Adrienne Lam with the telemetry processor and Unix computer. Image Credit: URC.

California State University, Los Angeles

Major: Electrical Engineering

Degree Level: Master of Science

Internship Site: NASA Jet Propulsion Laboratory, Pasadena, California

Mentor: Dr. Chau M. Buu

Abstract: The Deep Space Network (DSN) is an international antenna network funded by NASA which provides communications and support for various space missions. A 34-meter beam waveguide antenna is under construction in Canberra, Australia, and requires a new downlink channel and telemetry processors. This project focuses on testing of new telemetry decoders which are implemented on Field-Programmable Gate Array (FPGA) boards. These are also intended to replace legacy telemetry processors in the future to improve ease of maintenance and sustainability. The intended functionality and performance of the decoders must be the same as those which they are replacing. The new decoders are tested using different code rates, coding schemes and modulation schemes standard to the DSN. The testing process utilizes equipment similar to those at DSN ground stations, along with a signal generator to provide equivalent RF signals. The Python scripting language is used to verify the integrity of decoded data and can handle large sets. These tools are used to obtain characteristics of performance such as symbol error rate (SER), bit error rate (BER), and signal-to-noise-ratio (SNR). Throughout the course of the internship, the encoding scheme that was of primary focus was Turbo Code using code rates and modulation schemes seen on the actual DSN. Discrepancies in decoder performance were encountered during testing, with several sources identified. Various parameters were added to the output to isolate different sections of the test setup for identifying those sources of error.

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