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

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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.