。动态网络架构，包括移动中继和无人机，可以支持这些要求，这要归功于它们的自适应性。在这样的中继辅助网络中最大化数据速率可以通过以下方式来实现：1）设计环境感知和基于中继的波束成形码本，2）管理由中继引起的或对中继的无线干扰，以及3）中继的自适应放置。与这些问题的现有解决方案不同，该项目首先对弯曲表面上的无线信道和网络拓扑的底层数据结构进行变革性建模，称为黎曼流形。通过黎曼流形的透镜的无线网络的新颖设计导致开发低延迟结构感知（即，“灰盒”）机器学习（ML）模型和低复杂度优化解决方案。该项目服务于国家利益，通过推动无线通信和网络基础科学的进步，沿着为全国研究界提供快速的ML模型和高效的优化工具，这将对解决无线网络等领域的许多挑战产生深远的影响。此外，该项目还为全国的工程专业学生提供了新的数学工具和实践教育平台，这些工具和平台将黎曼几何独特地集成到无线网络中。多名本科生和研究生，包括代表性不足的少数民族，通过这个项目得到指导。最后，该项目吸引高中生，包括自闭症谱系障碍等残疾学生，通过积极参与非欧几里德几何，工程和神经科学的跨学科活动，走向STEM。该项目旨在为利用黎曼几何工具，特别是几何机器学习（G-ML）和圆锥几何优化（CGO）奠定基础，以提高设计低延迟，低复杂度和高数据速率的动态网络的知识。这样的目标是通过跨越多个网络层的三个组件的协同集成来实现的（即，PHY、MAC、NET），如下所示。首先，环境感知的波束形成码本使用低延迟无监督G-ML模型来开发，这要归功于在黎曼流形上表示空间相关的无线信道的协方差矩阵。其次，无线链路调度和资源分配机制的开发使用低延迟监督G-ML模型，这是基于联合建模的黎曼几何和时间结构的干扰感知网络拓扑黎曼流形。最后，继电器的位置进行了优化，以最大限度地提高网络流量，使用低复杂性CGO，给出了在黎曼流形上的继电器增强网络拓扑的表示。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Learning Wireless Power Allocation Through Graph Convolutional Regression Networks Over Riemannian Manifolds

• DOI: 10.1109/tvt.2023.3325200
• 发表时间: 2024-03
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: R. Shelim;Ahmed S. Ibrahim

Wireless Link Scheduling Over Recurrent Riemannian Manifolds

• DOI: 10.1109/tvt.2022.3228212
• 发表时间: 2023-04
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: R. Shelim;A. Ibrahim

Geometric Machine Learning Over Riemannian Manifolds for Wireless Link Scheduling

• DOI: 10.1109/access.2022.3153324
• 发表时间: 2022
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: R. Shelim;A. Ibrahim

Relay Placement for Maximum Flow Rate via Learning and Optimization Over Riemannian Manifolds

• DOI: 10.1109/tmlcn.2023.3309772
• 发表时间: 2023
• 期刊: IEEE Transactions on Machine Learning in Communications and Networking
• 作者: Imtiaz Nasim;A. Ibrahim

Optimal Placement of Reconfigurable Intelligent Surfaces for Spectrum Coexistence With Radars

可重构智能表面的最佳放置以实现与雷达的频谱共存

• DOI: 10.1109/tvt.2022.3165391
• 发表时间: 2022
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Kafafy, Mai;Ibrahim, Ahmed S.;Ismail, Mahmoud H.
• 通讯作者: Ismail, Mahmoud H.