Potential Technology Models for Active-Passive Coexistence

Due to their extreme sensitivity and ability to quantify thermal variations at the 0.1K level or better, the needs of passive Earth Exploration Satellite Service (EESS) sensors are typically 20–30dB more demanding from the standpoint of RFI than are most communication systems. In addition, the spectral bands needed for weather and climate sensing are tied to the quantum mechanical resonances and thermal emission spectral features of the atmospheric and surface, and are not substitutable. Due to advances in microwave receiver and calibration hardware along with data assimilation techniques the largest relative impact on the accuracy of numerical weather forecasts now originates from satellite microwave radiometers, which provide key information to keep numerical weather prediction models properly initialized. These data needs must be met on a global basis for the purposes of severe weather detection and prediction of both weather and climate. Global and national economies and lives ever more crucially depend on these forecasts, particularly in the current era of rapid and dangerous climate change. As a result, spectral coexistance between passive EESS sensors and the burgeoning number of active telecommunications systems operating near the necessary EESS bands is of ever greater importance. This workshop presentation summarizes the contemporary spectral needs of passive EESS sensors along with potential technology solutions for ensuing that out-of-band (OOB) radio frequency interference (RFI) from active systems are precluded. Solutions are based on the 7-dimensional division of “electrospace” into manifolds that are rapidly and dynamically allocated by a broker and provide a suitable margin of RFI reduction into EESS sensors while simultaeously permitting nearly negligible reductions in active user channel capacity. To be effective these solutions require mutual adherance to transmit/no-transmit decisions provided by a real-time spectral broker capable of predicting RFI, and feedback from all users on channel usage success to improve both predictability and system performance. Enabling EESS technology includes real-time broadband spectrometry with high dynamic range and optimized RFI detection algorithms, along with feedback pathways to convey this information. Similarly important enabling technology includes the incorporation of digital beam steering and nulling algorithms into 5G base station hardware, real-time harmonic and OOB detection hardware for power amplifier spectral response adjustment, and a broker-based system for coordinating electrospace requests, assessing RFI predictions, and incorporating success/failure knowledge into allocations algorithms.