GOALI: CNS: Medium: Communication-Computation Co-Design for Rural Connectivtiy and Intelligence under Nonuniformity: Modeling, Analysis, and Implementation

目标：CNS：媒介：非均匀性下农村互联和智能的通信计算协同设计：建模、分析和实现

• 批准号: 2212565
• 负责人: Taejoon Kim
• 金额: $ 100万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2212565&HistoricalAwards=false
• 关键词: GOALI; CNS; Medium; Communication; Computation

Urban societies throughout the world have come to depend on access to reliable and high-speed wireless, as well as the intelligent data-driven services they can deliver to end users. Low population densities and sparse infrastructure in rural areas, however, make them less attractive for investment in network connectivity. Fifth generation (5G)’s support for vertical industry integration has further magnified the broadband access inequalities between urban and rural environments. Technologies that have been proposed to provide rural broadband access are not designed to address the non-uniformity in demand for communication and computation resources that are found throughout these environments; for example, many automated farming services will have stringent delay requirements for short periods during the day while residential areas exhibit traditional diurnal patterns. This project aims to address this challenge by establishing a core set of methodologies that synergize the design of rural connectivity and computing. Emerging massive multiple-input multiple-output (MIMO) technology is integrated with the design of hierarchical data processing architectures across the rural infrastructure to adapt based on spatial and temporal demand variations. Extensive evaluations are conducted based on a massive MIMO platform and wireless device equipment available from the industrial partner. The project contributes to the development of a diverse workforce in rural broadband and intelligence through the formation of research teams integrating undergraduate and graduate students across institutions.Project research activities are organized into three thrusts. Thrust 1 develops agile communication techniques under non-uniform rural settings, addressing the needs for multi-user management and resource allocation for massive MIMO infrastructure. This includes the development of novel network control algorithms with scheduling policies that are customized for heterogeneous rural connectivity needs. The developed algorithms are further enhanced by reducing scheduling overhead through position-aided channel representation techniques. Thrust 2 investigates the orchestration of computation tasks under rural non-uniformity, focusing on establishing intelligent rural data processing architectures leveraging the broadband connectivity provided by Thrust 1. Techniques for distributing data processing among clusters of rural devices aiming to execute collaborative tasks rapidly are developed via joint optimization of device-to-device communications and task utility metrics. The orchestration of data processing and intelligence synchronization steps along the rural network hierarchy is also considered to adapt based on task demand variations. Thrust 3 consists of proof-of-concept implementation and testing of the methodologies in Thrust 1&2. This employs a testbed developed leveraging a commercial-grade hybrid massive MIMO base station system engineered by the industrial partner. This project is jointly funded by CNS and the Established Program to Stimulate Competitive Research (EPSCoR).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.

世界各地的城市社会已经开始依赖于可靠和高速的无线接入，以及它们可以向最终用户提供的智能数据驱动服务。然而，农村地区人口密度低，基础设施稀疏，对网络连接投资的吸引力较小。第五代（5G）对垂直行业整合的支持进一步放大了城乡环境之间的宽带接入不平等。已经提出的用于提供农村宽带接入的技术并没有被设计为解决在这些环境中发现的对通信和计算资源的需求的不一致性;例如，许多自动化农业服务将在白天的短时间内具有严格的延迟要求，而住宅区则表现出传统的昼夜模式。该项目旨在通过建立一套核心方法来应对这一挑战，这些方法可使农村连接和计算的设计产生协同作用。新兴的大规模多输入多输出（MIMO）技术与整个农村基础设施的分层数据处理架构的设计相集成，以根据空间和时间需求变化进行适应。基于工业合作伙伴提供的大规模MIMO平台和无线设备设备进行了广泛的评估。该项目通过组建由各机构本科生和研究生组成的研究团队，促进农村宽带和智能领域多样化劳动力的发展。Thrust 1开发了非均匀农村环境下的敏捷通信技术，解决了大规模MIMO基础设施的多用户管理和资源分配需求。这包括开发新的网络控制算法与调度策略，定制异构农村连接的需求。通过位置辅助信道表示技术减少调度开销，进一步增强了所开发的算法。Thrust 2研究了农村非均匀性下计算任务的编排，重点是利用Thrust 1提供的宽带连接建立智能农村数据处理架构。通过设备到设备通信和任务效用度量的联合优化，开发了用于在旨在快速执行协作任务的农村设备集群之间分配数据处理的技术。数据处理和智能同步步骤的编排沿着农村网络层级也被认为是基于任务需求变化来适应的。推力3包括推力1和推力2中的方法的概念验证实施和测试。这采用了利用工业合作伙伴设计的商业级混合大规模MIMO基站系统开发的测试平台。该项目由CNS和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Time-Varying Noise Perturbation and Power Control for Differential-Privacy-Preserving Wireless Federated Learning

• DOI: 10.1109/ieeeconf59524.2023.10476780
• 发表时间: 2023-10
• 期刊: 2023 57th Asilomar Conference on Signals, Systems, and Computers
• 作者: Dang Qua Nguyen;Taejoon Kim

Optimal Single-Bit Relaying Strategies With Multi-Relay Diversity

具有多中继分集的最佳单比特中继策略

• DOI: 10.1109/tit.2023.3292356
• 发表时间: 2023
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Bliss, Matthew;Wang, Chih-Chun;Love, David J.
• 通讯作者: Love, David J.

Linear Coding for Gaussian Two-Way Channels

高斯双向通道的线性编码

• DOI: 10.1109/allerton49937.2022.9929416
• 发表时间: 2022
• 期刊: and Computing (Allerton
• 作者: Kim, Junghoon;Hosseinalipour, Seyyedali;Kim, Taejoon;Love, David J.;Brinton, Christopher G.
• 通讯作者: Brinton, Christopher G.

Dynamic and Robust Sensor Selection Strategies for Wireless Positioning With TOA/RSS Measurement

用于 TOA/RSS 测​​量无线定位的动态且鲁棒的传感器选择策略

• DOI: 10.1109/tvt.2023.3279833
• 发表时间: 2023
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Oh, Myeung Suk;Hosseinalipour, Seyyedali;Kim, Taejoon;Love, David J.;Krogmeier, James V.;Brinton, Christopher G.
• 通讯作者: Brinton, Christopher G.

On the Optimal Delay Growth Rate of Multi-Hop Line Networks: Asymptotically Delay-Optimal Designs and the Corresponding Error Exponents

• DOI: 10.1109/tit.2023.3283802
• 发表时间: 2023-10
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Dennis Ogbe;Chih-Chun Wang;D. Love