NSF-AoF: SOLID: System-wide Operation via Learning In-device Dissimilarities

蜂窝通信系统继续采用新的多天线技术。特别是，第三代、第四代和第五代蜂窝系统在基站基础设施和设备中使用多天线方面取得了进步。这些天线的一个主要应用是支持多输入多输出（MIMO）通信，众所周知，这可以提高频谱效率，从而提高设备在给定带宽下可以实现的数据速率。即使来自同一制造商，天线的数量和使用方式也可能因设备型号而异。与此同时，蜂窝系统支持的设备类型正在增长，不仅限于智能手机，还包括其他高度移动的平台，如飞行器、汽车和机器人。设备之间硬件的差异，加上设备的高移动性，使得配置天线以提供最高性能的MIMO通信具有挑战性。该项目开发了受机器学习启发的解决方案，使设备能够协同学习最佳配置。通过学习设备内差异进行全系统操作是北卡罗莱纳州立大学（NC State）和坦佩雷大学（TAU）无线通信专家之间的合作项目。该提案的总体目标是在大规模动态系统中采用机器学习辅助的协同解决方案进行MIMO波束预测和码本优化。这种网络的主要挑战是设备硬件的极端多样性（例如，天线设计和配置）。现有的分布式机器学习方法没有明确地包括这种类型的客户端异构，也没有完全支持数据、网络资源和部署的时间和空间异构。该项目团队将开发一种新颖的集成学习和无线网络框架，该框架将能够设计和优化先进的MIMO波束管理解决方案，专门为高度多样化和动态的系统量身定制。该项目将为5G+/pre-6G MIMO通信在具有高度移动和异构代理的非固定环境中的协作设备中心波束管理提供新算法。具体的技术贡献出现在几个方向上：(a)以用户为中心的分布式学习，以优化基于码本的MIMO通信；(b)以机器学习友好的方式新颖地表示设备异质性；(c)优化网络资源，促进分布式学习。直接影响将是5G+/pre-6G网络中通信效率的提高。长期影响将是建立核心原则，用于设计在具有各种硬件和资源的无线系统上部署的快速可靠的分布式机器学习训练方法。该奖项反映了美国国家科学基金会的法定使命，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。

Cellular communication systems continue to incorporate new multiple-antenna technologies. In particular, third, fourth and fifth generation cellular systems saw advancements in the use of multiple antennas at the base-station infrastructure and multiple antennas in the devices. A main application of these antennas was to support multiple-input multiple-output (MIMO) communication, which is known to increase spectral efficiency and thus the data rates that can be achieved by devices in a given bandwidth. The numbers of antennas and the ways the antennas are used can vary across device models even from the same manufacturer. At the same time, the types of devices supported in cellular systems is growing beyond smartphones to include other highly mobile platforms like aerial vehicles, automobiles, and robots. The differences in the hardware between devices, coupled with the high device mobility, makes it challenging to configure the antennas to provide MIMO communication with the highest performance. This project develops machine learning-inspired solutions to empower devices to learn optimal configurations collaboratively. System-wide Operation via Learning In-device Dissimilarities is a cooperation among experts in wireless communications at North Carolina State University (NC State) and Tampere University (TAU). The overall objective of the proposal is to employ machine-learning-assisted collaborative solutions for MIMO beam prediction and codebook optimization in a large-scale dynamic system. The key challenge of such networks is the extreme diversity of the devices’ hardware (e.g., antenna designs and configurations). The existing distributed ML approaches do not explicitly include this type of client heterogeneity and do not fully support the temporal and spatial heterogeneity of data, network resources, and deployments. The project team will develop a novel integrated-learning and wireless-networking framework, which will enable the design and optimization of advanced MIMO beam-management solutions specifically tailored to the highly diverse and dynamic system. This project will result in new algorithms for collaborative device-centric beam management for 5G+/pre-6G MIMO communications in non-stationary environments with highly mobile and heterogeneous agents. The specific technical contributions occur in several directions: (a) Distributed user-centric learning for optimizing codebook-based MIMO communications; (b) Novel representation of device heterogeneity in an ML-friendly way; and (c) Network-resource optimization to facilitate distributed learning. The immediate impact will be improved communication efficiency in 5G+/pre-6G networks. The longer-term impact will be the establishment of the core principles for designing fast and reliable methods of distributed ML training deployed over wireless systems with diverse hardware and resources.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.