Spectrum Sharing Dynamic Protection Area Neighborhoods for Radio Astronomy

To enforce incumbent protection through a spectrum access system (SAS) or future centralized shared spectrum system, dynamic protection area (DPA) neighborhood distances are employed. These distances are distance radii, in which citizen broadband radio service devices (CBSDs) are considered as potential interferers for the incumbent spectrum users. The goal of this paper is to create an algorithm to define DPA neighborhood distances for radio astronomy (RA) facilities with the intent to incorporate those distances into existing SASs and to adopt for future frameworks to increase national spectrum sharing. This paper first describes an algorithm to calculate sufficient neighborhood distances. Verifying this algorithm by recalculating previously calculated and currently used neighborhood distances for existing DPAs then proves its viability for extension to radio astronomy facilities. Applying the algorithm to the Hat Creek Radio Observatory (HCRO) with customized parameters results in distance recommendations, 112 kilometers for category A (devices with 30 dBm/10 MHz max EIRP) and 144 kilometers for category B (devices with 47 dBm/10MHz max EIRP), for HCRO's inclusion into a SAS and shows that the algorithm can be applied to RA facilities in general. Calculating these distances identifies currently used but likely out-of-date metrics and assumptions that should be revisited for the benefit of spectrum sharing.Comment: 6 pages, 5 figures, 5 tables, published to WCNC 202

An Interference Analysis Framework (IAF) for Spectrum Sharing Feasibility and Coexistence Determination

Due to recent increases in mobile broadband services by consumers, the Federal Communications Commission (FCC) is compelled to make more spectrum available for commercial use. The FCC spectrum-service rulemaking process does not formally assess risk of interference, apply statistical distributions, or recommend coexistence testing. The methodology developed in this dissertation proposes a framework that applies risk, statistics, and coexistence testing and demonstrates how spectrum regulatory decision-making processes would benefit from using a more technically defensible approach. Allowing new systems into a spectrum band can cause interference to the operating incumbent users but preventing new commercial spectrum systems can have negative economic and technological repercussions. Therefore, carefully assessing the effects of a more heavily used spectrum by treating analytical spectrum coexistence testing metrics as distributions can lead to more information-based, data-driven decision making. When using risk assessments, regulators’ decisions will improve to include results from well-engineered and transparent spectrum coexistence testing accounting for measurement uncertainties with translations to real-world deployments by accounting for all applicable variabilities

The Modular S-Band Radio Suite

Radios are a necessity for every spacecraft mission, yet there is no existing family of radios specifically designed for nanosatellite-class spacecraft. Conventional spacecraft radios typically try to address a wide range of missions with a single design. As a result, they often tend toward higher complexity, mass, power consumption and cost, making them less than ideal for use in small spacecraft, where mass, volume and power constraints are typically severe. AeroAstro’s Modular S-Band Radio Suite (MSBRS) is intended to specifically address this otherwise neglected market. The MSBRS is a set of very small, inexpensive radio modules designed to enhance cost, volume, and mass efficiencies for 1 to 30 kilogram nanosatellites. AeroAstro is developing the MSBRS for nanosatellites using a cost-effective mixture of commercial-off-the-shelf components with proven design methods and innovative satellite engineering. Through its modular design, the MSBRS provides significantly more flexibility to tailor the radio to the needs of each particular mission. For example, if a mission needs more transmitted power to meet its link budget, then the appropriate high-power amplifier module is incorporated into the “stack” of modules comprising the complete radio. MSBRS provides only what the mission requires and doesn\u27t burden the spacecraft with unnecessary and unused capabilities

Passive and Active Spectrum Sharing (PASS)

PASS is supported by the National Science Foundation (see NSF Award no. ECCS-2030233). This page provides access to the noise-floor survey SigMF[1][2] data sets generated as part of the project. The University of Colorado Boulder (CU) Passive and Active Spectrum Sharing (PASS) project directly addresses the related problems of protecting passive users while enabling secure, dynamic spectrum sharing between passive and active systems. The PASS project is an interdisciplinary collaboration with deep expertise in spectrum science, spectrum sharing, wireless systems, and system security. Radio frequency (RF) spectrum has become a scarce resource. The Passive and Active Spectrum Sharing (PASS) project will systematically survey and characterize RF noise, and explore and evaluate alternatives for enabling spectrum sharing between passive and active systems. The PASS research will be a significant step forward in understanding and mitigating RF noise, and will enable more dynamic and efficient sharing of spectrum. Our purpose behind a data standard is to enable data sharing and collaboration. Along with developing the standard and gathering data on RF noise in various environments, the PASS project will develop a prototype database for storing and retrieving RF data. Several sites exist which provide SigMF-based RF data for download. For example, researchers at the UC Berkeley SETI Institute have made available SigMF files from observations made with the Allen Telescope Array, an array of 42 dish antennas at the Hat Creek Observatory in California[3]. Additional datasets are available from Breakthrough Listen observations using the Green Bank Telescope. Deepsig.io has datasets in multiple formats, including hdf5, and Python &lsquo;pickle&rsquo; files [4]. Click&nbsp;here&nbsp;to access PASS Baseline Noise Survey Datasets. [1] SigMF:&nbsp;The Signal Metadata Format Specification [2] GNURadio:&nbsp;The Free and Open Software Radio Ecosystem [3] SETI Institute: SETI Institute [4] DeepSig, Inc.:&nbsp;RF Datasets for Machine Learning</p