Cellular Wireless Networks in the Upper Mid-Band

The upper mid-band -- roughly from 7 to 24 GHz -- has attracted considerable recent interest for new cellular services. This frequency range has vastly more spectrum than the highly congested bands below 7 GHz while offering more favorable propagation and coverage than the millimeter wave (mmWave) frequencies. Realizing the full potential of these bands, however, will require fundamental changes to the design of cellular systems. Most importantly, spectrum will likely need to be shared with incumbents including communication satellites, military RADAR, and radio astronomy. Also, due to the wide bandwidth, directional nature of transmission, and intermittent occupancy of incumbents, cellular systems will need to be agile to sense and intelligently use large spatial and bandwidth degrees of freedom. This paper attempts to provide an initial assessment of the feasibility and potential gains of wideband cellular systems operating in the upper mid-band. The study includes: (1) a system study to assess potential gains of multi-band systems in a representative dense urban environment; (2) propagation calculations to assess potential cross interference between satellites and terrestrial cellular services; and (3) design and evaluation of a compact multi-band antenna array structure. Leveraging these preliminary results, we identify potential future research directions to realize next-generation systems in these frequencies.

Collaboration with Cellular Networks for RFI Cancellation at Radio Telescope

The growing need for electromagnetic spectrum to support the next generation (xG) communication networks increasingly generate unwanted radio frequency interference (RFI) in protected bands for radio astronomy. RFI is commonly mitigated at the Radio Telescope without any active collaboration with the interfering sources. In this work, we provide a method of signal characterization and its use in subsequent cancellation, that uses Eigenspaces derived from the telescope and the transmitter signals. This is different from conventional time-frequency domain analysis, which is limited to fixed characterizations (e.g., complex exponential in Fourier methods) that cannot adapt to the changing statistics (e.g., autocorrelation) of the RFI, typically observed in communication systems. We have presented effectiveness of this collaborative method using real-world astronomical signals and practical simulated LTE signals (downlink and uplink) as source of RFI along with propagation conditions based on preset benchmarks and standards. Through our analysis and simulation using these signals, we are able to remove 89.04% of the RFI from cellular networks, which reduces excision at the Telescope and capable of significantly improving throughput as corrupted time frequency bins data becomes usable.

Machine Learning in Heliophysics and Space Weather Forecasting: A White Paper of Findings and Recommendations

The authors of this white paper met on 16-17 January 2020 at the New Jersey Institute of Technology, Newark, NJ, for a 2-day workshop that brought together a group of heliophysicists, data providers, expert modelers, and computer/data scientists. Their objective was to discuss critical developments and prospects of the application of machine and/or deep learning techniques for data analysis, modeling and forecasting in Heliophysics, and to shape a strategy for further developments in the field. The workshop combined a set of plenary sessions featuring invited introductory talks interleaved with a set of open discussion sessions. The outcome of the discussion is encapsulated in this white paper that also features a top-level list of recommendations agreed by participants.