Collaborative Research: NSF-AoF: CNS Core: Small: Towards Scalable and Al-based Solutions for Beyond-5G Radio Access Networks

合作研究：NSF-AoF：CNS 核心：小型：面向超 5G 无线接入网络的可扩展和基于人工智能的解决方案

• 批准号: 2225578
• 负责人: David Love
• 金额: $ 21.5万
• 依托单位: Purdue 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: 2023-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225578&HistoricalAwards=false
• 关键词: Collaborative; Research; NSF; AoF; CNS

Over the last few years, discussions oriented toward defining sixth generation (6G) requirements and possible technologies have started to circulate within the wireless community. One of the key ideas will likely be to take steps to remove the conventional cell boundaries and facilitate enhanced joint uplink and downlink processing using many dispersed access points (APs). These ideas fall within the academic definition of cell-free massive multiple-input multiple-output (CFmMIMO). It alleviates the existing cell-edge and handover problems and improves energy efficiency. The primary limiting factor is achieving cell-free operation in a practically feasible way, with computational complexity and fronthaul requirements that are scalable to large networks with many users. This poses many important research questions that must be explored systematically and in-depth. This project firstly develops scalable artificial intelligence (AI)-based solutions. Together with the appropriate (cost-efficient) AP deployment planning tools (e.g., where to put the APs), these developments constitute a significant step toward enabling the low-latency and uniformly reliable wireless services at a lower cost. Given the international nature of the project, the project contributes to the development of a diverse workforce in AI and 6G wireless networks through the formation of international research teams integrating undergraduate and graduate students. Project research activities are organized into three thrusts. Thrust 1 develops scalable AI-based resource allocation solutions enabling the implementation of large-scale CFmMIMO. The developed solutions are further enhanced by exploring AI architectures applicable to large networks. This includes the security aspects, especially in the context of AI algorithms and architecture, and the cloud radio access network. Thrust 2 focuses on establishing network planning and waveform constraints to address scalable deployment solutions. This includes the development of infrastructure-aware minimum-cost AP deployment methodologies by taking into account the QoS requirements and available transport infrastructure. The developed methodologies are further augmented by developing a network-wide user signal detection method, accounting for the fronthaul capacity and the quantization resolution at each AP. This task also investigates how CFmMIMO can address many of today’s most challenging spectrum policy issues. Evaluation Thrust evaluates and analyzes the methodologies developed in Thrusts 1&2. This employs the existing US and European experimental testbeds and provides a continuous feedback cycle between theory and experimentation. The US team will build upon the prior experience with Colosseum. On the European side, the team will experiment with the Open Air Interface (OAI).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.

在过去的几年中，面向定义第六代（6G）需求和可能的技术的讨论已经开始在无线社区中传播。关键思想之一将可能是采取步骤来去除传统的小区边界，并使用许多分散的接入点（AP）来促进增强的联合上行链路和下行链路处理。这些想法属于无小区大规模多输入多输出（CFmMIMO）的学术定义。它解决了现有的小区边缘和切换问题，提高了能量效率。主要限制因素是以实际可行的方式实现无小区操作，具有可扩展到具有许多用户的大型网络的计算复杂性和前传要求。这就提出了许多重要的研究问题，必须进行系统和深入的探讨。该项目首先开发基于可扩展人工智能（AI）的解决方案。与适当的（具有成本效益的）AP部署规划工具（例如，AP的位置），这些发展构成了以较低成本实现低延迟和一致可靠的无线服务的重要一步。鉴于该项目的国际性质，该项目通过组建整合本科生和研究生的国际研究团队，为人工智能和6G无线网络的多元化劳动力的发展做出了贡献。项目研究活动分为三个重点。Thrust 1开发可扩展的基于AI的资源分配解决方案，实现大规模CFmMIMO。通过探索适用于大型网络的AI架构，进一步增强了开发的解决方案。这包括安全方面，特别是在人工智能算法和架构以及云无线电接入网络的背景下。第二个重点是建立网络规划和波形约束，以解决可扩展的部署解决方案。这包括通过考虑QoS要求和可用的传输基础设施来开发基础设施感知的最低成本AP部署方法。所开发的方法通过开发网络范围的用户信号检测方法来进一步增强，考虑到每个AP处的前传容量和量化分辨率。该任务还研究了CFmMIMO如何解决当今许多最具挑战性的频谱政策问题。评估推力评估和分析的推力1 2开发的方法。这采用了现有的美国和欧洲的实验测试平台，并提供了理论和实验之间的连续反馈周期。美国队将建立在以前的经验与斗兽场。在欧洲方面，该团队将试验开放式空气接口（OAI）。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

Defending Adversarial Attacks on Deep Learning-Based Power Allocation in Massive MIMO Using Denoising Autoencoders

• DOI: 10.1109/tccn.2023.3261307
• 发表时间: 2022-11
• 期刊: IEEE Transactions on Cognitive Communications and Networking
• 影响因子: 8.6
• 作者: R. Sahay;M. Zhang;D. Love;Christopher G. Brinton

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.

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.

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

The Capacity of Channels with O(1)-Bit Feedback

O(1) 位反馈的通道容量

• DOI: 10.1109/isit54713.2023.10206714
• 发表时间: 2023
• 期刊: Proceedings of the 2023 IEEE International Symposium on Information Theory
• 作者: Ruzomberka, Eric;Jang, Yongkyu;Love, David J.;Poor, H. Vincent
• 通讯作者: Poor, H. Vincent