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Toward cognitive vector network analyzers for conducting wireless stimulus-response measurements in open-area environments

University of British Columbia

Vector network analyzers (VNAs) are often used to measure antenna performance, channel response, and shielding effectiveness in open-area environments. In such applications, external interference from other users may sporadically occupy portions of the frequency band of interest and thus compromise the integrity of the measurements. The simple strategies for avoiding such interference that are commonly employed may be ineffective because: 1) clear channels within the band may not be available, 2) it may be difficult to find suitable antennas for use in adjacent clear bands, 3) the other users in the band may be uncooperative, 4) the interference encountered in the band may be intolerable even during off-peak hours or 5) it may not be possible or convenient to move to a different measurement location. Here, we show that the reliability and accuracy of VNA-based wireless measurements performed under such circumstances can be significantly improved by applying cognitive radio concepts where uncooperative wireless systems are cast as primary users and the VNA is cast as the secondary user. For the case of long-burst interference, i.e., scenarios dominated by voice and video transmissions that are much longer than the VNA measurement dwell time, we propose and demonstrate a scheme that uses carrier sensing to: 1) avoid collisions between VNA and primary user transmissions and 2) identify and reject corrupted measurements. For the case of short-burst interference, i.e., scenarios dominated by data packet transmissions that are much shorter than the VNA measurement dwell time, we show that identification and rejection of corrupted measurements is more difficult but can be accomplished by modifying the interference-aware VNA to apply robust estimation to the results. The main limitation of the second scheme is the time required to collect the additional measurement data required. In both cases, re-purposing existing hardware within the VNA and making relatively minor enhancements to the firmware would both simplify implementation and significantly decrease the data collection time. Both schemes represent an important step toward realizing a fully cognitive VNA that is capable of sensing its environment and configuring itself to conduct interference-free wireless measurements as quickly and effectively as possible.

Text

2015-04-30

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/53013

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/