Automated RF Interference Mitigation System
communications
Automated RF Interference Mitigation System (LEW-TOPS-166)
Enabling robust space-to-ground communications
Overview
Space-to-ground radio frequency (RF) communications transfer critical data that enables successful satellite and spacecraft operations. Such communications are susceptible to co-channel interference produced by terrestrial or space-based sources, and to interfering signals from shared spectrum scenarios (which become more common as space activity increases). If radio frequency interference (RFI) could be detected and mitigated in an automated fashion, link performance and reliability could be improved.
After observing unexpected interference events during space-to-ground communications, innovators from the NASA Glenn Research Center (GRC) developed a software-based automated RFI mitigation system to increase communication reliability. The system uses Cyclic Auto-Correlation signal processing techniques to monitor the spectrum and detect interfering signals. The innovation also applies a multi-objective optimization approach to mitigate interference by changing link parameters for continuous optimization.
The Technology
Innovators at NASA GRC have designed, developed, and prototyped a software-based, automated, RFI mitigation system to lessen the impact of interfering radio signals on satellite-to-ground communication links. This invention detects interfering RF signals using signal processing techniques and intelligently mitigates the interference by automatically changing communication link parameters. The system continuously optimizes the link to improve data-rate transfer and link reliability in a dynamic environment.
The interference mitigation system is composed of several algorithms that allow it to detect and respond to interference and optimize the link based on a given set of mission objective weights. The system runs in real-time, continuously ingesting link information from the receiving modem and spectrum information from the monitoring subsystem. This information then feeds into an Adaptive Coding and Modulation (ACM) loop and a mitigation subsystem. The ACM loop uses algorithms to decide if mitigation should take place and, if so, what mitigation actions are available to consider. These actions are then ranked using a multi-objective, weighted-sum algorithm and the best-ranked action is chosen and applied to the link by the link controller.
During testing at NASA, the RFI mitigation system was shown to achieve the highest average throughput over all test cases compared to alternative mitigation strategies. Thus, companies that have experienced degraded space-to-ground communications performance due to RF interference may find significant value in the invention.
Benefits
- Improved communications link: NASA's RFI mitigation system enables space-to-ground communications to achieve higher throughput and resilience during RFI events.
- Maturity: The software system has been prototyped, tested, and characterized for various communication channel impairments.
- High throughput: During testing, the RFI mitigation system was shown to achieve, on average, the highest throughput during RFI events relative to all other evaluated strategies.
- Addresses a growing need: As more spacecraft using RF communications systems enter the RF spectrum, the increase in RFI events makes a mitigation system increasingly important for maintaining robust space-to-ground communications.
- Real-time performance tailoring: The system operator can selectively change the relative weights of the five communication link parameters in real-time. For example, this makes it possible for an operator to choose to save power at all costs in one instance, or instead optimize throughput. The software will choose a different way to minimize interference in each case.
Applications
- Space-to-ground communications
- Satellite communications
- RF communications
Technology Details
communications
LEW-TOPS-166
LEW-19965-1
"Interference Mitigation Using Cyclic Autocorrelation and Multi-Objective Optimization", Mic Koch & Joseph Downey, 2019.
https://ntrs.nasa.gov/citations/20190027051
