In this thesis, we benchmark the Cognitive Radios Test System version 2.0 (CRTSv.2) to analyze its software performance with respect to its internal structure and design choices. With the help of system monitoring and profiling tools, CRTSv.2 is tested to quantitatively evaluate its features and understand its shortcomings. With the help of GNU Radio, a popular, easy-to-use software radios framework, we ascertain that CRTSv.2 has a low memory footprint, fewer dependencies and overall, is a lightweight framework that can potentially be used for real-time signal processing. Several open-source measurement tools such as valgrind, perf, top, etc. are used to evaluate the CPU utilization, memory footprint and to postulate the origins of latencies. Based on our evaluation, we observe that CRTSv.2 shows a CPU utilization of approximately 9% whereas GNU Radio is 59%. CRTSv.2 has lower heap memory consumption of approximately 3MB to GNU Radio's 25MB. This study establishes a methodology to evaluate the performance of two SDR frameworks systematically and quantitatively. / Master of Science / When picking the best person for the job, we rely on the person's performance in past projects of a similar nature. The same can be said for software. Software radios provide the capability to perform signal processing functions in software, making them prime candidates towards solving modern problems such as spectrum scarcity, internet-of-things(IoT) adoption, vehicle-to-vehicle communication etc. In order to operate and configure software radios, software frameworks are provided that let the user make changes to the waveform, perform signal processing and data management. In this thesis, we consider two such frameworks,GNU Radio and CRTSv.2. A software performance evaluation is conducted to assess framework overheads contributing to operation of an orthogonal frequency-division multiplexing (OFDM) digital modulation scheme. This provides a quantitative analysis of a signals-specific use case which can be used by researchers to evaluate the optimal framework for research. This analysis can be generalized for different signal processing capabilities by understanding the total framework overhead removed from signal processing costs.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/97568 |
| Date | 08 April 2020 |
| Creators | Gadgil, Kalyani Surendra |
| Contributors | Electrical and Computer Engineering, Dietrich, Carl B., Saad, Walid, MacKenzie, Allen B., Polys, Nicholas F. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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