Blind Measurement of Receiver System Noise

• Published in: IEEE transactions on microwave theory and techniques 2020-06, Vol.68 (6), p.2435-2453
• Main Authors: Kuester, Daniel G., Wunderlich, Adam, McGillivray, Duncan A., Gu, Dazhen, Puls, Audrey K.
• Format: Article
• Language: English
• Subjects: Communication systems; Computer simulation; Diagnostic systems; Extraterrestrial measurements; Gauges; information processing; Measurement techniques; Microwave measurement; Monte Carlo simulation; Noise; Noise levels; Noise measurement; Parameter estimation; Random errors; Receivers; regression analysis; Signal processing; Signal to noise ratio; system validation; Temperature measurement; Uncertainty; Wireless communication
• ISSN: 0018-9480; 1557-9670
• DOI: 10.1109/TMTT.2020.2986286

Tightly packaged receivers pose a challenge for noise measurements. Their only outputs are often diagnostic or benchmark information-"user data" that result from unknown processing. These include data rate test results, signal-to-noise ratio estimated by the receiver, and so on. Some of these are important gauges of communication viability that may be enshrined in performance and conformance specifications. Engineers can estimate these parameters based on standards and simplified system models, but there are few means to validate against physical measurements. We propose here a set of measurement techniques to complement and support models of system noise. The approach is founded on a semiparametric model of the noise response of a full-stack receiver. We probe this response experimentally by systematically perturbing signals and excess noise levels at the receiver input. The resulting technique is blind to protocol and implementation details. We introduce the design and implementation of some novel test capabilities required for these tests: a precision programmable excess noise source and a highly directive programmable attenuator. We also introduce a regression procedure to estimate system noise (or NF) from the controlled input conditions and summary statistics of the user data output. We also estimate uncertainty in the measurement by combining traditional methods with a Monte Carlo method that propagates random errors through the regression. Case studies demonstrate the measurement with consumer wireless networking and geolocation equipment. These include verification by repeatability testing and cross-comparison against Y -factor measurements.