EAGER: ML-enabled early warning of blockage and beam transitions in mobile, hybrid sub-6GHz/mmWave systems

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The emerging generation of cellular communication, 5G, is expected to utilize frequencies in the range of tens to hundreds of GHz (mmWave) to overcome the bandwidth limitations inherent to 4G (sub-6 GHz) systems. However, mmWave signals do not propagate as far, are more susceptible to blocking by physical objects, and require more directional communication than 4G signals. Indeed, early deployments of commercial 5G mmWave networks in 2019 and 2020 have suffered from major coverage and penetration problems. This project evaluates the feasibility of a novel, potentially transformative approach to obtain an early warning of blockages and antenna beam transitions at mmWave (5G) using sub-6GHz (4G-like) observations. Suitability of machine learning (ML) for enabling this task is investigated. A realistic physics-based propagation model is enhanced to validate the proposed approach. The project is strengthened by the interdisciplinary PI team with combined expertise in communication theory, signal processing, and propagation physics. The proposed methods introduce innovations to advance the broader fields of 5G networks and mmWave propagation modeling. The insights of the proposed research will be integrated into courses and presentations to student organizations, and the outcomes published for professionals. A graduate student and undergraduate students will be trained in a diverse, multidisciplinary, and inclusive environment about vibrant wireless communications topics. Close collaboration with NSF BWAC and PAWR platforms will enhance the success of proposed research and outreach plans by dissemination of the research outcomes in the centers' events.This high-risk, high reward project develops novel digital signal processing (DSP) and ML solutions for solving resiliency problems in real-world mmWave deployments. These methods are suitable for hybrid communication systems, where the sub-6 GHz and mmWave bands are employed simultaneously. Using the Fresnel theory of diffraction and our accurate physics-based model, we have previously demonstrated that diffracted sub-6 GHz signals reach a specified received signal strength (RSS) threshold much earlier than mmWave signals. The latter property is exploited in this project to develop an early-warning method that forecasts blockage, beam direction, and other rapid changes in mmWave signals several to tens of milliseconds (several to hundreds of slots) ahead in mobile communications systems. The early-warning approach provides hybrid mobile communications systems with sufficient time to adapt the data rate, change the antenna direction, or perform a handover between the two frequencies or base stations before a significant change of the mmWave signal occurs. The early warning method relies solely on the physical properties of diffraction, not on measured environments or dense beams or users. It improves resilience of mobile mmWave networks to blockages and other rapid signal changes by continuously adapting to the environmental features, e.g., moving obstacles or small reflectors not captured by base-station siting, environmental mapping, and other previously proposed approaches. The early warning algorithm is trained and validated using an accurate spatiotemporal sub-6 GHz/mmWave hybrid channel model, which can provide a large set of physically realistic scenarios. Utilization of the physical model in this project will provide insights for collecting 'smart data' in future hybrid channel measurements using PAWR platform at NC State and online data repositories.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.