RINGS:Deep Generative Models for Ultra High-Dimensional Next Generation Communication Systems

RINGS：超高维下一代通信系统的深度生成模型

• 批准号: 2148141
• 负责人: Jeffrey Andrews
• 金额: $ 73.24万
• 依托单位: University of Texas at Austin
• 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: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148141&HistoricalAwards=false
• 关键词: RINGS; Deep; Generative; Models; Ultra

Wireless communications applications of the 2030s --- such as extended reality, autonomous vehicles, and telemedicine --- will require extremely high bit rates and reduced energy consumption. To fulfill these requirements, sixth-generation (6G) era systems will leverage ultra-high carrier frequencies enabled by deploying massive arrays of very small antennas. Such architectures are termed ultra-high dimensional (UHD) communication systems. This is a very challenging scenario for wireless communication, since the UHD channel is difficult to learn and the wireless energy must be radiated in a highly directional manner towards the intended receiver. Existing approaches to channel estimation and beam alignment will not scale to the UHD scenario in terms of complexity, power consumption, or overhead signaling. This project aims to develop novel, scalable, and high-performing approaches to channel estimation and beam alignment for UHD communication systems using deep generative models (DGMs), an emerging toolset from unsupervised deep learning. This project will advance the fundamentals of DGMs in a manner appropriate for decentralized real-time applications, and also develop these novel methods for the unique challenges of UHD communication systems.The project will develop novel theoretical tools for solving challenging inverse problems arising in UHD communication systems, in particular considering (i) resilience to measurement errors and outliers; (ii) resilience to shifting channel distributions and low-resolution measurements; (iii) adaptation to changes in data distribution via federated generative adversarial network (GAN) training; (iv) compressed GAN representations; and (v) incorporating prior information in untrained DGMs via learned regularization. The project will also research DGM-based (i) interference-robust channel estimation and GAN-training techniques; (ii) federated downlink UHD channel estimation paradigms; (iii) end-to-end UHD channel estimators that account for physical impairments such as low-resolution quantization and RF nonlinearity; (iv) low-latency beam alignment using compressed DGM-based channel representations; and (v) joint channel estimation and beam alignment using DGM-based site-specific codebooks. The project will also build novel simulators and experiments to accurately characterize UHD channels in the context of 6G cellular systems, in collaboration with the RINGS industry partners, to benefit the wider 6G ecosystem.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.

21世纪30年代的无线通信应用，如扩展现实、自动驾驶汽车和远程医疗，将需要极高的比特率和更低的能耗。为了满足这些要求，第六代（6G）时代的系统将利用超高载波频率，通过部署大量非常小的天线阵列来实现。这种架构被称为超高维（UHD）通信系统。对于无线通信来说，这是一个非常具有挑战性的场景，因为超高清信道很难学习，而且无线能量必须以高度定向的方式辐射到预期的接收器。现有的信道估计和波束对准方法在复杂性、功耗或开销信号方面无法扩展到UHD场景。该项目旨在使用深度生成模型（dgm）为超高清通信系统开发新颖、可扩展和高性能的信道估计和波束对准方法，这是一种来自无监督深度学习的新兴工具集。该项目将以适合分散实时应用的方式推进dgm的基础，并为超高清通信系统的独特挑战开发这些新方法。该项目将开发新的理论工具，以解决超高清通信系统中出现的具有挑战性的反向问题，特别是考虑到(i)测量误差和异常值的弹性；（ii）对移动信道分布和低分辨率测量的适应能力；（iii）通过联邦生成对抗网络（GAN）训练来适应数据分布的变化；（iv）压缩的GAN表示；(v)通过学习正则化将先验信息纳入未经训练的dgm。该项目还将研究基于dgm的(i)抗干扰信道估计和gan训练技术；（ii）联合下行链路UHD信道估计范式；（iii）端到端超高清信道估计器，考虑到物理缺陷，如低分辨率量化和射频非线性；（iv）使用基于压缩dgm的信道表示进行低延迟波束对齐；(v)使用基于dgm的特定站点码本进行联合信道估计和波束对准。该项目还将与RINGS行业合作伙伴合作，建立新的模拟器和实验，以准确表征6G蜂窝系统背景下的超高清信道，从而使更广泛的6G生态系统受益。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。